CN114017461A - 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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CN114017461A
CN114017461A CN202111342080.5A CN202111342080A CN114017461A CN 114017461 A CN114017461 A CN 114017461A CN 202111342080 A CN202111342080 A CN 202111342080A CN 114017461 A CN114017461 A CN 114017461A
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vehicle
stretcher
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CN114017461B (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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    • 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
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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
    • 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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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 so as to calculate pose information and velocity information of the vehicle-mounted double-layer stretcher; calculating expected stretching speeds of all electric cylinders of the vehicle-mounted double-layer stretcher and expected lengths of connection points of all the electric cylinders and a base of the vehicle-mounted double-layer stretcher and a lower-layer stretcher according to the robot kinematics model, the pose information and the speed information; obtaining expected positions, expected speeds and real-time moment feedforward of the connecting shafts based on the robot dynamic model, the expected stretching speeds, the expected lengths and the real-time accelerations corresponding to the connecting shafts; inputting the expected position, the expected speed, the real-time moment feedforward and the load moment of each connecting shaft by using a BP neural network so as to update the control rate parameters; and carrying out self-balancing on the vibration reduction platform based on the control rate parameter. The vibration angle and the speed are compensated 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 prior ambulance or military rescue vehicle, the used stretcher has no damping effect or only slight damping effect, and has buffering performance and belongs to passive damping. As shown in fig. 1, such a stretcher has poor damping or cushioning properties, and has a certain damping effect on vibrations having a high frequency, but has little damping effect on vibrations having a low vibration frequency and a large fluctuation range, which may seriously affect the treatment of a patient or a wounded person. For example, in a battlefield or other situations, the ground is uneven, and if the vibration reduction of the stretcher in the rescue vehicle is not performed, the life safety of the wounded person may be affected.
In response to the phenomenon and the requirement, a self-balancing vibration reduction stretcher is urgently needed.
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 damping 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 the vehicle-mounted double-layer stretcher so as to calculate pose information and velocity information of the vehicle-mounted double-layer stretcher;
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 according to a robot kinematic model, the pose information and the speed information;
obtaining expected positions, expected speeds and real-time moment feedforward of the connecting shafts based on a robot dynamic model, the expected stretching speeds, the expected lengths and the real-time accelerations corresponding to the connecting shafts;
inputting 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 by using a BP (back propagation) neural network to obtain a target real-time moment feedforward so as to update the control rate parameter;
and based on the control rate parameter, enabling the vibration reduction platform to be self-balanced.
Further preferably, the acquiring a three-dimensional angular velocity value and a three-dimensional acceleration value of the vehicle-mounted double-layered stretcher to calculate pose information and velocity information of the vehicle-mounted double-layered 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 inertia measurement unit of a base of the vehicle-mounted double-layer stretcher;
and obtaining the pose information and the speed information according to a rotation matrix of a satellite coordinate system and an inertia coordinate system of a base of the vehicle-mounted double-layer stretcher, the three-dimensional angular velocity value and the three-dimensional acceleration value.
Further preferably, the obtaining the pose information and the velocity information according to the rotation matrix of the satellite coordinate system and the inertial coordinate system of the base of the vehicle-mounted double-layered stretcher, the three-dimensional angular velocity value and the three-dimensional acceleration value includes:
carrying out high-pass filtering on the three-dimensional angular velocity value, and carrying out low-pass filtering on the three-dimensional acceleration value;
inputting the three-dimensional angular velocity value after high-pass filtering and the three-dimensional acceleration value after low-pass filtering into the following formula to obtain initial pose information:
αm0＝arctan(amy/amz),γm0＝-arcsin(amx/g)；
αmA＝αm0+ωαt,γmA＝γm0+ωγt；
carrying out low-pass filtering, velocity integration and position integration on the three-dimensional acceleration value to obtain the position information;
wherein ,αm0Is the initial angle in the alpha direction; alpha is alphamxAcceleration in the x-axis direction obtained after low-pass filtering; alpha is alphamyAcceleration in the y-axis direction obtained after low-pass filtering; alpha is alphamzAcceleration in the z-axis direction obtained after low-pass filtering; gamma raym0Is the initial angle in the gamma direction; g is the acceleration of gravity; omegaαIs the angular velocity in the alpha direction after high-pass filtering; omegaγIs the angular velocity in the gamma direction after high-pass filtering; t is the current time; alpha is alphamAIs alphaAA target angle in direction; gamma raymAIs gammaAA target angle in the direction.
Further preferably, the calculating, according to the robot kinematics model, the pose information and the speed information, an expected telescopic speed of each electric cylinder of the vehicle-mounted double-layered stretcher and an expected length of a connecting shaft between the electric cylinder and a base of the vehicle-mounted double-layered stretcher and a connecting point of a lower-layer stretcher includes:
according to the configuration of the vehicle-mounted double-layer stretcher, a is obtained1、a3、b1、b3、c；
Obtaining the expected length of the telescopic speed, the electric cylinder and a connecting shaft between the base of the vehicle-mounted double-layer stretcher and the connecting point of the lower-layer stretcher by utilizing a robot kinematics model:
Figure BDA0003352502840000031
Figure BDA0003352502840000032
wherein ,ltar1,ltar2,ltar3,ltar4Respectively setting the expected stretching speed of each electric cylinder of the vehicle-mounted double-layer stretcher;
Figure BDA0003352502840000033
the expected lengths of the connecting shafts between the electric cylinder and the base of the vehicle-mounted double-layer stretcher and the connecting point of the lower-layer stretcher are respectively.
Further preferably, before the obtaining of the desired position, the desired speed, and the real-time moment feedforward of each connecting shaft based on the robot dynamic model, the desired telescopic speed, the desired length, and the real-time acceleration corresponding to each connecting shaft, the method includes:
acquiring a robot dynamics model by a virtual power method:
Figure BDA0003352502840000041
wherein ,MtRepresenting an inertia matrix in task space, FGtRepresenting the gravity term in task space, FStRepresenting the elastic and damping force terms in the task space, FCtRepresenting the terms of Coriolis force and centrifugal force in task space, utRepresenting control force in task space, ut＝R0[τ1τ2τ3τ4]T；R0A transformation matrix, τ, representing the task space and the joint spaceiThe forces of the four electric cylinders are respectively represented.
Further preferably, the obtaining of the desired position, the desired speed, and the real-time moment feedforward of each connecting shaft based on the robot dynamic 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 dynamic model to obtain the real-time output of the electric cylinder;
obtaining real-time moment feedforward of the connecting shaft according to the transmission relation between the connecting shaft and the motor:
Figure BDA0003352502840000042
wherein ,fiFeeding forward a real-time moment;
Figure BDA0003352502840000043
is an electric cylinderForce is exerted in time; etaiIs the transmission ratio of the motor.
Further preferably, the obtaining of the target real-time torque feedforward to update the control rate parameter by 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 includes:
correcting an inertia matrix and a gravity term of the robot dynamics model through the BP neural network based on 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 to obtain the target real-time moment feedforward;
wherein the robot dynamics model is:
Figure BDA0003352502840000044
wherein ,τiThe output of the electric cylinder is adopted, and J is a Jacobian matrix of the robot; kp，Kd，KeRespectively representing a rigidity coefficient matrix, a damping coefficient matrix and a contact force matrix of the robot.
Further preferably, the obtaining of the target real-time torque feedforward to update the control rate parameter by 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 includes:
calculating and obtaining position errors, speed errors and moment errors of each electric cylinder of the vehicle-mounted double-layer stretcher at different moments and load moments of each connecting shaft under different situations based on expected positions, expected speeds, real-time moment feedforward of each connecting shaft and the load moments of each connecting shaft under different situations;
using 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 situations as input signals of the BP neural network
Figure BDA0003352502840000051
Wherein, the input is:
Figure BDA0003352502840000052
as weight terms, biIs a bias term;
neurons of a hidden layer of the BP neural network excite sigma by adopting a nonlinear excitation function sigmodjObtaining the hidden layer output sigmaj', i.e. hi＝[h1i h2i h3i h4i]T，
Figure BDA0003352502840000053
The error of the BP neural network output and the ideal output is e (t), and the error performance function of the BP neural network output is expressed as
Figure BDA0003352502840000054
According to the gradient downhill method, the accuracy l is setrUpdate when equal to 0.03
Figure BDA0003352502840000055
Figure BDA0003352502840000056
And repeating the loop until the e (t) is less than the set value, and finishing iteration and training to obtain the control rate parameter.
A vibration-damped platform for a vehicle-mounted double-layered 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 expected stretching 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 according to a robot kinematics model, the pose information and the speed information;
the dynamics module is used for obtaining expected positions, expected speeds and real-time moment feedforward of the connecting shafts based on a robot dynamics model, the expected stretching speeds, the expected lengths and the real-time acceleration corresponding to the connecting shafts;
the self-adaptive control module is used for inputting 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 by using a BP (back propagation) neural network to obtain a target real-time moment feedforward so as to update the control rate parameters;
and the balancing module is used for enabling the vibration reduction platform to be self-balanced on the basis of the control rate parameter.
An on-board double-layered stretcher comprising:
the device comprises a vehicle, a bed body, a first servo electric cylinder, a second servo electric cylinder, a third servo electric cylinder, a fourth servo electric cylinder and a base of the vehicle-mounted double-layer stretcher; the vibration dampening platform of claim 9;
the bed body comprises a lower-layer stretcher bed and an upper-layer stretcher bed; the base is arranged 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 joint; the third servo electric cylinder and the fourth servo electric cylinder are arranged on the base through revolute pair joints;
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 spherical hinges.
The invention provides a vehicle-mounted double-layer stretcher, a vibration reduction platform of the vehicle-mounted double-layer stretcher and a control method, which have the following beneficial effects:
1) the invention compensates the vibration angle and speed based on the kinematics and dynamics of the robot, has better effect than the acceleration compensation and passive vibration reduction used in the common active vibration reduction, can achieve good vibration reduction effect by applying the structure, and greatly reduces the injury of jolting to the wounded.
2) The invention uses speed and position control and is assisted by a feed-forward force compensation mode, accurately controls the posture of the bed body, 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 can be suitable for various scenes on the basis of accurate control, and can be adapted (or laid or seated) to different using modes of the self-balancing double-layer stretcher bed structure. The self-adaptive control can also automatically identify and adapt to different loads of wounded persons on the stretcher (two heavy wounded persons can lie or one heavy wounded person lies, one to four light wounded persons sit on the stretcher), and parameters are automatically adjusted to maintain bed body balance under different scenes.
Drawings
The above features, technical characteristics, advantages and implementation manners of the vehicle-mounted double-layer stretcher, the vibration reduction platform of the vehicle-mounted double-layer stretcher and the control method thereof will be further described in a clear and understandable manner by referring to the accompanying drawings.
Fig. 1 is a schematic view of a double-layered cot of the present invention;
fig. 2 is a schematic view of the structural arrangement of the double-layered cot of the present invention with respect to orientation;
fig. 3 is a schematic view of the structural arrangement of the double-layered cot of the present invention with respect to the components;
FIG. 4 is a schematic view of the control framework of the present invention;
FIG. 5 is a control schematic diagram of a BP neural network in the present invention;
FIG. 6 is a network block diagram of a BP neural network in the present invention;
fig. 7 is a schematic view of a method for controlling a vibration reduction platform of a vehicle-mounted double-layered stretcher according to the present invention;
fig. 8 is a schematic view of a vibration damping platform of a vehicle-mounted double-layer stretcher.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, 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 will 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, the drawings only schematically show the parts relevant to the present invention, and they do not represent the actual structure as a product. In addition, in order to make the drawings concise and understandable, components having the same structure or function in some of the drawings are only schematically illustrated or only labeled. In this document, "one" means not only "only one" but also a case of "more than one".
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
In addition, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not intended to indicate or imply 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 be made with reference to the accompanying drawings. It is obvious that the drawings in the following description are only some examples of the invention, and that for a person skilled in the art, other drawings and embodiments can be derived from them without inventive effort.
In an embodiment of the present invention, a method for controlling a vibration reduction platform of a vehicle-mounted double-layered stretcher includes:
s100, 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.
Specifically, an IMU inertia measurement unit is arranged on a base in the vehicle-mounted double-layer stretcher bed, so that real-time acceleration a of a vehicle in three directions, namely the front direction, the back direction, the left-right horizontal direction and the up-down vertical direction can be providedmx,amy,amzAngular velocities ω with three directions of rotationmα，ωmβ，ωmγ. In addition, if the vehicle in which the stretcher is located can provide the speed and position information, the stretcher may be calculated 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/inertia conduction unit, high-pass filtering and low-pass filtering are used for calculating accurate pose information and speed information of the platform.
S200, calculating expected stretching speeds of all electric cylinders of the vehicle-mounted double-layer stretcher and expected lengths of the electric cylinders and connecting shafts between bases of the vehicle-mounted double-layer stretcher and lower-layer stretcher connecting points according to the robot kinematics model, the pose information and the speed information.
Specifically, the servo electric cylinder (200) shown in fig. 2 can feed back the stroke and speed of the electric cylinder in real time, and we can obtain the distance l between the connecting point of the electric cylinder and the base and the connecting point of the electric cylinder and the lower stretcher bed from the distance l1,l2,l3,l4And speed
Figure BDA0003352502840000091
Illustratively, according to a robot kinematic model of the vibration reduction platform of the vehicle-mounted double-layer stretcher and the obtained pose information and speed information, the expected telescopic speed of each electric cylinder in the joint space and the expected length of a connecting shaft between corresponding connecting points are obtained.
S300, obtaining expected positions, expected speeds and real-time moment feedforward of the connecting shafts based on the robot dynamic model, the expected stretching speeds, the expected lengths and the real-time accelerations corresponding to the connecting shafts.
Specifically, the real-time acceleration is obtained according to the real-time actual position and speed fed back by each shaft of the connecting mechanism.
And calculating a kinematic and dynamic model of the parallel robot according to the configuration of the vibration reduction platform of the vehicle-mounted double-layer stretcher, and respectively substituting the expected stretching speed, the expected length and the real-time acceleration corresponding to each connecting shaft to obtain the length of the shaft of each electric cylinder in the joint space at the moment, namely the expected length of the shaft, the expected position, the expected speed output by the electric cylinder and a force feedforward value.
S400, inputting 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 by using a BP neural network, and obtaining a target real-time moment feedforward to update the control rate parameters.
Specifically, the desired position, the desired speed, the torque feedforward and the load torque under different situations of each electric cylinder are input by using the BP neural network, so as to obtain a control rate parameter related to the output torque, update the control rate parameter in real time, and obtain a new torque feedforward value, as shown in fig. 5.
S500, based on the control rate parameter, enabling the vibration reduction platform to be self-balanced.
Specifically, the control scheme of the invention mainly depends on active vibration reduction to maintain the vehicle-mounted double-layer stretcher at alphaA，γAThe balance in the degree of freedom, as shown in fig. 2, is maintained in y by means of passive damping (spring mechanism)ABalance in degrees of freedom.
Illustratively, a new moment feedforward value is calculated by using the calculated new moment feedforward value, and is substituted into a control frame in fig. 4, and is updated in real time, and a PID parameter in the control frame is adjusted, so that a self-balancing function of the vehicle-mounted double-layer stretcher damping platform is realized, and an algorithm is completed.
Wherein, 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 is composed 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 (derivative) controllers have been used for centuries as the first controllers in practical use and are still the most widely used industrial controllers. The PID controller is simple and easy to understand, and does not need prerequisites such as an accurate system model in use, so that the PID controller becomes the most widely applied controller.
In this embodiment, this proposal is applicable to the self-balancing double-deck 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 and cause the 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 heavy wounded persons can lie or one heavy wounded person can lie, and one to four light wounded persons sit), and parameters are automatically adjusted to maintain bed body balance under different scenes.
This proposal use speed, position control assist with the mode of feedforward force compensation, and accurate control bed body position appearance has improved the damping performance of double-deck stretcher by a wide margin, can be applicable to on various medical ambulances, compares traditional passive damping mode, has obviously promoted the performance of damping.
Meanwhile, compared with the traditional single-layer stretcher structure, the double-layer stretcher structure is more complex in structure, but one double-layer stretcher can be used by more wounded persons, so that the cost is reduced in a large-scale view, and the efficiency is improved.
Example two
Based on the above embodiments, the same parts as those in the above embodiments are not repeated, and this embodiment provides a method for controlling a vibration damping platform of a vehicle-mounted double-layered stretcher, wherein:
the method for acquiring the three-dimensional angular velocity value and the three-dimensional acceleration value of the vehicle-mounted double-layer stretcher so as to calculate the pose information and the 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 inertia measurement unit of the base of the vehicle-mounted double-layer stretcher.
And obtaining the pose information and the speed information according to a rotation matrix of a satellite coordinate system and an inertia coordinate system of a base of the vehicle-mounted double-layer stretcher, the three-dimensional angular velocity value and the three-dimensional acceleration value.
The obtaining of the pose information and the velocity 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 velocity value and the three-dimensional acceleration value comprises:
carrying out high-pass filtering on the three-dimensional angular velocity value, and carrying out low-pass filtering on the three-dimensional acceleration value;
inputting the three-dimensional angular velocity value after high-pass filtering and the three-dimensional acceleration value after low-pass filtering into the following formula to obtain initial pose information:
αm0＝arctan(amy/amz),γm0＝-arcsin(amx/g)；
αmA＝αm0+ωαt,γmA＝γm0+ωγt；
carrying out low-pass filtering, velocity integration and position integration on the three-dimensional acceleration value to obtain the position information;
wherein ,αm0Is the initial angle in the alpha direction; alpha is alphamxAcceleration in the x-axis direction obtained after low-pass filtering; alpha is alphamyAcceleration in the y-axis direction obtained after low-pass filtering; alpha is alphamzAcceleration in the z-axis direction obtained after low-pass filtering; gamma raym0Is the initial angle in the gamma direction; g is the acceleration of gravity; omegaαIs the angular velocity in the alpha direction after high-pass filtering; omegaγIs the angular velocity in the gamma direction after high-pass filtering; t is the current time; alpha is alphamAIs alphaAA target angle in direction; gamma raymAIs gammaAA target angle in the 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,αm0＝arctan(amy/amz),γm0＝-arcsin(amxin terms of/g). Then, by integrating the time, the real-time angle alpha in the alpha and gamma directions of the base can be obtainedmA＝αm0+ωαt,γmA＝γm0+ωγt。
Because the values obtained by the sensors have drift errors and the vehicle travels over a bumpy surface, the acceleration values change more frequently. The acceleration measured by the sensor is processed by low-pass filtering, the angular velocity is processed by high-pass filtering, and the acceleration and the angular velocity are substituted into the formula, so that more accurate attitude information alpha can be obtainedA，γA(ii) a And using real-time acceleration amyThrough low-pass filtering processing, speed integration and position integration are carried out to obtain more accurate position information yA。
According to the robot kinematics model, the position and orientation 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 the electric cylinder and a connecting shaft between a base of the vehicle-mounted double-layer stretcher and a connecting point of a lower-layer stretcher, comprising the following steps:
according to the configuration of the vehicle-mounted double-layer stretcher, a is obtained1、a3、b1、b3、c。
Obtaining the expected length of the telescopic speed, the electric cylinder and a connecting shaft between the base of the vehicle-mounted double-layer stretcher and the connecting point of the lower-layer stretcher by utilizing a robot kinematics model:
Figure BDA0003352502840000121
Figure BDA0003352502840000122
wherein ,ltar1,ltar2,ltar3,ltar4Respectively setting the expected stretching speed of each electric cylinder of the vehicle-mounted double-layer stretcher;
Figure BDA0003352502840000131
the expected lengths of the connecting shafts between the electric cylinder and the base of the vehicle-mounted double-layer stretcher and the connecting point of the lower-layer stretcher are respectively.
Illustratively, we can obtain a according to the configuration of a double-layer stretcher (as shown in fig. 1)1、a3、b1、b3C, in order to compensate the pose, using robot kinematics, we have:
1、
Figure BDA0003352502840000132
and by deriving the time from both sides of the above formula, we can obtain
2、
Figure BDA0003352502840000133
Wherein, the K value is a constant matrix derived from the formula in 1, and specifically includes:
Figure BDA0003352502840000134
therefore, the expected expansion and contraction speed l of the electric cylinder can be obtainedtar1,ltar2,ltar3,ltar4And a desired length between upper and lower corresponding connection points
Figure BDA0003352502840000141
Wherein, as shown in fig. 1, a1-a4 respectively represent the hinge point positions of the four cylinders and the lower platform; B1-B4 show the hinge point positions of the four cylinders with the upper platform, respectively.
a1 represents half the distance between a1 and a 2; a3 represents half the distance between A3 and a 4; b1 represents half the distance between B1 and B2; b3 represents half the distance between B3 and B4. c represents the distance between the third (four) cylinders and the first (two) cylinders (parallel to each other) as viewed in the plane direction of xByB/xAyA.
Before obtaining the expected position, the expected speed and the real-time moment feedforward of each connecting shaft based on the robot dynamic model, the expected stretching speed, the expected length and the real-time acceleration corresponding to each connecting shaft, the method comprises the following steps:
acquiring a robot dynamics model by a virtual power method:
Figure BDA0003352502840000142
wherein ,MtRepresenting an inertia matrix in task space, FGtRepresenting the gravity term in task space, FStRepresenting the elastic and damping force terms in the task space, FCtRepresenting the terms of Coriolis force and centrifugal force in task space, utRepresenting control force in task space, ut＝R0[τ1τ2τ3τ4]T；R0A transformation matrix, τ, representing the task space and the joint spaceiThe forces of the four electric cylinders are respectively represented.
Wherein the kinetic equation has ut，ut＝R0[τ1τ2τ3τ4]T。τiRepresents [ tau ]1τ2τ3τ4]Represents the calculated forces of the four cylinders, here τiWith lower surface τiIn the equation of dynamicsiThe values are consistent.
Based on the robot dynamic model, the expected stretching speed, the expected length and the corresponding real-time acceleration of each connecting shaft, the expected position, the expected speed and the real-time moment feedforward of each connecting shaft are obtained, and the method 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 dynamic model to obtain the real-time output of the electric cylinder. Obtaining real-time moment feedforward of the connecting shaft according to the transmission relation between the connecting shaft and the motor:
Figure BDA0003352502840000151
wherein ,fiFeeding forward a real-time moment;
Figure BDA0003352502840000152
real-time output of the electric cylinder; etaiIs the transmission ratio of the motor.
For example, each shaft in the electric cylinder can feed back the actual speed and position information in real time. The feedback speed information is subjected to differential processing to obtain the actual acceleration, speed and position information of each shaft, and the actual acceleration, speed and position information is substituted into the dynamic model to calculate the real-time output of each shaft
Figure BDA0003352502840000153
According to the transmission relationship of shaft and motor, etaiFor the transmission ratio of the motor, the moment feedforward on each shaft can be obtained
Figure BDA0003352502840000154
On the basis of adding the expected speed and the expected position into the speed ring, the calculated feedforward value of the torque output and the control output of the given value of the controller in the current ring are superposed and are transmitted to the motors of all the shafts, the feedforward value of the torque is refreshed in real time, and accordingly the feedforward compensation of the torque is performed, and a control frame is shown in fig. 4.
The method for obtaining the target real-time moment feedforward to update the control rate parameters by 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 comprises the following steps:
and correcting an inertia matrix and a gravity term of the robot dynamic model through the BP neural network based on 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 so as to obtain the target real-time moment feedforward.
Wherein the robot dynamics model is:
Figure BDA0003352502840000155
wherein ,τiThe output of the electric cylinder is adopted, and J is a Jacobian matrix of the robot; kp，Kd，KeRespectively representing a rigidity coefficient matrix, a damping coefficient matrix and a contact force matrix of the robot.
Specifically, because the use scenario of the double-layer stretcher has multiple possibilities, under the condition that four light wounded persons are loaded and one heavy wounded person is loaded and two heavy wounded persons are loaded, the inertia matrix in the kinetic equation is not consistent with the gravity term in the task space, so that the magnitude of the compensated feedforward force is not consistent under each situation.
Therefore, we use adaptive control to modify the inertia matrix and the gravity term in the dynamic equation to make the double-layered stretcher applicable to various situations.
In order to identify the load situation in the self-adaptive control, the real-time output of the electric cylinder can be calculated by reading the current in the electric cylinder in real time and setting the actual output of the electric cylinder as taui', the robot dynamics equation can be expressed as:
Figure BDA0003352502840000161
wherein τiIs the electric cylinder output calculated by the control rate, J represents the Jacobian matrix of the robot, Kp，Kd，KeRespectively 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 a corresponding inertia array and a gravity term in a task space according to a position error, a speed error, a moment error and a load moment of the robot, which are different at each moment, 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 parameters by 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 comprises the following steps:
and calculating and acquiring 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 situations based on 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.
Using 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 situations as input signals of the BP neural network
Figure BDA0003352502840000162
Wherein, the input is:
Figure BDA0003352502840000163
as weight terms, biIs a bias term;
neurons of a hidden layer of the BP neural network excite sigma by adopting a nonlinear excitation function sigmodjObtaining the hidden layer output sigmaj', i.e. hi＝[h1i h2i h3i h4i]T，
Figure BDA0003352502840000164
The error of the BP neural network output and the ideal output is e (t), and the error performance function of the BP neural network output is expressed as
Figure BDA0003352502840000171
According to the gradient downhill method, the accuracy l is setrUpdate when equal to 0.03
Figure BDA0003352502840000172
Figure BDA0003352502840000173
And repeating the loop until the e (t) is less than the set value, and finishing iteration and training to obtain the control rate parameter.
Specifically, the position error, the speed error, 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
Figure BDA0003352502840000174
Input is as
Figure BDA0003352502840000175
As weight terms, biIs the bias term.
Neurons of a hidden layer of a BP neural network excite sigma by adopting a nonlinear excitation function sigmodjObtaining the hidden layer output sigmaj', i.e. hi＝[h1i h2i h3i h4i]T，
Figure BDA0003352502840000176
The error of the BP neural network output and the ideal output is e (t), and the error performance function of the BP neural network output is expressed as
Figure BDA0003352502840000177
Then, according to the gradient hill descending method, the precision l is setrUpdate when equal to 0.03
Figure BDA0003352502840000178
And repeating the loop until e (t) is less than the set value of 0.01, finishing the iteration, and training to obtain the desired control rate. A network block diagram of the BP neural network is shown in fig. 6.
The enabling the active damping platform to be self-balancing based on the control rate parameter includes:
and replacing the control rate parameter with the control rate parameter corresponding to the real-time moment feedforward 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 (as shown in fig. 5 and 6) is reused in the control frame of fig. 4, instead of kp3 in fig. 4, the double-layered stretcher structure can be maintained at αA，γABalance in degrees of freedom.
In the embodiment, the scheme compensates the vibration angle and the vibration speed based on the kinematics and the dynamics of the robot, and the effect is superior to the acceleration compensation and the passive vibration reduction used in the general active vibration reduction;
the scheme can be suitable for various scenes on the basis of accurate control, and can be adapted (or lie or sit) to different using modes of the self-balancing double-layer stretcher bed structure shown in the figure.
EXAMPLE III
Based on the above embodiments, the same parts as those in the above embodiments are not repeated, and this embodiment provides a damping platform for a vehicle-mounted double-layered stretcher, as shown in fig. 8, including:
the acquiring module 100 is configured to acquire 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.
And the calculation module 200 is used for calculating the expected stretching 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 of the vehicle-mounted double-layer stretcher and a connecting point of a lower-layer stretcher according to the robot kinematics model, the pose information and the speed information.
And the dynamics module 300 is used for 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 stretching speed, the expected length and the real-time acceleration corresponding to each connecting shaft.
And the adaptive control module 400 is used for inputting 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 by using the BP neural network to obtain a target real-time moment feedforward so as to update the control rate parameters.
A balancing module 500, configured to enable the vibration reduction platform to be self-balancing based on the control rate parameter.
Based on the above embodiments, the same parts as those in the above embodiments are not repeated, and this embodiment provides a vehicle-mounted double-layered stretcher, as shown in fig. 2 and 3, including:
the device comprises a vehicle, a bed body, a first servo electric cylinder, a second servo electric cylinder, a third servo electric cylinder, a fourth servo electric cylinder and a base of the vehicle-mounted double-layer stretcher; the vibration reduction platform.
The bed body comprises a lower-layer stretcher bed and an upper-layer stretcher bed; the base is arranged 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 joint; the third servo electric cylinder and the fourth servo electric cylinder are arranged on the base through revolute pair joints; 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 spherical hinges.
The control scheme of the double-layer stretcher bed is realized through main control to realize self balance. The structural scheme of the double-layer stretcher is as shown in fig. 2 and fig. 3, a base (100) of the self-balancing double-layer stretcher is installed in a vehicle box body, two servo electric cylinders (200) are installed on the base (100) through hooke joint joints (400), the other two servo electric cylinders (200) are installed on the base (100) through revolute pair joints (500), the four electric cylinders are connected with a 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 a vehicle, 4 light wounded persons can sit on the lower-layer stretcher bed (301) or a heavy wounded person stretcher is placed on the upper-layer stretcher bed (302), namely, the control object can realize the rescue of two heavy wounded persons or one to four light wounded persons and one heavy wounded person.
The inner base of the stretcher bed is provided with an IMU inertia measurement unit which can provide three directions of the front and back, the left and right horizontal direction and the up and down vertical direction of the vehicleReal time acceleration a in directionmx,amy,amzAngular velocities ω with three directions of rotationmα，ωmβ，ωmγ。
In addition, if the vehicle in which the stretcher is located can provide the speed and position information, the stretcher may be calculated using the information provided by the vehicle.
The servo electric cylinder shown in the figure can feed back the stroke and the speed of the electric cylinder in real time, and 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-layer stretcher bed can be obtained1,l2,l3,l4And speed
Figure BDA0003352502840000191
The vehicle-mounted double-layer stretcher can be applied to the following control scheme of the vibration damping platform of the vehicle-mounted double-layer stretcher, and comprises the following steps:
1. the control scheme of the invention mainly depends on active vibration reduction to maintain the double-layer stretcher at alphaA，γABalancing in degrees of freedom, as shown in FIG. 2, by means of passive damping (spring mechanism) to maintain the gantry in yABalance in degrees of freedom. The invention mainly focuses on the way of actively controlling vibration reduction.
2. According to the random coordinate system and the rotation matrix of the inertial coordinate system of the base, the initial values of alpha and gamma can be obtainedm0＝arctan(amy/amz),γm0＝-arcsin(amxIn terms of/g). Then, by integrating the time, the real-time angle alpha in the alpha and gamma directions of the base can be obtainedmA＝αm0+ωαt,γmA＝γm0+ωγt。
3. Because the values obtained by the sensors have drift errors and the vehicle travels over a bumpy surface, the acceleration values change more frequently. The acceleration measured by the sensor is processed by low-pass filtering, the angular velocity is processed by high-pass filtering, and then the angular velocity is substituted into the formula 2, so that more accurate attitude information alpha can be obtainedA，γA(ii) a And using real-time additionSpeed amyThrough low-pass filtering processing, speed integration and position integration are carried out to obtain more accurate position information yA。
4. According to the configuration of the double-layer stretcher (shown in figure 1), we can obtain a1、a3、b1、b3C, in order to compensate the pose, using robot kinematics, we have:
Figure BDA0003352502840000201
and by deriving the two sides of the above formula with respect to time, we can obtain:
Figure BDA0003352502840000202
therefore, the expected expansion and contraction speed l of the electric cylinder can be obtainedtar1,ltar2,ltar3,ltar4And a desired length between upper and lower corresponding connection points
Figure BDA0003352502840000203
5. By the virtual power method, we obtain the kinetic equation under the task space (operating space):
Figure BDA0003352502840000211
wherein MtRepresenting an inertia matrix in task space, FGtRepresenting the gravity term in task space, FStRepresenting the elastic and damping force terms in the task space, FCtRepresenting the terms of Coriolis force and centrifugal force in task space, utThe control force in the task space is shown, and the relation between the control force and the four electric cylinders is as followst＝R0[τ1τ2τ3τ4]T。R0A transformation matrix, τ, representing the task space and the joint spaceiThen respectively represent fourThe force of the bar.
6. Each shaft in the electric cylinder can feed back actual speed and position information in real time. The feedback speed information is subjected to differential processing to obtain the actual acceleration, speed and position information of each shaft, and the actual acceleration, speed and position information is substituted into the dynamic model to calculate the real-time output of each shaft
Figure BDA0003352502840000212
According to the transmission relationship of shaft and motor, etaiFor the transmission ratio of the motor, the moment feedforward on each shaft can be obtained
Figure BDA0003352502840000213
7. On the basis of adding the expected speed and the expected position into the speed ring, the calculated feedforward value of the torque output and the control output of the given value of the controller in the current ring are superposed and are transmitted to the motors of all the shafts, the feedforward value of the torque is refreshed in real time, and accordingly the feedforward compensation of the torque is performed, 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 that four light wounded persons are loaded and one heavy wounded person is loaded and two heavy wounded persons are loaded, the inertia matrix in the dynamic equation is not consistent with the gravity term in the task space, so that the magnitude of the compensated feedforward force is not consistent under each situation. Therefore, we use adaptive control to modify the inertia matrix and the gravity term in the dynamic equation to make the double-layered stretcher applicable to various situations.
9. In order to identify the load situation in the self-adaptive control, the real-time output of the electric cylinder can be calculated by reading the current in the electric cylinder in real time and setting the actual output of the electric cylinder as taui', the robot dynamics equation can be expressed as
Figure BDA0003352502840000214
wherein τiIs the electric cylinder output calculated by the control rate, J represents the Jacobian matrix of the robot, Kp，Kd，KeIndividual watchAnd displaying a stiffness 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 a corresponding inertia matrix and a gravity term in a task space according to a position error, a speed error, a moment error and a load moment of the robot at different times, and transmits the obtained controller parameters to the control rate in 9.
11. Wherein, the position error, the speed error, the moment error of each electric cylinder at different time and the load moment on each shaft under different conditions of joint space are used as the input signals of the BP neural network
Figure BDA0003352502840000221
Input is as
Figure BDA0003352502840000222
As weight terms, biIs the bias term.
Neurons of a hidden layer of a BP neural network excite sigma by adopting a nonlinear excitation function sigmodjObtaining the hidden layer output sigmaj', i.e. hi＝[h1i h2i h3i h4i]T，
Figure BDA0003352502840000223
The error of the BP neural network output and the ideal output is e (t), and the error performance function of the BP neural network output is expressed as
Figure BDA0003352502840000224
Then, according to the gradient hill descending method, the precision l is setrUpdate when equal to 0.03
Figure BDA0003352502840000225
And repeating the loop until e (t) is less than the set value of 0.01, finishing the iteration, and training to obtain the desired control rate. A network block diagram of the BP neural network is shown in fig. 6.
wherein ,σiIndicating inputs of individual cylinders at different timesA signal;
Figure BDA0003352502840000227
Mithe 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 joint space are respectively shown.
Figure BDA0003352502840000226
Respectively representing the weight of the position error, the speed error, the moment error and the load moment; biIs a constant bias term, with wi、σiInputs u together forming a BP neural networki；σjRepresenting an input signal in a BP neural network; sigmaj' represents hidden layer output in the BP neural network; h isi＝[h1i h2i h3i h4i]TRepresenting the output of the hidden layer.
Figure BDA0003352502840000231
In, [ u1, u2, u3, u4 ]]Representing inputs u of BP neural networki；biConstant bias term, yiRepresenting an output of the neural network; the error between the BP neural network output and the ideal output is e (t).
Specifically, the error performance function of the BP neural network is expressed as E (t); lrExpressed as the calculation accuracy of the BP neural network;
Figure BDA0003352502840000232
expressed as the derivative of the error performance function of the BP neural network to each weight term;
Figure BDA0003352502840000233
expressed as the derivative of the error performance function of the BP neural network to the output item of the hidden layer;
Figure BDA0003352502840000234
denoted as BP neural networkThe derivative of the error performance function to the bias term.
12. The control rate (as shown in fig. 5 and 6) obtained by the training method in 10 and 11 is reused in the control frame in fig. 4, instead of kp3 in fig. 4, the double-layer stretcher structure can be maintained at alphaA，γABalance in degrees of freedom.
The proposal is suitable for the self-balancing double-layer stretcher structure shown in the figure, and the structure can achieve good vibration reduction effect by applying the proposal, thereby greatly reducing the injury to the wounded caused by bumping. Meanwhile, the self-adaptive control can also automatically identify and adapt to different loads of wounded persons on the stretcher (two heavy wounded persons can lie or one heavy wounded person can lie, and one to four light wounded persons sit), and parameters are automatically adjusted to maintain bed body balance under different scenes.
This proposal use speed, position control assist with the mode of feedforward force compensation, and accurate control bed body position appearance has improved the damping performance of double-deck stretcher by a wide margin, can be applicable to on various medical ambulances, compares traditional passive damping mode, has obviously promoted the performance of damping.
Meanwhile, compared with the traditional single-layer stretcher structure, the double-layer stretcher structure is more complex in structure, but one double-layer stretcher can be used by more wounded persons, so that the cost is reduced in a large-scale view, and the efficiency is improved.
It should be noted that the above embodiments can be freely combined as necessary. The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.

Claims (10)

1. A control method of a vibration reduction platform of a vehicle-mounted double-layer stretcher is characterized by comprising the following steps:
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;
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 according to a robot kinematic model, the pose information and the speed information;
obtaining expected positions, expected speeds and real-time moment feedforward of the connecting shafts based on a robot dynamic model, the expected stretching speeds, the expected lengths and the real-time accelerations corresponding to the connecting shafts;
inputting 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 by using a BP (back propagation) neural network to obtain a target real-time moment feedforward so as to update the control rate parameter;
and based on the control rate parameter, enabling the vibration reduction platform to be self-balanced.
2. The method for controlling the vibration damping platform of the vehicle-mounted double-layered stretcher according to claim 1, wherein the step of obtaining the three-dimensional angular velocity value and the three-dimensional acceleration value of the vehicle-mounted double-layered stretcher to calculate the pose information and the velocity information of the vehicle-mounted double-layered stretcher comprises the steps of:
receiving a three-dimensional angular velocity value and a three-dimensional acceleration value of the vehicle-mounted double-layer stretcher transmitted by an IMU inertia measurement unit of a base of the vehicle-mounted double-layer stretcher;
and obtaining the pose information and the speed information according to a rotation matrix of a satellite coordinate system and an inertia coordinate system of a base of the vehicle-mounted double-layer stretcher, the three-dimensional angular velocity value and the three-dimensional acceleration value.
3. The method for controlling the vibration damping platform of the vehicle-mounted double-layered stretcher according to claim 2, wherein the obtaining of the pose information and the velocity information according to the rotation matrix of the satellite coordinate system and the inertial coordinate system of the base of the vehicle-mounted double-layered stretcher, the three-dimensional angular velocity value and the three-dimensional acceleration value comprises:
carrying out high-pass filtering on the three-dimensional angular velocity value, and carrying out low-pass filtering on the three-dimensional acceleration value;
inputting the three-dimensional angular velocity value after high-pass filtering and the three-dimensional acceleration value after low-pass filtering into the following formula to obtain initial pose information:
αm0＝arctan(amy/amz),γm0＝-arcsin(amx/g)；
αmA＝αm0+ωαt,γmA＝γm0+ωγt；
carrying out low-pass filtering, velocity integration and position integration on the three-dimensional acceleration value to obtain the position information;
wherein ,αm0Is the initial angle in the alpha direction; alpha is alphamxAcceleration in the x-axis direction obtained after low-pass filtering; alpha is alphamyAcceleration in the y-axis direction obtained after low-pass filtering; alpha is alphamzAcceleration in the z-axis direction obtained after low-pass filtering; gamma raym0Is the initial angle in the gamma direction; g is the acceleration of gravity; omegaαIs the angular velocity in the alpha direction after high-pass filtering; omegaγIs the angular velocity in the gamma direction after high-pass filtering; t is the current time; alpha is alphamAIs alphaAA target angle in direction; gamma raymAIs gammaAA target angle in the direction.
4. The method for controlling the vibration damping platform of the vehicle-mounted double-layered stretcher according to claim 3, wherein the calculating of the expected telescopic speed of each electric cylinder of the vehicle-mounted double-layered stretcher and the expected length of the connection shaft between the electric cylinder and the base of the vehicle-mounted double-layered stretcher and the connection point of the lower-layered stretcher according to the robot kinematics model, the pose information and the speed information comprises:
according to the configuration of the vehicle-mounted double-layer stretcher, a is obtained1、a3、b1、b3、c；
Obtaining the expected length of the telescopic speed, the electric cylinder and a connecting shaft between the base of the vehicle-mounted double-layer stretcher and the connecting point of the lower-layer stretcher by utilizing a robot kinematics model:
Figure FDA0003352502830000031
Figure FDA0003352502830000032
wherein ,ltar1,ltar2,ltar3,ltar4Respectively setting the expected stretching speed of each electric cylinder of the vehicle-mounted double-layer stretcher;
Figure FDA0003352502830000033
the expected lengths of the connecting shafts between the electric cylinder and the connecting points of the base and the lower layer stretcher of the vehicle-mounted double-layer stretcher are respectively set; a is1The position of the first electric cylinder and the second electric cylinder is half of the position of a hinge point of the lower layer stretcher; a is3The third electric cylinder and the fourth electric cylinder are half of the hinge point position of the lower layer stretcher; b1The position of the hinge point of the first electric cylinder and the second electric cylinder on the upper layer stretcher is half of that of the hinge point; b3The position of the third electric cylinder and the fourth electric cylinder is half of the position of a hinge point of the upper layer stretcher; and c is the distance between the third electric cylinder and the first electric cylinder.
5. The method for controlling the vibration damping platform of the vehicle-mounted double-layered stretcher according to claim 3, wherein before the obtaining of the desired position, the desired speed and the real-time moment feedforward of each connecting shaft based on the robot dynamic model, the desired stretching speed, the desired length and the real-time acceleration corresponding to each connecting shaft, the method comprises:
acquiring a robot dynamics model by a virtual power method:
Figure FDA0003352502830000034
wherein ,MtRepresenting an inertia matrix in task space, FGtRepresenting the gravity term in task space, FStRepresenting the elastic and damping force terms in the task space, FCtRepresenting the terms of Coriolis force and centrifugal force in task space, utRepresenting control force in task space, ut＝R0[τ1 τ2 τ3 τ4]T；R0A transformation matrix, τ, representing the task space and the joint spaceiThe forces of the four electric cylinders are respectively represented.
6. The method for controlling the vibration damping platform of the vehicle-mounted double-layered stretcher according to claim 3, wherein the obtaining of the desired position, the desired speed and the real-time moment feedforward of each connecting shaft based on the robot dynamic model, the desired stretching speed, the desired length and the real-time acceleration corresponding to each connecting shaft comprises:
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 dynamic model to obtain the real-time output of the electric cylinder;
obtaining real-time moment feedforward of the connecting shaft according to the transmission relation between the connecting shaft and the motor:
Figure FDA0003352502830000041
wherein ,fiFeeding forward a real-time moment;
Figure FDA0003352502830000042
real-time output of the electric cylinder; etaiIs the transmission ratio of the motor.
7. The method for controlling the vibration damping platform of the vehicle-mounted double-layered stretcher according to claim 3, wherein the obtaining of the target real-time moment feedforward to update the control rate parameters by 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 comprises:
correcting an inertia matrix and a gravity term of the robot dynamics model through the BP neural network based on 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 to obtain the target real-time moment feedforward;
wherein the robot dynamics model is:
Figure FDA0003352502830000043
wherein ,τiThe output of the electric cylinder is adopted, and J is a Jacobian matrix of the robot; kp，Kd，KeRespectively representing a rigidity coefficient matrix, a damping coefficient matrix and a contact force matrix of the robot.
8. The method for controlling the vibration damping platform of the vehicle-mounted double-layered stretcher according to claim 7, wherein the obtaining of the target real-time moment feedforward to update the control rate parameters by 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 comprises:
calculating and obtaining position errors, speed errors and moment errors of each electric cylinder of the vehicle-mounted double-layer stretcher at different moments and load moments of each connecting shaft under different situations based on expected positions, expected speeds, real-time moment feedforward of each connecting shaft and the load moments of each connecting shaft under different situations;
using 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 situations as input signals of the BP neural network
Figure FDA0003352502830000051
Wherein, the input is:
Figure FDA0003352502830000052
as weight terms, biIs a bias term;
neurons of a hidden layer of the BP neural network excite sigma by adopting a nonlinear excitation function sigmodjTo obtain a hidden layer output σ'jI.e. hi＝[h1i h2i h3i h4i]T，
Figure FDA0003352502830000053
The error of the BP neural network output and the ideal output is e (t), and the error performance function of the BP neural network output is expressed as
Figure FDA0003352502830000054
According to the gradient downhill method, the accuracy l is setrUpdate when equal to 0.03
Figure FDA0003352502830000055
Figure FDA0003352502830000056
Repeating the loop until e (t) is less than the set value, and finishing iteration and training to obtain a control rate parameter;
wherein ,eli,
Figure FDA0003352502830000057
MiRespectively 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 joint space; sigmajRepresenting an input signal in a BP neural network; sigma'jRepresenting hidden layer output in the BP neural network;
e1(t)、e2(t)、e3(t)、e4(t) respectively representing the error between the network output and the ideal output after the position error of each electric cylinder at different moments passes through the BP neural network; the speed error of each electric cylinder at different moments passes through a BP neural network, and then the error between the network output and the ideal output is obtained; the torque error of each electric cylinder at different moments passes through a BP neural network, and then the error between the network output and the ideal output is obtained; the load moment of each connecting shaft in the joint space passes through the BP neural network and then the error between the network output and the ideal output is obtained; the error performance function of the BP neural network is expressed as E (t); lrExpressed as the computational accuracy of the BP neural network.
9. The utility model provides a damping platform of on-vehicle double-deck stretcher which characterized in that includes:
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 expected stretching 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 according to a robot kinematics model, the pose information and the speed information;
the dynamics module is used for obtaining expected positions, expected speeds and real-time moment feedforward of the connecting shafts based on a robot dynamics model, the expected stretching speeds, the expected lengths and the real-time acceleration corresponding to the connecting shafts;
the self-adaptive control module is used for inputting 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 by using a BP (back propagation) neural network to obtain a target real-time moment feedforward so as to update the control rate parameters;
and the balancing module is used for enabling the vibration reduction platform to be self-balanced on the basis of the control rate parameter.
10. A vehicle-mounted double-layer stretcher is characterized by comprising:
the device comprises a vehicle, a bed body, a first servo electric cylinder, a second servo electric cylinder, a third servo electric cylinder, a fourth servo electric cylinder and a base of the vehicle-mounted double-layer stretcher; the vibration dampening platform of claim 9;
the bed body comprises a lower-layer stretcher bed and an upper-layer stretcher bed; the base is arranged 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 joint; the third servo electric cylinder and the fourth servo electric cylinder are arranged on the base through revolute pair joints;
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 spherical hinges.
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