CN114017461B - Vehicle-mounted double-layer stretcher, vibration reduction platform of vehicle-mounted double-layer stretcher and control method - Google Patents

Vehicle-mounted double-layer stretcher, vibration reduction platform of vehicle-mounted double-layer stretcher and control method Download PDF

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CN114017461B
CN114017461B CN202111342080.5A CN202111342080A CN114017461B CN 114017461 B CN114017461 B CN 114017461B CN 202111342080 A CN202111342080 A CN 202111342080A CN 114017461 B CN114017461 B CN 114017461B
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vehicle
expected
electric cylinder
stretcher
speed
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CN114017461A (en
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钱瀚欣
胡景晨
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Shanghai New Era Robot Co ltd
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Shanghai New Era Robot Co ltd
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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
    • F16F—SPRINGS; SHOCK-ABSORBERS; MEANS FOR DAMPING VIBRATION
    • F16F15/00—Suppression of vibrations in systems; Means or arrangements for avoiding or reducing out-of-balance forces, e.g. due to motion
    • F16F15/002—Suppression of vibrations in systems; Means or arrangements for avoiding or reducing out-of-balance forces, e.g. due to motion characterised by the control method or circuitry
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61G—TRANSPORT, PERSONAL CONVEYANCES, OR ACCOMMODATION SPECIALLY ADAPTED FOR PATIENTS OR DISABLED PERSONS; OPERATING TABLES OR CHAIRS; CHAIRS FOR DENTISTRY; FUNERAL DEVICES
    • A61G1/00—Stretchers
    • A61G1/04—Parts, details or accessories, e.g. head-, foot-, or like rests specially adapted for stretchers
    • A61G1/052—Struts, spars or legs
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61G—TRANSPORT, PERSONAL CONVEYANCES, OR ACCOMMODATION SPECIALLY ADAPTED FOR PATIENTS OR DISABLED PERSONS; OPERATING TABLES OR CHAIRS; CHAIRS FOR DENTISTRY; FUNERAL DEVICES
    • A61G1/00—Stretchers
    • A61G1/06—Supports for stretchers, e.g. to be placed in or on vehicles
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
    • F16F—SPRINGS; SHOCK-ABSORBERS; MEANS FOR DAMPING VIBRATION
    • F16F15/00—Suppression of vibrations in systems; Means or arrangements for avoiding or reducing out-of-balance forces, e.g. due to motion
    • F16F15/02—Suppression of vibrations of non-rotating, e.g. reciprocating systems; Suppression of vibrations of rotating systems by use of members not moving with the rotating systems
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
    • F16F—SPRINGS; SHOCK-ABSORBERS; MEANS FOR DAMPING VIBRATION
    • F16F15/00—Suppression of vibrations in systems; Means or arrangements for avoiding or reducing out-of-balance forces, e.g. due to motion
    • F16F15/02—Suppression of vibrations of non-rotating, e.g. reciprocating systems; Suppression of vibrations of rotating systems by use of members not moving with the rotating systems
    • F16F15/04—Suppression of vibrations of non-rotating, e.g. reciprocating systems; Suppression of vibrations of rotating systems by use of members not moving with the rotating systems using elastic means
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61G—TRANSPORT, PERSONAL CONVEYANCES, OR ACCOMMODATION SPECIALLY ADAPTED FOR PATIENTS OR DISABLED PERSONS; OPERATING TABLES OR CHAIRS; CHAIRS FOR DENTISTRY; FUNERAL DEVICES
    • A61G2203/00—General characteristics of devices
    • A61G2203/70—General characteristics of devices with special adaptations, e.g. for safety or comfort
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
    • F16F—SPRINGS; SHOCK-ABSORBERS; MEANS FOR DAMPING VIBRATION
    • F16F2230/00—Purpose; Design features
    • F16F2230/0011—Balancing, e.g. counterbalancing to produce static balance
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
    • F16F—SPRINGS; SHOCK-ABSORBERS; MEANS FOR DAMPING VIBRATION
    • F16F2230/00—Purpose; Design features
    • F16F2230/0047—Measuring, indicating
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
    • F16F—SPRINGS; SHOCK-ABSORBERS; MEANS FOR DAMPING VIBRATION
    • F16F2230/00—Purpose; Design features
    • F16F2230/08—Sensor arrangement
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
    • F16F—SPRINGS; SHOCK-ABSORBERS; MEANS FOR DAMPING VIBRATION
    • F16F2230/00—Purpose; Design features
    • F16F2230/18—Control arrangements
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation

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  • Engineering & Computer Science (AREA)
  • General Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Acoustics & Sound (AREA)
  • Aviation & Aerospace Engineering (AREA)
  • Mechanical Engineering (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Animal Behavior & Ethology (AREA)
  • General Health & Medical Sciences (AREA)
  • Public Health (AREA)
  • Veterinary Medicine (AREA)
  • Feedback Control In General (AREA)

Abstract

The invention provides a vehicle-mounted double-layer stretcher, a vibration reduction platform of the vehicle-mounted double-layer stretcher and a control method, wherein the vibration reduction platform comprises the following steps: acquiring a three-dimensional angular velocity value and a three-dimensional acceleration value of the vehicle-mounted double-layer stretcher to calculate pose information and velocity information of the vehicle-mounted double-layer stretcher; according to the robot kinematics model, pose information and speed information, calculating expected telescopic speeds of all electric cylinders of the vehicle-mounted double-layer stretcher, and expected lengths of connection points of all electric cylinders with a base of the vehicle-mounted double-layer stretcher and a lower-layer stretcher; based on a robot dynamics model, expected telescopic speed, expected length and real-time acceleration corresponding to each connecting shaft, obtaining expected positions, expected speeds and real-time moment feedforward of each connecting shaft; the BP neural network is utilized to input the expected position, expected speed and real-time moment feedforward and load moment of each connecting shaft so as to update the control rate parameter; and performing self-balancing on the vibration reduction platform based on the control rate parameter. And compensating the vibration angle and the speed based on the kinematics and the dynamics of the robot.

Description

Vehicle-mounted double-layer stretcher, vibration reduction platform of vehicle-mounted double-layer stretcher and control method
Technical Field
The invention relates to the technical field of rescue equipment, in particular to a vehicle-mounted double-layer stretcher, a vibration reduction platform of the vehicle-mounted double-layer stretcher and a control method.
Background
In the existing ambulance or military rescue vehicle, the stretcher used has no vibration damping effect or only slight vibration damping effect, and the stretcher has the buffering performance and also belongs to passive vibration damping. As shown in fig. 1, such a stretcher is not good in vibration damping or buffering performance, and has a certain vibration damping effect on vibration with a high frequency, but has little vibration damping effect on vibration with a low vibration frequency and a large fluctuation amplitude, which seriously affects the treatment of patients or wounded persons. For example, in a battlefield or the like, the ground is uneven, and if vibration reduction of the stretcher in the rescue vehicle is not performed, life safety of a wounded person may be affected.
In response to this phenomenon and the need, there is an urgent need for a vibration reducing cot that is capable of achieving self-balancing.
Disclosure of Invention
The invention aims to provide a vehicle-mounted double-layer stretcher, a vibration reduction platform of the vehicle-mounted double-layer stretcher and a control method, and solves the problems.
The technical scheme provided by the invention is as follows:
the invention provides a control method of a vibration reduction platform of a vehicle-mounted double-layer stretcher, which comprises the following steps:
Acquiring a three-dimensional angular velocity value and a three-dimensional acceleration value of a vehicle-mounted double-layer stretcher to calculate pose information and velocity information of the vehicle-mounted double-layer stretcher;
according to a robot kinematic model, the pose information and the speed information, calculating expected telescopic speeds of all electric cylinders of the vehicle-mounted double-layer stretcher and expected lengths of connecting shafts between the electric cylinders and connecting points of a base and a lower-layer stretcher of the vehicle-mounted double-layer stretcher;
based on a robot dynamics model, the expected telescopic speed, the expected length and the real-time acceleration corresponding to each connecting shaft, obtaining expected positions, expected speeds and real-time moment feedforward of each connecting shaft;
inputting expected positions, expected speeds and real-time moment feedforward of all connecting shafts and load moment of all connecting shafts under different situations by using a BP neural network to obtain target real-time moment feedforward so as to update control rate parameters;
and based on the control rate parameter, enabling the vibration reduction platform to perform self-balancing.
Further preferably, the acquiring the three-dimensional angular velocity value and the three-dimensional acceleration value of the vehicle-mounted double-layer stretcher to calculate pose information and velocity information of the vehicle-mounted double-layer stretcher includes:
Receiving a three-dimensional angular velocity value and a three-dimensional acceleration value of the vehicle-mounted double-layer stretcher transmitted by an IMU inertial measurement unit of a base of the vehicle-mounted double-layer stretcher;
and obtaining pose information and speed information according to the rotation matrix of the satellite coordinate system and the inertial coordinate system of the base of the vehicle-mounted double-layer stretcher, the three-dimensional angular speed value and the three-dimensional acceleration value.
Further preferably, the obtaining the pose information and the speed information according to the rotation matrix of the satellite coordinate system and the inertial coordinate system of the base of the vehicle-mounted double-layer stretcher, the three-dimensional angular speed value and the three-dimensional acceleration value includes:
high-pass filtering is carried out on the three-dimensional angular velocity value, and low-pass filtering is carried out on the three-dimensional acceleration value;
the initial pose information is obtained by inputting the three-dimensional angular velocity value after high-pass filtering and the three-dimensional acceleration value after low-pass filtering into the following formulas:
α m0 ＝arctan(a my /a mz ),γ m0 ＝-arcsin(a mx /g)；
α mA ＝α m0 +ω α t,γ mA ＝γ m0 +ω γ t；
performing low-pass filtering, speed integration and position integration on the three-dimensional acceleration value to obtain the position information;
wherein ,αm0 Is the initial angle in the alpha direction; alpha mx The acceleration in the x-axis direction is obtained after low-pass filtering; alpha my The acceleration in the y-axis direction obtained after the low-pass filtering; alpha mz The acceleration in the z-axis direction is obtained after low-pass filtering; gamma ray m0 Is the initial angle in the gamma direction; g is gravity acceleration; omega α Is the angular velocity in the alpha direction after high-pass filtering; omega γ Is the angular velocity in the gamma direction after high pass filtering; t is the current moment; alpha mA Alpha is alpha A A target angle in the direction; gamma ray mA Is gamma A Target angle in direction.
Further preferably, the calculating the expected telescopic speed of each electric cylinder of the vehicle-mounted double-layer stretcher and the expected length of the connecting shaft between the electric cylinder and the base and lower-layer stretcher connecting point of the vehicle-mounted double-layer stretcher according to the robot kinematic model, the pose information and the speed information includes:
obtaining a according to the configuration of the vehicle-mounted double-layer stretcher 1 、a 3 、b 1 、b 3 、c；
Obtaining the expected length of a connecting shaft between the telescopic speed and the connecting points of the electric cylinder, the base of the vehicle-mounted double-layer stretcher and the lower-layer stretcher by using a robot kinematics model:
wherein ,ltar1 ,l tar2 ,l tar3 ,l tar4 The expected telescopic speeds of the electric cylinders of the vehicle-mounted double-layer stretcher are respectively;the expected lengths of the connecting shafts between the electric cylinder and the base and the connecting points of the lower stretcher of the vehicle-mounted double-layer stretcher are respectively set.
Further preferably, before the desired position, the desired speed and the real-time moment feedforward of each connecting shaft are obtained based on the robot dynamics model, the desired telescopic speed, the desired length and the real-time acceleration corresponding to each connecting shaft, the method comprises:
And obtaining a robot dynamics model by a virtual power method:
wherein ,Mt Representing an inertia matrix under task space, F Gt Representing gravity terms under task space, F St Representing the elastic force and damping force items under the task space, F Ct Representing the Coriolis force and centrifugal force terms in the task space, u t Representing control force under task space, u t ＝R 0 [τ 1 τ 2 τ 3 τ 4 ] T ；R 0 Transformation matrix, τ, representing task space and joint space i The forces of the four cylinders are indicated respectively.
Further preferably, the obtaining the feedforward of the desired position, the desired speed and the real-time moment of each connecting shaft based on the robot dynamics model, the desired telescopic speed, the desired length and the real-time acceleration corresponding to each connecting shaft includes:
differentiating the real-time speed and the real-time position to obtain the expected position and the expected speed;
inputting the expected position and the expected speed into the robot dynamics model to obtain real-time output of the electric cylinder;
according to the transmission relation between the connecting shaft and the motor, obtaining real-time moment feedforward of the connecting shaft:
wherein ,fi Is real-time moment feedforward;the real-time output of the electric cylinder is realized; η (eta) i Is the transmission ratio of the motor.
Further preferably, the step of inputting the expected position, the expected speed, the real-time torque feedforward of each connecting shaft and the load torque of each connecting shaft under different situations by using the BP neural network to obtain the target real-time torque feedforward to update the control rate parameter includes:
Correcting an inertia array and a gravity term of the robot dynamics model based on the expected positions, expected speeds and real-time moment feedforward of each connecting shaft and the load moment of each connecting shaft under different situations through the BP neural network so as to acquire the target real-time moment feedforward;
wherein, the robot dynamics model is:
wherein ,τi The output of the electric cylinder is that J is a jacobian matrix of the robot; k (K) p ，K d ，K e Respectively representing a rigidity coefficient matrix, a damping coefficient matrix and a contact force matrix of the robot.
Further preferably, the step of inputting the expected position, the expected speed, the real-time torque feedforward of each connecting shaft and the load torque of each connecting shaft under different situations by using the BP neural network to obtain the target real-time torque feedforward to update the control rate parameter includes:
based on the expected position, expected speed and real-time moment feedforward of each connecting shaft and the load moment of each connecting shaft under different situations, calculating and obtaining the position error, speed error and moment error of each electric cylinder of the vehicle-mounted double-layer stretcher at different moments and the load moment of each connecting shaft under different situations;
taking the position error, the speed error and the moment error of each electric cylinder of the vehicle-mounted double-layer stretcher at different moments and the load moment of each connecting shaft under different conditions as input signals of the BP neural network
Wherein the input is: as weight term, b i Is a bias term;
neurons of a hidden layer of the BP neural network adopt a nonlinear excitation function sigmod to excite sigma j Obtaining hidden layer output sigma j ', i.e. h i ＝[h 1i h 2i h 3i h 4i ] T ，
The error between the BP neural network output and the ideal output is e (t), and the error performance function is expressed as
Setting the precision l according to the gradient hill-drop method r =0.03, update
And repeating the loop until e (t) is smaller than the set value, and training to obtain the control rate parameter.
A vibration reduction platform for an on-board double-layer stretcher, comprising:
the acquisition module is used for acquiring a three-dimensional angular velocity value and a three-dimensional acceleration value of the vehicle-mounted double-layer stretcher so as to calculate pose information and velocity information of the vehicle-mounted double-layer stretcher;
the calculation module is used for calculating the expected telescopic speed of each electric cylinder of the vehicle-mounted double-layer stretcher and the expected length of a connecting shaft between the electric cylinder and the connecting point of the base and the lower-layer stretcher of the vehicle-mounted double-layer stretcher according to the robot kinematic model, the pose information and the speed information;
the dynamics module is used for obtaining expected positions, expected speeds and real-time moment feed-forward of the connecting shafts based on a robot dynamics model, the expected telescopic speed, the expected length and the real-time acceleration corresponding to the connecting shafts;
The self-adaptive control module is used for inputting expected positions, expected speeds and real-time moment feedforward of each connecting shaft and load moment of each connecting shaft under different situations by using the BP neural network, and obtaining target real-time moment feedforward so as to update control rate parameters;
and the balancing module is used for self-balancing the vibration reduction platform based on the control rate parameter.
A vehicular double-layer stretcher comprising:
the vehicle, the bed body, the first servo electric cylinder, the second servo electric cylinder, the third servo electric cylinder, the fourth servo electric cylinder and the base of the vehicle-mounted double-layer stretcher; the vibration reduction platform is arranged on the base;
the bed body comprises a lower layer stretcher and an upper layer stretcher; the base is installed in a box body of the vehicle;
the first servo electric cylinder and the second servo electric cylinder are arranged on the base through a Hooke's joint; the third servo electric cylinder and the fourth servo electric cylinder are mounted on the base through a revolute pair joint;
the first servo electric cylinder, the second servo electric cylinder, the third servo electric cylinder and the fourth servo electric cylinder are respectively connected with the bed body through a spherical hinge.
The vehicle-mounted double-layer stretcher, the vibration reduction platform of the vehicle-mounted double-layer stretcher and the control method provided by the invention have the following beneficial effects:
1) The invention adopts the robot kinematics and dynamics to compensate the vibration angle and speed, the effect is better than the acceleration compensation and passive vibration reduction used in the general active vibration reduction, and the structure can achieve good vibration reduction effect by applying the invention, thereby greatly reducing the injury to wounded caused by jolt.
2) The invention uses the mode of speed and position control assisted by feedforward force compensation to accurately control the posture of the bed, greatly improves the vibration damping performance of the double-layer stretcher, can be suitable for various medical ambulances, and obviously improves the vibration damping performance compared with the traditional passive vibration damping mode.
3) The scheme is also suitable for various scenes on the basis of accurate control, and can adapt (or lie or sit) to different use modes of the self-balancing double-layer stretcher structure. The self-adaptive control can also automatically identify and adapt to different loads of wounded persons on the stretcher (two wounded persons can lie or one wounded person can lie, one to four wounded persons sit), and parameters can be automatically adjusted to maintain the bed balance under different scenes.
Drawings
The above features, technical features, advantages and implementation manners of a vehicle-mounted double-layer stretcher, a vibration reduction platform of the vehicle-mounted double-layer stretcher and a control method will be further described in a clear and understandable manner with reference to the accompanying drawings.
FIG. 1 is a schematic diagram of a double layer cot of the present invention;
FIG. 2 is a schematic view of a structural solution of the double-layered cot of the present invention with respect to orientation;
FIG. 3 is a schematic illustration of the structural aspects of the double layer cot of the present invention with respect to components;
FIG. 4 is a schematic diagram of a control framework of the present invention;
FIG. 5 is a control schematic of the BP neural network of the present invention;
FIG. 6 is a network block diagram of a BP neural network in the present invention;
FIG. 7 is a schematic diagram of a method of controlling a vibration reduction platform of a vehicular dual-layer stretcher according to the present invention;
FIG. 8 is a schematic view of a vibration reduction platform of a vehicular dual-layer stretcher of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system configurations, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. However, it will be apparent to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It should be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
For the sake of simplicity of the drawing, the parts relevant to the present invention are shown only schematically in the figures, which do not represent the actual structure thereof as a product. Additionally, in order to simplify the drawing for ease of understanding, components having the same structure or function in some of the drawings are shown schematically with only one of them, or only one of them is labeled. Herein, "a" means not only "only this one" but also "more than one" case.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
In addition, in the description of the present application, the terms "first," "second," and the like are used merely to distinguish between descriptions and are not to be construed as indicating or implying relative importance.
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following description will explain the specific embodiments of the present invention with reference to the accompanying drawings. It is evident that the drawings in the following description are only examples of the invention, from which other drawings and other embodiments can be obtained by a person skilled in the art without inventive effort.
The invention provides a control method of a vibration reduction platform of a vehicle-mounted double-layer stretcher, which comprises the following steps:
s100, acquiring a three-dimensional angular velocity value and a three-dimensional acceleration value of the vehicle-mounted double-layer stretcher to calculate pose information and velocity information of the vehicle-mounted double-layer stretcher.
Specifically, the inner base of the vehicle-mounted double-layer stretcher is provided with an IMU inertial measurement unit, so that real-time acceleration a of the vehicle in three directions of the horizontal direction of the front and back, the left and right and the vertical direction of the upper and lower can be provided mx ,a my ,a mz Angular velocity omega from three directions of rotation mα ，ω mβ ，ω mγ . In addition, if the vehicle in which the stretcher is located can provide these speed position information, the stretcher may also perform calculation using the information provided by the vehicle.
Illustratively, according to the three-dimensional angular velocity value and the acceleration value of the fixed platform acquired by the sensor/inertial conduction unit, high-pass filtering and low-pass filtering are used to calculate accurate pose information and accurate velocity information of the platform.
S200, calculating expected telescopic speeds of all electric cylinders of the vehicle-mounted double-layer stretcher and expected lengths of connecting shafts between the electric cylinders and connecting points of the base and the lower-layer stretcher of the vehicle-mounted double-layer stretcher according to a robot kinematic model, the pose information and the speed information.
Specifically, the servo electric cylinder (200) shown in FIG. 2 can feed back the travel and speed of the electric cylinder in real time, so that the corresponding electric cylinder can be obtainedDistance l between the connection point of the base and the connection point of the electric cylinder and the lower stretcher 1 ,l 2 ,l 3 ,l 4 And speed of
The expected length of the connecting shaft between the expected telescopic speed of each electric cylinder and the corresponding connecting point in the joint space is obtained according to the robot kinematic model of the vehicle-mounted double-layer stretcher vibration reduction platform, the obtained pose information and the obtained speed information.
S300, based on a robot dynamics model, the expected telescopic speed, the expected length and the real-time acceleration corresponding to each connecting shaft, obtaining expected positions, expected speeds and real-time moment feedforward of each connecting shaft.
Specifically, the real-time acceleration of the connecting mechanism is obtained according to the real-time actual position and speed fed back by each shaft of the connecting mechanism.
According to the parallel robot configuration of the vehicle-mounted double-layer stretcher vibration reduction platform, a kinematic model and a dynamic model of the vehicle-mounted double-layer stretcher vibration reduction platform are calculated, and the desired telescopic speed, the desired length and the real-time acceleration corresponding to each connecting shaft are respectively substituted into the parallel robot model to obtain the length of the shaft of each electric cylinder in the joint space, so that the length of the shaft is the desired length, the desired position and the desired speed and force feedforward value output by the electric cylinder.
S400, inputting expected positions, expected speeds and real-time moment feedforward of each connecting shaft and load moment of each connecting shaft under different situations by using a BP neural network, and obtaining target real-time moment feedforward to update control rate parameters.
Specifically, by using the BP neural network, the expected position, the expected speed, the moment feedforward and the load moment under different conditions of each electric cylinder are input, the control rate parameter about the output moment is obtained, updated in real time, and a new moment feedforward value is obtained, as shown in fig. 5.
S500 is based on the control rate parameter to make the vibration reduction platform self-balancing.
In particular, the control scheme of the invention mainly depends onActive vibration reduction for maintaining vehicle-mounted double-layer stretcher at alpha A ，γ A Balance in degree of freedom, as shown in FIG. 2, the on-board double-layer stretcher is maintained in y by means of passive vibration reduction (spring mechanism) A Balance in degrees of freedom.
The new moment feedforward is calculated by using the calculated new moment feedforward value, substituted into the control frame as shown in fig. 4, updated in real time, and PID parameters in the control frame are adjusted, so that the self-balancing function of the vehicle-mounted double-layer stretcher vibration reduction platform is realized, and the algorithm is completed.
The PID parameter is a control rate parameter, and the PID controller (proportional-integral-derivative controller) is a common feedback loop component in industrial control application and consists of a proportional unit P, an integral unit I and a derivative unit D. The key to this theory and application is how to better correct the system after making the correct measurements and comparisons. PID (proportional), integral, derivative) controllers have been the earliest practical controllers for almost a hundred years and are still the most widely used industrial controllers. The PID controller is simple and easy to understand, and the accurate system model and other preconditions are not needed in the use, so that the PID controller is the most widely applied controller.
In this embodiment, this proposal is applicable to the self-balancing double-layer stretcher structure shown in the figure, and the application of this proposal can make this structure reach good damping effect, reduces the injury that jolts caused to wounded by a wide margin. Meanwhile, the self-adaptive control can also automatically identify and adapt to different loads of wounded persons on the stretcher (two wounded persons can lie or one wounded person can lie, one to four wounded persons sit), and parameters are automatically adjusted to maintain the bed balance under different scenes.
The vibration damping device has the advantages that the mode of feedforward force compensation is adopted for speed and position control, the position and posture of the bed are accurately controlled, the vibration damping performance of the double-layer stretcher is greatly improved, the vibration damping device can be suitable for various medical ambulances, and compared with the traditional passive vibration damping mode, the vibration damping performance is obviously improved.
Meanwhile, the double-layer stretcher structure is proposed, compared with the traditional single-layer stretcher structure, the double-layer stretcher is more complex in structure, but one double-layer stretcher can be used by more wounded persons, so that the cost is reduced from a large-scale perspective, and the efficiency is improved.
Example two
Based on the above embodiments, the same parts as those of the above embodiments are not repeated in this embodiment, and this embodiment provides a control method for a vibration reduction platform of a vehicle-mounted double-layer stretcher, where:
The obtaining the three-dimensional angular velocity value and the three-dimensional acceleration value of the vehicle-mounted double-layer stretcher to calculate pose information and velocity information of the vehicle-mounted double-layer stretcher comprises the following steps:
and receiving the three-dimensional angular velocity value and the three-dimensional acceleration value of the vehicle-mounted double-layer stretcher transmitted by the IMU inertial measurement unit of the base of the vehicle-mounted double-layer stretcher.
And obtaining pose information and speed information according to the rotation matrix of the satellite coordinate system and the inertial coordinate system of the base of the vehicle-mounted double-layer stretcher, the three-dimensional angular speed value and the three-dimensional acceleration value.
The obtaining the pose information and the speed information according to the rotation matrix of the satellite coordinate system and the inertial coordinate system of the base of the vehicle-mounted double-layer stretcher, the three-dimensional angular speed value and the three-dimensional acceleration value comprises the following steps:
high-pass filtering is carried out on the three-dimensional angular velocity value, and low-pass filtering is carried out on the three-dimensional acceleration value;
the initial pose information is obtained by inputting the three-dimensional angular velocity value after high-pass filtering and the three-dimensional acceleration value after low-pass filtering into the following formulas:
α m0 ＝arctan(a my /a mz ),γ m0 ＝-arcsin(a mx /g)；
α mA ＝α m0 +ω α t,γ mA ＝γ m0 +ω γ t；
performing low-pass filtering, speed integration and position integration on the three-dimensional acceleration value to obtain the position information;
wherein ,αm0 Is the initial angle in the alpha direction; alpha mx The acceleration in the x-axis direction is obtained after low-pass filtering; alpha my The acceleration in the y-axis direction obtained after the low-pass filtering; alpha mz The acceleration in the z-axis direction is obtained after low-pass filtering; gamma ray m0 Is the initial angle in the gamma direction; g is gravity acceleration; omega α Is the angular velocity in the alpha direction after high-pass filtering; omega γ Is the angular velocity in the gamma direction after high pass filtering; t is the current moment; alpha mA Alpha is alpha A A target angle in the direction; gamma ray mA Is gamma A Target angle in direction.
Specifically, according to the rotation matrix of the satellite coordinate system and the inertial coordinate system of the base, we can obtain the initial values of alpha and gamma, alpha m0 ＝arctan(a my /a mz ),γ m0 ＝-arcsin(a mx /g). By integrating the time, we can obtain the real-time angle alpha in the alpha and gamma directions of the base mA ＝α m0 +ω α t,γ mA ＝γ m0 +ω γ t。
Because the sensor obtains a value having a drift error, and the vehicle travels on a bumpy road surface, the acceleration value is changed more frequently. The acceleration measured by the sensor is processed by low-pass filtering, the diagonal velocity is processed by high-pass filtering, and then the acceleration is substituted into the formula, so that more accurate attitude information alpha can be obtained A ，γ A The method comprises the steps of carrying out a first treatment on the surface of the And utilizes the real-time acceleration a my Through low-pass filtering processing, speed integration and position integration are carried out, and accurate position information y is obtained A 。
According to the robot kinematics model, the pose information and the speed information, calculating the expected telescopic speed of each electric cylinder of the vehicle-mounted double-layer stretcher and the expected length of a connecting shaft between the electric cylinder and a base and lower-layer stretcher connecting point of the vehicle-mounted double-layer stretcher, wherein the method comprises the following steps:
obtaining a according to the configuration of the vehicle-mounted double-layer stretcher 1 、a 3 、b 1 、b 3 、c。
Obtaining the expected length of a connecting shaft between the telescopic speed and the connecting points of the electric cylinder, the base of the vehicle-mounted double-layer stretcher and the lower-layer stretcher by using a robot kinematics model:
wherein ,ltar1 ,l tar2 ,l tar3 ,l tar4 The expected telescopic speeds of the electric cylinders of the vehicle-mounted double-layer stretcher are respectively;the expected lengths of the connecting shafts between the electric cylinder and the base and the connecting points of the lower stretcher of the vehicle-mounted double-layer stretcher are respectively set.
Illustratively, depending on the configuration of the double layer stretcher (as shown in FIG. 1), we can obtain a 1 、a 3 、b 1 、b 3 The values of c, to compensate for pose, using robot kinematics we have:
1、
and derives the time on the two sides, we can obtain
2、
Wherein, the K value is a constant matrix obtained by deriving the formula in 1, and specifically:
thus, we can obtain the desired electric cylinder expansion speed l tar1 ,l tar2 ,l tar3 ,l tar4 And a desired length between upper and lower corresponding connection points
Wherein, as shown in fig. 1, A1-A4 respectively represent the hinge point positions of the four cylinders and the lower platform; B1-B4 respectively represent the hinge point positions of the four cylinders and the upper platform.
a1 represents half of the distance between A1 and A2; a3 represents half of the distance between A3 and A4; b1 represents half of the distance between B1 and B2; b3 represents half of the distance between B3 and B4. c represents the distance between the third (fourth) root cylinder and the first (second) root cylinder (parallel to each other) as seen in the planar direction of xByB/xAyA.
Before the expected position, the expected speed and the real-time moment feedforward of each connecting shaft are obtained based on the robot dynamics model, the expected telescopic speed, the expected length and the real-time acceleration corresponding to each connecting shaft, the method comprises the following steps:
and obtaining a robot dynamics model by a virtual power method:
wherein ,Mt Representing an inertia matrix under task space, F Gt Representing gravity terms under task space, F St Representing the elastic force and damping force items under the task space, F Ct Representing the Coriolis force and centrifugal force terms in the task space, u t Representing control force under task space, u t ＝R 0 [τ 1 τ 2 τ 3 τ 4 ] T ；R 0 Transformation matrix, τ, representing task space and joint space i The forces of the four cylinders are indicated respectively.
Wherein, the kinetic equation has u t ，u t ＝R 0 [τ 1 τ 2 τ 3 τ 4 ] T 。τ i Representation [ tau ] 1 τ 2 τ 3 τ 4 ]Representing the calculated four cylinder forces, here τ i And τ below i τ in the kinetic equation i The values are consistent.
The step of obtaining the expected position, the expected speed and the real-time moment feedforward of each connecting shaft based on the robot dynamics model, the expected telescopic speed, the expected length and the real-time acceleration corresponding to each connecting shaft comprises the following steps:
and differentiating the real-time speed and the real-time position to obtain the expected position and the expected speed. And inputting the expected position and the expected speed into the robot dynamics model to obtain the real-time output of the electric cylinder. According to the transmission relation between the connecting shaft and the motor, obtaining real-time moment feedforward of the connecting shaft:
wherein ,fi Is real-time moment feedforward;the real-time output of the electric cylinder is realized; η (eta) i Is the transmission ratio of the motor.
For example, each axis in the electric cylinder may feed back actual speed and position information in real time. The actual acceleration, speed and position information of each shaft can be obtained through differential processing of the feedback speed information, and then the real-time output of each shaft is calculated by substituting the dynamic model According to the transmission relation between the shaft and the motor, eta i For the transmission ratio of the motor, we can get the moment feed-forward on each shaft +.>
The method adds the expected speed and the expected position into the speed ring, superimposes the calculated feedforward value of the torque output and the control output of the given value of the controller in the current ring, transmits the superimposed value to the motors of all shafts, refreshes the torque feedforward value in real time, and accordingly performs torque feedforward compensation, and a control frame is shown in fig. 4.
The method for obtaining the target real-time moment feedforward to update the control rate parameter by using the BP neural network to input the expected position, the expected speed and the real-time moment feedforward of each connecting shaft and the load moment of each connecting shaft under different situations comprises the following steps:
and correcting an inertia array and a gravity term of the robot dynamics model based on the expected positions, expected speeds and real-time moment feedforward of each connecting shaft and the load moment of each connecting shaft under different situations through the BP neural network so as to acquire the target real-time moment feedforward.
Wherein, the robot dynamics model is:
wherein ,τi The output of the electric cylinder is that J is a jacobian matrix of the robot; k (K) p ，K d ，K e Respectively representing a rigidity coefficient matrix, a damping coefficient matrix and a contact force matrix of the robot.
Specifically, because the use scene of the double-layer stretcher has multiple possibilities, under the condition of loading four light wounded persons and one heavy wounded person and loading two heavy wounded persons, the inertia matrix in the kinetic equation is inconsistent with the gravity term under the task space, so that the feedforward force compensated under each condition is inconsistent.
Thus, we use adaptive control to correct the inertia matrix and gravity terms in the kinetic equation to make a double layer stretcher adaptable to a variety of situations.
In order to identify the load situation in the self-adaptive control, we can read the current in the electric cylinder in real time to calculate the real-time output of the electric cylinder, and set the actual output of the electric cylinder as tau i ' the robot dynamics equation can be expressed as:
wherein τi The control rate is calculated to obtain the output of the electric cylinder, J represents the Jacobian matrix of the robot, K p ，K d ，K e Respectively representing a rigidity coefficient matrix, a damping coefficient matrix and a contact force matrix of the robot.
In this embodiment, the adaptive strategy control module trains the control rate by using a BP neural network adaptive control algorithm, calculates the gravity items under the corresponding inertia matrix and task space according to the position errors, speed errors, moment errors and load moments of the robot at different moments, and transmits the obtained controller parameters to the control rate.
The method for obtaining the target real-time moment feedforward to update the control rate parameter by using the BP neural network to input the expected position, the expected speed and the real-time moment feedforward of each connecting shaft and the load moment of each connecting shaft under different situations comprises the following steps:
based on the expected position, expected speed and real-time moment feedforward of each connecting shaft and the load moment of each connecting shaft under different situations, calculating and obtaining the position error, speed error and moment error of each electric cylinder of the vehicle-mounted double-layer stretcher at different moments and the load moment of each connecting shaft under different situations.
Taking the position error, the speed error and the moment error of each electric cylinder of the vehicle-mounted double-layer stretcher at different moments and the load moment of each connecting shaft under different conditions as input signals of the BP neural network/>
Wherein the input is: as weight term, b i Is a bias term;
neurons of a hidden layer of the BP neural network adopt a nonlinear excitation function sigmod to excite sigma j Obtaining hidden layer output sigma' j I.e. h i ＝[h 1i h 2i h 3i h 4i ] T ，
h 1i 、h 2i 、h 3i 、h 4i Respectively representing the position error, speed error, moment error and load moment passing weight item w of each connecting shaft of each electric cylinder of the double-layer stretcher at different moments i And bias term b i The processed result is used as the input of the BP neural network hidden layer nonlinear excitation function sigmod, and the obtained four outputs are used as the output items of the BP neural network hidden layer and are combined to be h i I represents the number of each electric cylinder, h of all electric cylinders i Together referred to as sigma' j J represents the iteration number of the neural network;
u1, u2, u3, u4 represent the input u of the BP neural network i The method respectively represents the position error, the speed error, the moment error and the load moment passing weight item w of each connecting shaft under different situations of each electric cylinder of the double-layer stretcher at different moments i And bias term b i A result obtained after the processing is used as an input item of the BP neural network; u (u) i Representing the input of the ith electric cylinder, all u i Together referred to as sigma i As an input to the neural network as a whole, j represents the number of iterations of the neural network.
The error between the BP neural network output and the ideal output is e (t), and the error performance function is expressed as
Setting the precision l according to the gradient hill-drop method r =0.03, update
And repeating the loop until e (t) is smaller than the set value, and training to obtain the control rate parameter.
Specifically, the position error, the speed error and the moment error of each electric cylinder at different moments and the load moment on each shaft in the joint space under different situations are used as input signals of the BP neural network Input is +.> As weight term, b i Is a bias term.
Neurons of the hidden layer of a BP neural network excite sigma using a nonlinear excitation function sigmod j Obtaining hidden layer output sigma j ', i.e. h i ＝[h 1i h 2i h 3i h 4i ] T ，The error between the BP neural network output and the ideal output is e (t), and the error performance function is expressed as +.>Then setting the precision l according to the gradient hill-down method r =0.03, update-> wherein ,w′i B 'for updated weight term' i Is the updated bias term; h's' i Is the output of the updated hidden layer.
And repeating the cycle until e (t) is smaller than the set value of 0.01, and performing iteration to obtain the desired control rate. A network block diagram of the BP neural network is shown in fig. 6.
The self-balancing of the active vibration reduction platform based on the control rate parameter comprises the following steps:
and replacing the control rate parameter corresponding to the real-time moment feedforward with the control rate parameter so that the active vibration reduction platform performs self-balancing control to maintain the balance of the vehicle-mounted double-layer stretcher.
The control rate obtained by training (shown in fig. 5 and 6) is reused in the control frame of fig. 4 to replace kp3 in fig. 4, so that the double-layer stretcher structure can be maintained at alpha A ，γ A Balance in degrees of freedom.
In the embodiment, the vibration angle and the vibration speed are compensated based on the kinematics and the dynamics of the robot, and the effect is better than that of acceleration compensation and passive vibration reduction used in general active vibration reduction;
The scheme is also suitable for various scenes on the basis of accurate control, and can adapt (or lie or sit) to different using modes of the self-balancing double-layer stretcher structure shown in the figure.
Example III
Based on the above embodiment, the same parts as those of the above embodiment are not repeated in this embodiment, and this embodiment provides a vibration reduction platform of a vehicle-mounted double-layer stretcher, as shown in fig. 8, including:
the acquisition module 100 is used for acquiring a three-dimensional angular velocity value and a three-dimensional acceleration value of the vehicle-mounted double-layer stretcher so as to calculate pose information and velocity information of the vehicle-mounted double-layer stretcher.
The calculation module 200 is configured to calculate, according to a robot kinematic model, the pose information and the speed information, an expected expansion speed of each electric cylinder of the vehicle-mounted double-layer stretcher, and an expected length of a connection shaft between each electric cylinder and a connection point of a base and a lower-layer stretcher of the vehicle-mounted double-layer stretcher.
The dynamics module 300 is configured to obtain a desired position, a desired speed, and a real-time torque feedforward of each connection shaft based on the robot dynamics model, the desired telescopic speed, the desired length, and the real-time acceleration corresponding to each connection shaft.
The adaptive control module 400 is configured to input, using a BP neural network, a desired position, a desired speed, a real-time torque feedforward of each connection shaft, and load torques of each connection shaft under different situations, and obtain a target real-time torque feedforward to update the control rate parameter.
And the balancing module 500 is used for making the vibration reduction platform perform self-balancing based on the control rate parameter.
Based on the above embodiments, the same parts as those of the above embodiments are not repeated in this embodiment, and this embodiment provides a vehicle-mounted double-layer stretcher, as shown in fig. 2 and 3, including:
the vehicle, the bed body, the first servo electric cylinder, the second servo electric cylinder, the third servo electric cylinder, the fourth servo electric cylinder and the base of the vehicle-mounted double-layer stretcher; the vibration reduction platform.
The bed body comprises a lower layer stretcher and an upper layer stretcher; the base is installed in a box body of the vehicle; the first servo electric cylinder and the second servo electric cylinder are arranged on the base through a Hooke's joint; the third servo electric cylinder and the fourth servo electric cylinder are mounted on the base through a revolute pair joint; the first servo electric cylinder, the second servo electric cylinder, the third servo electric cylinder and the fourth servo electric cylinder are respectively connected with the bed body through a spherical hinge.
Illustratively, a control scheme for a double layer cot that achieves self-balancing through master control. As shown in fig. 2 and 3, the base (100) of the self-balancing double-layer stretcher is installed in a vehicle box, two servo electric cylinders (200) are installed on the base (100) through Hooke's joints (400), the other two servo electric cylinders (200) are installed on the base (100) through revolute joints (500), and four electric cylinders are connected with the stretcher body (300) through spherical hinges (600), and the stretcher body (300) comprises a lower-layer stretcher (301) and an upper-layer stretcher (302).
In the vehicle, 4 light wounded persons can sit on or put one heavy wounded person stretcher on the lower stretcher (301), and one heavy wounded person stretcher is put on the upper stretcher (302), namely, the control object can realize rescue of two heavy wounded persons or one to four light wounded persons and one heavy wounded person.
The base in the stretcher is provided with an IMU inertial measurement unit which can provide real-time acceleration a of the vehicle in three directions of the horizontal direction of the front and back, the left and right and the vertical direction of the up and down mx ,a my ,a mz Angular velocity omega from three directions of rotation mα ，ω mβ ，ω mγ 。
In addition, if the vehicle in which the stretcher is located can provide these speed position information, the stretcher may also perform calculation using the information provided by the vehicle.
The servo electric cylinder shown in the figure can feed back the travel and the speed of the electric cylinder in real time, so that the distance l between the connecting point corresponding to the electric cylinder and the base and the connecting point between the electric cylinder and the lower stretcher can be obtained 1 ,l 2 ,l 3 ,l 4 And velocity l 1 ，l 2 ，l 3 ，l 4 。
The vehicle-mounted double-layer stretcher can be applied to the following control scheme of the vehicle-mounted double-layer stretcher vibration reduction platform, and the steps are as follows:
1. the control scheme of the invention mainly relies on active vibration reduction to maintain the double-layer stretcher at alpha A ，γ A Balance in degrees of freedom, as shown in FIG. 2, the stretcher is maintained in y by means of passive vibration damping (spring mechanism) A Balance in degrees of freedom. The invention mainly focuses on the way of actively controlling vibration damping.
2. According to the rotation matrix of the satellite coordinate system and the inertial coordinate system of the base, we can obtain the initial values of alpha and gamma, alpha m0 ＝arctan(a my /a mz ),γ m0 ＝-arcsin(a mx /g). By integrating the time, we can obtain the real-time angle alpha in the alpha and gamma directions of the base mA ＝α m0 +ω α t,γ mA ＝γ m0 +ω γ t。
3. Because the sensor obtains a value having a drift error, and the vehicle travels on a bumpy road surface, the acceleration value is changed more frequently. The acceleration measured by the sensor is processed by low-pass filtering, the diagonal velocity is processed by high-pass filtering, and then the acceleration is substituted into the formula in 2, so that more accurate attitude information alpha can be obtained A ，γ A The method comprises the steps of carrying out a first treatment on the surface of the And utilizes the real-time acceleration a my Through low-pass filtering processing, speed integration and position integration are carried out, and accurate position information y is obtained A 。
4. From the configuration of the double layer cot (shown in FIG. 1), we can obtain a 1 、a 3 、b 1 、b 3 The values of c, to compensate for pose, using robot kinematics we have:
and deriving the time on both sides, we can obtain:
/>
thus, we can obtain the desired electric cylinder expansion speed l tar1 ,l tar2 ,l tar3 ,l tar4 And a desired length l between upper and lower corresponding connection points tar1 ，l tar2 ，l tar3 ，l tar4 。
5. By the virtual power method, we get the kinetic equation under the task space (operation space):
wherein Mt Representing an inertia matrix under task space, F Gt Representing gravity terms under task space, F St Representing the elastic force and damping force items under the task space, F Ct Representing the coriolis force and centrifugal force terms in the task space,u t representing the control force in the task space, the relationship between the control force and the four electric cylinders is as follows t ＝R 0 [τ 1 τ 2 τ 3 τ 4 ] T 。R 0 Transformation matrix, τ, representing task space and joint space i The forces of the four bars are respectively indicated.
6. Each shaft in the electric cylinder can feed back actual speed and position information in real time. The actual acceleration, speed and position information of each shaft can be obtained through differential processing of the feedback speed information, and then the real-time output of each shaft is calculated by substituting the dynamic model According to the transmission relation between the shaft and the motor, eta i For the transmission ratio of the motor, we can get the moment feed-forward on each shaft +.>
7. The method adds the expected speed and the expected position into the speed ring, superimposes the calculated feedforward value of the torque output and the control output of the given value of the controller in the current ring, transmits the superimposed value to the motors of all shafts, refreshes the torque feedforward value in real time, and accordingly performs torque feedforward compensation, and a control frame is shown in fig. 4.
8. Because the use scene of the double-layer stretcher has multiple possibilities, under the condition of loading four wounded persons and one wounded person and loading two wounded persons, the inertia matrix in the kinetic equation is inconsistent with the gravity term in the task space, so that the feedforward force compensated under each condition is inconsistent. Thus, we use adaptive control to correct the inertia matrix and gravity terms in the kinetic equation to make a double layer stretcher adaptable to a variety of situations.
9. In order to identify the load situation in the self-adaptive control, we can read the current in the electric cylinder in real time to calculate the real-time output of the electric cylinder, and set the actual output of the electric cylinder as tau i ' the robot dynamics equation can be expressed as
wherein τi The control rate is calculated to obtain the output of the electric cylinder, J represents the Jacobian matrix of the robot, K p ，K d ，K e Respectively representing a rigidity coefficient matrix, a damping coefficient matrix and a contact force matrix of the robot.
10. In this embodiment, the adaptive policy control module trains the control rate in 9 by using a BP neural network adaptive control algorithm, calculates the gravity term under the corresponding inertia matrix and task space according to the position error, speed error, moment error and load moment of the robot at different moments, and transmits the obtained controller parameters to the control rate in 9.
11. The position error, the speed error and the moment error of each electric cylinder at different moments and the load moment on each shaft in the joint space under different situations are used as input signals of the BP neural networkThe input is As weight term, b i Is a bias term.
Neurons of the hidden layer of a BP neural network excite sigma using a nonlinear excitation function sigmod j Obtaining hidden layer output sigma' j I.e. h i ＝[h 1i h 2i h 3i h 4i ] T ，The error between the BP neural network output and the ideal output is e (t), and the error performance function is expressed as +.>Then setting the precision l according to the gradient hill-down method r =0.03, update->And repeating the cycle until e (t) is smaller than the set value of 0.01, and performing iteration to obtain the desired control rate. A network block diagram of the BP neural network is shown in fig. 6.
wherein ,σi Input signals representing the respective electric cylinders at different moments;M i the position error, the speed error and the moment error of each electric lever at different moments and the load moment of each connecting shaft in the joint space are respectively represented.
The weights of the position error, the speed error, the moment error and the load moment are respectively represented; b i Is a constant bias term, and w i 、σ i Input u together forming BP neural network i ；σ j Representing an input signal in a BP neural network; sigma'. i Representing hidden layer output in the BP neural network; h is a i ＝[h 1i h 2i h 3i h 4i ] T Representing the output of the hidden layer.
In [ u1, u2, u3, u4 ]]Input u representing BP neural network i ；b i For constant bias term unchanged, y i Representing an output of the neural network; and the error between the BP neural network output and the ideal output is e (t).
Specifically, the BP neural network error performance function is expressed as E (t); l (L) r The calculation accuracy expressed as BP neural network;the derivatives of the BP neural network error performance function for the weight terms are expressed; />Expressed as BP neural network errorDerivative of energy function to hidden layer output term; />Expressed as the derivative of the BP neural network error performance function with respect to the bias term.
12. The control rate obtained by training the method in 10 and 11 (shown in fig. 5 and 6) is reused in the control frame of fig. 4, and the control frame replaces kp3 in fig. 4, so that the double-layer stretcher structure can be maintained at alpha A ，γ A Balance in degrees of freedom.
The self-balancing double-layer stretcher structure is suitable for the self-balancing double-layer stretcher structure shown in the figure, and the self-balancing double-layer stretcher structure can achieve good vibration reduction effect, so that injuries to wounded persons caused by jolt are greatly reduced. Meanwhile, the self-adaptive control can also automatically identify and adapt to different loads of wounded persons on the stretcher (two wounded persons can lie or one wounded person can lie, one to four wounded persons sit), and parameters are automatically adjusted to maintain the bed balance under different scenes.
The vibration damping device has the advantages that the mode of feedforward force compensation is adopted for speed and position control, the position and posture of the bed are accurately controlled, the vibration damping performance of the double-layer stretcher is greatly improved, the vibration damping device can be suitable for various medical ambulances, and compared with the traditional passive vibration damping mode, the vibration damping performance is obviously improved.
Meanwhile, the double-layer stretcher structure is proposed, compared with the traditional single-layer stretcher structure, the double-layer stretcher is more complex in structure, but one double-layer stretcher can be used by more wounded persons, so that the cost is reduced from a large-scale perspective, and the efficiency is improved.
It should be noted that the above embodiments can be freely combined as needed. The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.

Claims (8)

1. The control method of the vibration reduction platform of the vehicle-mounted double-layer stretcher is characterized by comprising the following steps of:
acquiring a three-dimensional angular velocity value and a three-dimensional acceleration value of a vehicle-mounted double-layer stretcher to calculate pose information and velocity information of the vehicle-mounted double-layer stretcher; the method specifically comprises the following steps: receiving a three-dimensional angular velocity value and a three-dimensional acceleration value of the vehicle-mounted double-layer stretcher transmitted by an IMU inertial measurement unit of a base of the vehicle-mounted double-layer stretcher; obtaining pose information and speed information according to a rotation matrix of a satellite coordinate system and an inertial coordinate system of a base of the vehicle-mounted double-layer stretcher, the three-dimensional angular speed value and the three-dimensional acceleration value, wherein the method specifically comprises the following steps of: high-pass filtering is carried out on the three-dimensional angular velocity value, and low-pass filtering is carried out on the three-dimensional acceleration value;
the initial pose information is obtained by inputting the three-dimensional angular velocity value after high-pass filtering and the three-dimensional acceleration value after low-pass filtering into the following formulas:
α m0 ＝arctan(a my /a mz ),γ m0 ＝-arcsin(a mx /g)；
α mA ＝α m0 +ω α t,γ mA ＝γ m0 +ω γ t；
performing low-pass filtering, speed integration and position integration on the three-dimensional acceleration value to obtain pose information;
wherein ,αm0 Is the initial angle in the alpha direction; alpha mx The acceleration in the x-axis direction is obtained after low-pass filtering; alpha my The acceleration in the y-axis direction obtained after the low-pass filtering; alpha mz The acceleration in the z-axis direction is obtained after low-pass filtering; gamma ray m0 Is the initial angle in the gamma direction; g is gravity acceleration; omega α Is the angular velocity in the alpha direction after high-pass filtering; omega γ Is the angular velocity in the gamma direction after high pass filtering; t is the current moment; alpha mA Alpha is alpha A A target angle in the direction; gamma ray mA Is gamma A A target angle in the direction;
according to a robot kinematic model, the pose information and the speed information, calculating expected telescopic speeds of all electric cylinders of the vehicle-mounted double-layer stretcher and expected lengths of connecting shafts between the electric cylinders and connecting points of a base and a lower-layer stretcher of the vehicle-mounted double-layer stretcher;
based on a robot dynamics model, the expected telescopic speed, the expected length and the real-time acceleration corresponding to each connecting shaft, obtaining expected positions, expected speeds and real-time moment feedforward of each connecting shaft;
inputting expected positions, expected speeds and real-time moment feedforward of all connecting shafts and load moment of all connecting shafts under different situations by using a BP neural network to obtain target real-time moment feedforward so as to update control rate parameters;
And based on the control rate parameter, enabling the vibration reduction platform to perform self-balancing.
2. The method according to claim 1, wherein calculating the desired telescopic speed of each electric cylinder of the on-vehicle double-layer stretcher and the desired length of the connecting shaft between the electric cylinder and the base and lower-layer stretcher connecting point of the on-vehicle double-layer stretcher according to the robot kinematics model, the pose information and the speed information comprises:
obtaining a according to the configuration of the vehicle-mounted double-layer stretcher 1 、a 3 、b 1 、b 3 、c；
Obtaining the expected length of a connecting shaft between the telescopic speed and the connecting points of the electric cylinder, the base of the vehicle-mounted double-layer stretcher and the lower-layer stretcher by using a robot kinematics model:
equation one:
formula II:
wherein, the K value is a constant matrix obtained by deriving the formula I; gamma ray A In the Z-axis direction, y A In the Y-axis direction, alpha A In the X-axis direction, l tar1 ,l tar2 ,l tar3 ,l tar4 The expected telescopic speeds of the electric cylinders of the vehicle-mounted double-layer stretcher are respectively;the expected lengths of connecting shafts between the electric cylinder and the base and between the electric cylinder and the connecting points of the lower stretcher of the vehicle-mounted double-layer stretcher are respectively set; a, a 1 Half of the hinge point position of the first electric cylinder and the second electric cylinder on the lower stretcher; a, a 3 Half of the position of the hinge point of the third electric cylinder and the fourth electric cylinder on the lower stretcher; b 1 Half of the hinge point position of the upper stretcher is the hinge point position of the first electric cylinder and the second electric cylinder; b 3 Half of the position of the hinge point of the third electric cylinder and the fourth electric cylinder on the upper stretcher; c is the distance between the third electric cylinder and the first electric cylinder.
3. The control method of the vibration reduction platform of the vehicle-mounted double-layer stretcher according to claim 2, characterized by comprising, before the desired position, the desired speed and the real-time moment feedforward of each connecting shaft are obtained based on the robot dynamics model, the desired telescopic speed, the desired length and the real-time acceleration corresponding to each connecting shaft:
and obtaining a robot dynamics model by a virtual power method:
wherein ,Mt Representing an inertia matrix under task space, F Gt Representing gravity terms under task space, F St Representing the elastic force and damping force items under the task space, F Ct Representing the Coriolis force and centrifugal force terms in the task space, u t Representing control force under task space, u t ＝R 0 [τ 1 τ 2 τ 3 τ 4 ] T ；R 0 Representing task space and joint spaceIs a transform matrix of τ i The forces of the four cylinders are indicated respectively.
4. The method for controlling a vibration reduction platform of a vehicular double-layer stretcher according to claim 3, wherein the obtaining the feedforward of the desired position, the desired speed and the real-time moment of each connecting shaft based on the robot dynamics model, the desired telescopic speed, the desired length and the real-time acceleration corresponding to each connecting shaft includes:
Differentiating the real-time speed and the real-time position to obtain the expected position and the expected speed;
inputting the expected position and the expected speed into the robot dynamics model to obtain real-time output of the electric cylinder;
according to the transmission relation between the connecting shaft and the motor, obtaining real-time moment feedforward of the connecting shaft:
wherein ,fi Is real-time moment feedforward;the real-time output of the electric cylinder is realized; η (eta) i Is the transmission ratio of the motor.
5. The control method of the vibration reduction platform of the vehicle-mounted double-layer stretcher according to claim 3, wherein the steps of inputting the expected position, the expected speed, the real-time moment feedforward of each connecting shaft and the load moment of each connecting shaft under different situations by using the BP neural network, obtaining the target real-time moment feedforward to update the control rate parameter include:
correcting an inertia array and a gravity term of the robot dynamics model based on the expected positions, expected speeds and real-time moment feedforward of each connecting shaft and the load moment of each connecting shaft under different situations through the BP neural network so as to acquire the target real-time moment feedforward;
wherein, the robot dynamics model is:
wherein ,ltari To the desired position, l i For a real-time location,for the desired speed, ++>For real-time speed τ i The actual output of the electric cylinder is set as tau' i ；/>The real-time output of the electric cylinder is realized; j is a jacobian matrix of the robot; k (K) p ，K d ，K e Respectively representing a rigidity coefficient matrix, a damping coefficient matrix and a contact force matrix of the robot.
6. The control method of the vibration reduction platform of the vehicle-mounted double-layer stretcher according to claim 5, wherein the steps of inputting the expected position, the expected speed, the real-time moment feedforward of each connecting shaft and the load moment of each connecting shaft under different situations by using the BP neural network, obtaining the target real-time moment feedforward to update the control rate parameter include:
based on the expected position, expected speed and real-time moment feedforward of each connecting shaft and the load moment of each connecting shaft under different situations, calculating and obtaining the position error, speed error and moment error of each electric cylinder of the vehicle-mounted double-layer stretcher at different moments and the load moment of each connecting shaft under different situations;
taking the position error, the speed error and the moment error of each electric cylinder of the vehicle-mounted double-layer stretcher at different moments and the load moment of each connecting shaft under different conditions as input signals of the BP neural network
Wherein the input is:the weight items are weights respectively representing position errors, speed errors, moment errors and load moment; b i Is a bias term;
neurons of a hidden layer of the BP neural network adopt a nonlinear excitation function sigmod to excite sigma j Obtaining hidden layer output sigma' j I.e. h i ＝[h 1i h 2i h 3i h 4i ] T ， h 1i 、h 2i 、h 3i 、h 4i The results obtained by processing the position errors, the speed errors and the moment errors of the electric cylinders of the double-layer stretcher at different moments and the load moment of each connecting shaft under different situations through the weight term wi and the bias term bi are respectively used as the input of the BP neural network hidden layer nonlinear excitation function sigmod, and the obtained four outputs are used as the output items of the BP neural network hidden layer and are combined into h i I represents the number of each electric cylinder, h of all electric cylinders i Together referred to as sigma' j J represents the iteration number of the neural network;
wherein ,yi For the output of the neural network, h i Is the output of the hidden layer; u1, u2, u3, u4 represent the input u of the BP neural network i The method respectively represents the position error, the speed error, the moment error and the load moment passing weight item w of each connecting shaft under different situations of each electric cylinder of the double-layer stretcher at different moments i And bias term b i A result obtained after the processing is used as an input item of the BP neural network; u (u) i Representing the ith electricityCylinder inputs, all u i Together referred to as sigma i As an input to the overall neural network, j represents the number of iterations of the neural network;
the error between the BP neural network output and the ideal output is e (t), and the error performance function is expressed as
Setting the precision l according to the gradient hill-drop method r =0.03, update wherein ,w′i B 'for updated weight term' i Is the updated bias term; h's' i Outputting the updated hidden layer;
repeating the loop until e (t) is smaller than the set value, and training to obtain a control rate parameter;
wherein ,M i respectively representing the position error, the speed error and the moment error of each electric lever at different moments and the load moment of each connecting shaft in the joint space; sigma (sigma) j Representing an input signal in a BP neural network; sigma'. j Representing hidden layer output in the BP neural network;
e 1 (t)、e 2 (t)、e 3 (t)、e 4 (t) respectively representing the errors between the network output and the ideal output after the position errors of the various electric cylinders pass through the BP neural network; the speed error of each electric cylinder at different time passes through the BP neural network and then is output by the network and is output with the ideal error; the moment errors of the various electric cylinders at different times pass through the BP neural network and then are output by the network and the error between the ideal output is generated; the load moment of each connecting shaft in the joint space passes through the BP neural network and then is output by the network and is an error between ideal output; BP neural network error performance The function is denoted as E (t); l (L) r Expressed as the computational accuracy of the BP neural network.
7. A vibration reduction platform for a vehicular double-layer stretcher, comprising:
the acquisition module is used for acquiring a three-dimensional angular velocity value and a three-dimensional acceleration value of the vehicle-mounted double-layer stretcher so as to calculate pose information and velocity information of the vehicle-mounted double-layer stretcher; the method specifically comprises the following steps: receiving a three-dimensional angular velocity value and a three-dimensional acceleration value of the vehicle-mounted double-layer stretcher transmitted by an IMU inertial measurement unit of a base of the vehicle-mounted double-layer stretcher; obtaining pose information and speed information according to a rotation matrix of a satellite coordinate system and an inertial coordinate system of a base of the vehicle-mounted double-layer stretcher, the three-dimensional angular speed value and the three-dimensional acceleration value, wherein the method specifically comprises the following steps of: high-pass filtering is carried out on the three-dimensional angular velocity value, and low-pass filtering is carried out on the three-dimensional acceleration value;
the initial pose information is obtained by inputting the three-dimensional angular velocity value after high-pass filtering and the three-dimensional acceleration value after low-pass filtering into the following formulas:
α m0 ＝arctan(a my /a mz ),γ m0 ＝-arcsin(a mx /g)；
α mA ＝α m0 +ω α t,γ mA ＝γ m0 +ω γ t；
performing low-pass filtering, speed integration and position integration on the three-dimensional acceleration value to obtain pose information;
wherein ,αm0 Is the initial angle in the alpha direction; alpha mx The acceleration in the x-axis direction is obtained after low-pass filtering; alpha my The acceleration in the y-axis direction obtained after the low-pass filtering; alpha mz The acceleration in the z-axis direction is obtained after low-pass filtering; gamma ray m0 Is the initial angle in the gamma direction; g is gravity acceleration; omega α Is the angular velocity in the alpha direction after high-pass filtering; omega γ Is the angular velocity in the gamma direction after high pass filtering; t is the current moment; alpha mA Alpha is alpha A A target angle in the direction;γ mA is gamma A A target angle in the direction;
the calculation module is used for calculating the expected telescopic speed of each electric cylinder of the vehicle-mounted double-layer stretcher and the expected length of a connecting shaft between the electric cylinder and the connecting point of the base and the lower-layer stretcher of the vehicle-mounted double-layer stretcher according to the robot kinematic model, the pose information and the speed information;
the dynamics module is used for obtaining expected positions, expected speeds and real-time moment feed-forward of the connecting shafts based on a robot dynamics model, the expected telescopic speed, the expected length and the real-time acceleration corresponding to the connecting shafts;
the self-adaptive control module is used for inputting expected positions, expected speeds and real-time moment feedforward of each connecting shaft and load moment of each connecting shaft under different situations by using the BP neural network, and obtaining target real-time moment feedforward so as to update control rate parameters;
And the balancing module is used for self-balancing the vibration reduction platform based on the control rate parameter.
8. A vehicular double-layer stretcher, characterized by comprising:
the vehicle, the bed body, the first servo electric cylinder, the second servo electric cylinder, the third servo electric cylinder, the fourth servo electric cylinder and the base of the vehicle-mounted double-layer stretcher; the vibration reduction platform of claim 7;
the bed body comprises a lower layer stretcher and an upper layer stretcher; the base is installed in a box body of the vehicle;
the first servo electric cylinder and the second servo electric cylinder are arranged on the base through a Hooke's joint; the third servo electric cylinder and the fourth servo electric cylinder are mounted on the base through a revolute pair joint;
the first servo electric cylinder, the second servo electric cylinder, the third servo electric cylinder and the fourth servo electric cylinder are respectively connected with the bed body through a spherical hinge.
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