CN114102599A - Flexible mechanical arm-based human-computer interaction adaptive control method and system - Google Patents

Flexible mechanical arm-based human-computer interaction adaptive control method and system Download PDF

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CN114102599A
CN114102599A CN202111464122.2A CN202111464122A CN114102599A CN 114102599 A CN114102599 A CN 114102599A CN 202111464122 A CN202111464122 A CN 202111464122A CN 114102599 A CN114102599 A CN 114102599A
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servo motor
matrix
mechanical arm
connecting rod
torque
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CN114102599B (en
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李智军
刘弘暄
李国欣
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University of Science and Technology of China USTC
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University of Science and Technology of China USTC
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1602—Programme controls characterised by the control system, structure, architecture
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J18/00—Arms

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Abstract

The invention provides a man-machine interaction self-adaptive control method and system based on a flexible mechanical arm, which comprises the following steps: calculating an expected rotation angle of the servo motor through a connecting rod position, a rigidity matrix, a sliding mode vector, a dynamic regression matrix, physical parameters, a positive definite diagonal matrix, an acting force applied to the connecting rod and a known weight function of the flexible mechanical arm; calculating the output torque of the servo motor according to the current corner and the expected corner of the servo motor, the expected estimated speed of the servo motor, the position of a connecting rod, a rigidity matrix, a positive definite matrix, a sliding mode vector of the servo motor, an inertia matrix, a dynamic regression matrix and physical parameters; the servo motor outputs a torque to enable the mechanical arm to reach a new position. The invention continuously adjusts the current rotation angle and the torque of the servo motor according to the acting force of a person on the flexible mechanical arm, so that the mechanical arm can reach the expected position and avoid the damage to the person, and the invention has wide application prospect.

Description

Flexible mechanical arm-based human-computer interaction adaptive control method and system
Technical Field
The invention relates to the technical field of mechanical arm control, in particular to a man-machine interaction self-adaptive control method and system based on a flexible mechanical arm.
Background
Traditional mechanical arms often use rigid connection, are difficult to react quickly to sudden changes of human input in interaction with human, and are easy to cause injury to human due to the rigid characteristics of mechanical structures and motion tracks of the mechanical arms. Therefore, the flexible mechanical arm is produced. However, the research on the control method of the flexible robot arm is not mature, and thus the application of the flexible robot arm is also hindered.
With the development of robotics, more and more robots are entering the fields of industry, medicine, education, entertainment, etc. to automatically or assist humans in completing different kinds of tasks. Under the scenes of auxiliary assembly, rehabilitation training, teaching demonstration, interactive entertainment and the like, a human and a robot need to be in contact interaction more or less. Traditional mechanical arms often use rigid connection, are difficult to react quickly to sudden changes of human input in interaction with human, and are easy to cause injury to human due to the rigid characteristics of mechanical structures and motion tracks of the mechanical arms. Therefore, the flexible mechanical arm is produced.
However, in actual use, research on a control method of the flexible robot arm is not mature, and thus application of a robot using the flexible robot arm is also hindered. Therefore, the human-computer interaction adaptive control method of the flexible mechanical arm is researched and developed, the problem of human-computer interaction adaptive control of the flexible mechanical arm is solved, and the method has important social significance and wide market prospect.
Patent document CN108427324A (application number: CN201810324657.1) discloses a flexible mechanical arm control simulation platform and a working method thereof, wherein the simulation platform comprises a flexible mechanical arm mathematical model, a simulation platform main interface, a control scheme selection and implementation module, an expected track setting module, a control parameter setting module and a parameter state display module, a GUI language for constructing a graphical interface is used to design a human-computer interaction interface of the simulation platform, and a data interaction channel is established with a control simulation system based on Simulink. A user inputs each control parameter and model parameter of the simulation platform on a human-computer interaction interface, and parameter setting, mathematical simulation operation and simulation image display are realized by clicking each function button.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a man-machine interaction self-adaptive control method and system based on a flexible mechanical arm.
The invention provides a man-machine interaction self-adaptive control method based on a flexible mechanical arm, which comprises the following steps:
step 1: calculating an expected rotation angle of the servo motor through a connecting rod position, a rigidity matrix, a sliding mode vector, a dynamic regression matrix, physical parameters, a positive definite diagonal matrix, an acting force applied to the connecting rod and a known weight function of the flexible mechanical arm;
step 2: calculating the output torque of the servo motor according to the current corner and the expected corner of the servo motor, the expected estimated speed of the servo motor, the position of a connecting rod, a rigidity matrix, a positive definite matrix, a sliding mode vector of the servo motor, an inertia matrix, a dynamic regression matrix and physical parameters;
and step 3: and (3) outputting torque by the servo motor to enable the mechanical arm to reach a new position, returning to the step 1 to continue execution, and performing self-adaptive control of human-computer interaction on the mechanical arm.
Preferably, the flexible mechanical arm comprises a servo motor, a ball screw, a spring, a linear position sensor, a transmission belt and a connecting rod;
the ball screw is driven by a servo motor, and the connecting rod is driven by a transmission belt;
the transmission belt is connected to a nut of the ball screw through a spring, a connecting point of the transmission belt on the spring is connected with a sliding block of the linear position sensor in parallel, acting force is applied to the connecting rod, the connecting rod is forced to drive the transmission belt to move, and flexible output is achieved through the buffering effect of the spring and the torque adjustment of the servo motor.
Preferably, the servo motor outputs a rotation angle and a torque to drive the ball screw to rotate, and the rotation is converted into linear motion by a nut of the ball screw;
the linear position sensor obtains the track, the torque and the acting force of a person of the actual robot output to the outside by measuring the position of the sliding block.
Preferably, the desired rotation angle θ of the servo motordThe expression of (a) is:
Figure BDA0003389674600000021
wherein q is the position of a connecting rod of the flexible mechanical arm; k is a stiffness matrix; sqIs a sliding mode vector;
Figure BDA0003389674600000022
is a dynamics regression matrix;
Figure BDA0003389674600000023
is a physical parameter; kqIs a positive definite diagonal matrix; f. ofeActing force applied to the connecting rod; w (-) is a known weighting function.
Preferably, the expression of the output torque τ of the servo motor is:
Figure BDA0003389674600000024
wherein theta is the current rotation angle of the servo motor; thetadIs the desired rotation angle of the servo motor;
Figure BDA0003389674600000031
estimating a velocity for the servo motor; q is the position of the connecting rod; k is a stiffness matrix; kθIs a positive definite matrix;
Figure BDA0003389674600000032
a sliding mode vector of the servo motor; b is an inertia matrix;
Figure BDA0003389674600000033
is a dynamics regression matrix;
Figure BDA0003389674600000034
are physical parameters.
Preferably, the deviation Δ x of the actual position of the mechanical arm from the desired position is:
Δx＝x-xd
the sliding mode vector of the mechanical arm is as follows:
Figure BDA0003389674600000035
physical parameters
Figure BDA0003389674600000036
The update formula of (2) is:
Figure BDA0003389674600000037
wherein x is the actual position; x is the number ofdIs a desired position; j (q) is the Jacobian matrix from joint position space to Cartesian space, J+(q) is the pseudo-inverse of J (q); l isqA known positive definite matrix;
Figure BDA0003389674600000038
is a known regression matrix; e.g. of the typepIs a prediction error; alpha is alphaqIs a positive number constant.
Preferably, the observed value of the rotation angle of the servo motor deviates from the expected value thereof
Figure BDA0003389674600000039
Comprises the following steps:
Figure BDA00033896746000000310
the reference vectors are:
Figure BDA00033896746000000311
the sliding mode vector of the servo motor is as follows:
Figure BDA00033896746000000312
physical parameters
Figure BDA00033896746000000313
The update formula of (2) is:
Figure BDA00033896746000000314
wherein the content of the first and second substances,
Figure BDA00033896746000000315
in order to be an observer estimate,
Figure BDA00033896746000000316
is to
Figure BDA00033896746000000317
The integral of (a) is calculated,
Figure BDA00033896746000000318
is to
Figure BDA00033896746000000319
Differentiation of (1); alpha is alphaθIs a positive number constant; l isθIs a positive definite matrix.
The invention provides a human-computer interaction self-adaptive control system based on a flexible mechanical arm, which comprises:
module M1: calculating an expected rotation angle of the servo motor through a connecting rod position, a rigidity matrix, a sliding mode vector, a dynamic regression matrix, physical parameters, a positive definite diagonal matrix, an acting force applied to the connecting rod and a known weight function of the flexible mechanical arm;
module M2: calculating the output torque of the servo motor according to the current corner and the expected corner of the servo motor, the expected estimated speed of the servo motor, the position of a connecting rod, a rigidity matrix, a positive definite matrix, a sliding mode vector of the servo motor, an inertia matrix, a dynamic regression matrix and physical parameters;
module M3: the servo motor outputs torque to enable the mechanical arm to reach a new position, the return module M1 continues to execute, and adaptive control of human-computer interaction is conducted on the mechanical arm.
Preferably, the flexible mechanical arm comprises a servo motor, a ball screw, a spring, a linear position sensor, a transmission belt and a connecting rod;
the ball screw is driven by a servo motor, and the connecting rod is driven by a transmission belt;
the transmission belt is connected to a nut of the ball screw through a spring, the connecting point of the transmission belt on the spring is connected with a sliding block of the linear position sensor in parallel, acting force is applied to the connecting rod, the connecting rod is forced to drive the transmission belt to move, and flexible output is realized through the buffering action of the spring and the torque adjustment of the servo motor;
the servo motor outputs a corner and a torque to drive the ball screw to rotate, and the rotation is converted into linear motion by a nut of the ball screw;
the linear position sensor obtains the track, the torque and the acting force of a person of the actual robot output to the outside by measuring the position of the sliding block.
Preferably, the desired rotation angle θ of the servo motordThe expression of (a) is:
Figure BDA0003389674600000041
wherein q is the position of a connecting rod of the flexible mechanical arm; k is a stiffness matrix; sqIs a sliding mode vector;
Figure BDA0003389674600000042
is a dynamics regression matrix;
Figure BDA0003389674600000043
is a physical parameter; kqIs a positive definite diagonal matrix; f. ofeActing force applied to the connecting rod; w (-) is a known weight function;
the expression of the output torque tau of the servo motor is as follows:
Figure BDA0003389674600000044
wherein theta is the current rotation angle of the servo motor; thetadIs the desired rotation angle of the servo motor;
Figure BDA0003389674600000045
estimating a velocity for the servo motor; q is the position of the connecting rod; k is a stiffness matrix; kθIs a positive definite matrix;
Figure BDA0003389674600000046
a sliding mode vector of the servo motor; b is an inertia matrix;
Figure BDA0003389674600000047
is a dynamics regression matrix;
Figure BDA0003389674600000048
is a physical parameter;
the deviation Δ x of the actual position of the mechanical arm from the desired position is:
Δx＝x-xd
the sliding mode vector of the mechanical arm is as follows:
Figure BDA0003389674600000049
physical parameters
Figure BDA00033896746000000410
The update formula of (2) is:
Figure BDA00033896746000000411
wherein x is the actual position; x is the number ofdIs a desired position; j (q) is the Jacobian matrix from joint position space to Cartesian space, J+(q) is the pseudo-inverse of J (q); l isqA known positive definite matrix;
Figure BDA0003389674600000051
is a known regression matrix; e.g. of the typepIs a prediction error; alpha is alphaqIs a positive number constant;
deviation of observed value of servo motor corner from expected value
Figure BDA0003389674600000052
Comprises the following steps:
Figure BDA0003389674600000053
the reference vectors are:
Figure BDA0003389674600000054
the sliding mode vector of the servo motor is as follows:
Figure BDA0003389674600000055
physical parameters
Figure BDA0003389674600000056
The update formula of (2) is:
Figure BDA0003389674600000057
wherein the content of the first and second substances,
Figure BDA0003389674600000058
in order to be an observer estimate,
Figure BDA0003389674600000059
is to
Figure BDA00033896746000000510
The integral of (a) is calculated,
Figure BDA00033896746000000511
is to
Figure BDA00033896746000000512
Differentiation of (1); alpha is alphaθIs a positive number constant; l isθIs a positive definite matrix.
Compared with the prior art, the invention has the following beneficial effects:
(1) the method can control the flexible mechanical arm, and can continuously adjust the current corner and torque of the servo motor according to the acting force of a person on the flexible mechanical arm, so that the mechanical arm can reach an expected position and avoid the damage to the person, and the method has wide application prospect;
(2) the invention can make the output of the mechanical arm actively respond to the acting force of a user on the mechanical arm, continuously adjust the current rotating angle and torque of the servo motor, and continuously control the robot to reach the expected position through closed-loop control and simultaneously avoid the injury to the user.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is a schematic view of a flexible robotic arm provided in accordance with an embodiment of the present invention;
fig. 2 is a block diagram of a closed-loop system of a flexible manipulator human-machine interaction adaptive control method according to an embodiment of the present invention.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the present invention.
Example (b):
according to the man-machine interaction self-adaptive control method of the flexible mechanical arm, the flexible mechanical arm comprises a servo motor, a ball screw, a spring, a linear position sensor, a transmission belt and a connecting rod. The ball screw is driven by a servo motor, and the connecting rod is driven by a transmission belt. The transmission belt is connected to the nut of the ball screw through a spring, and the connection point of the transmission belt on the spring is connected with the slide block of the linear position sensor in parallel. The person exerts the effort to the connecting rod, forces the connecting rod to drive the drive belt to move, is adjusted by the cushioning effect of spring and the torque of servo motor, realizes flexible output.
The servo motor outputs a rotation angle theta and a torque tau to drive the ball screw to rotate, and the rotation is converted into linear motion by a nut of the ball screw.
The linear position sensor obtains the actual robot track q and the torque tau output to the outside by measuring the position of the slide block0Acting force f of mane。
As shown in FIG. 1, the method for controlling the flexible mechanical arm in a man-machine interaction self-adaptive mode comprises a servo motor 1, a ball screw 2, a spring 3, a linear position sensor 4, a transmission belt 5 and a connecting rod 6.
As shown in fig. 2, a human-computer interaction adaptive control method for a flexible manipulator includes the following steps:
(1) obtaining the joint position q of the connecting rod rotating shaft through a linear position sensor, wherein the joint position q is obtained through a formula:
x＝Γ(q)
obtaining the Cartesian space coordinates of the current corner, wherein Γ (·) is a conversion function from the joint space of the rotating shaft to the Cartesian space, and according to the formula:
Δx＝x-xd
calculating a current position x and a desired position xdThen by the formula:
Figure BDA0003389674600000061
calculating a reference vector
Figure BDA0003389674600000062
Wherein J (q) is from joint position space to fluteJacobian matrix of Carl space, J+(q) is the pseudo-inverse of J (q), feIs the action force of the human on the connecting rod, w (-) is a weight function, and is represented by the formula:
Figure BDA0003389674600000063
given, where κ, R are given constants, h (·) | · | |. non-calculation2-R2. The sliding mode vector of the connecting rod can then be calculated:
Figure BDA0003389674600000064
and physical parameters
Figure BDA0003389674600000065
The update formula of (2):
Figure BDA0003389674600000066
in the formula (I), the compound is shown in the specification,
Figure BDA0003389674600000071
is a positive definite matrix and is known,
Figure BDA0003389674600000072
Figure BDA0003389674600000073
is the error of the prediction and is,
Figure BDA0003389674600000074
is a known regression matrix that is used to determine,
Figure BDA0003389674600000075
is a known kinetic regression matrix. The updating formula is continuously updated
Figure BDA0003389674600000076
Up to
Figure BDA0003389674600000077
Becomes 0.
Finally, the desired rotational angle θ of the servo motor can be calculateddComprises the following steps:
Figure BDA0003389674600000078
where K is a rigid diagonal matrix of a known flexible manipulator, KqIs a positive definite diagonal matrix;
(2) knowing the desired angle of rotation theta of the servomotordThe position θ of the servo motor is obtained by a photoelectric sensor.
Figure BDA0003389674600000079
The observer, which is composed of the following equation, yields:
Figure BDA00033896746000000710
in the formula (I), the compound is shown in the specification,
Figure BDA00033896746000000711
for error observation, η is an auxiliary variable, β is a normal number, KeIs a positive definite matrix and the matrix is a negative definite matrix,
Figure BDA00033896746000000712
is to use the formula:
Figure BDA00033896746000000713
updated DθIs estimated model of (1), in
Figure BDA00033896746000000714
Is an estimated vector of the observer, LoIs a positive definite matrix.
Known view of the corner of the servo motorDeviation of measured value from its expected value
Figure BDA00033896746000000715
Comprises the following steps:
Figure BDA00033896746000000716
reference vector:
Figure BDA00033896746000000717
sliding mode vector of servo motor:
Figure BDA00033896746000000718
and physical parameters
Figure BDA00033896746000000719
The update formula of (2):
Figure BDA00033896746000000720
the output torque τ of the servo motor is:
Figure BDA00033896746000000721
in the formula (I), the compound is shown in the specification,
Figure BDA00033896746000000722
is to
Figure BDA00033896746000000723
The integral of (a) is calculated,
Figure BDA00033896746000000724
is to
Figure BDA00033896746000000725
Differential, uncertainty parameter of
Figure BDA00033896746000000726
Updated by the following equation:
Figure BDA00033896746000000727
(3) the servo motor outputs a torque tau, so that the mechanical arm moves to a new position, and the control algorithm returns to (1) to continue calculating the next expected rotation angle.
According to the process, the man-machine interaction self-adaptive control method of the flexible mechanical arm can adjust the output and the expected position of the motor in real time according to the acting force of a person on the flexible mechanical arm, so that the mechanical arm can reach the expected position and can avoid the injury to the person.
In the description of the present application, it is to be understood that the terms "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience in describing the present application and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and thus, should not be construed as limiting the present application.
Those skilled in the art will appreciate that, in addition to implementing the systems, apparatus, and various modules thereof provided by the present invention in purely computer readable program code, the same procedures can be implemented entirely by logically programming method steps such that the systems, apparatus, and various modules thereof are provided in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Therefore, the system, the device and the modules thereof provided by the present invention can be considered as a hardware component, and the modules included in the system, the device and the modules thereof for implementing various programs can also be considered as structures in the hardware component; modules for performing various functions may also be considered to be both software programs for performing the methods and structures within hardware components.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.

Claims (10)

1. A man-machine interaction self-adaptive control method based on a flexible mechanical arm is characterized by comprising the following steps:
step 1: calculating an expected rotation angle of the servo motor through a connecting rod position, a rigidity matrix, a sliding mode vector, a dynamic regression matrix, physical parameters, a positive definite diagonal matrix, an acting force applied to the connecting rod and a known weight function of the flexible mechanical arm;
step 2: calculating the output torque of the servo motor according to the current corner and the expected corner of the servo motor, the expected estimated speed of the servo motor, the position of a connecting rod, a rigidity matrix, a positive definite matrix, a sliding mode vector of the servo motor, an inertia matrix, a dynamic regression matrix and physical parameters;
and step 3: and (3) outputting torque by the servo motor to enable the mechanical arm to reach a new position, returning to the step 1 to continue execution, and performing self-adaptive control of human-computer interaction on the mechanical arm.
2. The human-computer interaction adaptive control method based on the flexible mechanical arm is characterized in that the flexible mechanical arm comprises a servo motor, a ball screw, a spring, a linear position sensor, a transmission belt and a connecting rod;
the ball screw is driven by a servo motor, and the connecting rod is driven by a transmission belt;
the transmission belt is connected to a nut of the ball screw through a spring, a connecting point of the transmission belt on the spring is connected with a sliding block of the linear position sensor in parallel, acting force is applied to the connecting rod, the connecting rod is forced to drive the transmission belt to move, and flexible output is achieved through the buffering effect of the spring and the torque adjustment of the servo motor.
3. The human-computer interaction self-adaptive control method based on the flexible mechanical arm is characterized in that the servo motor outputs a rotation angle and a torque to drive the ball screw to rotate, and the rotation angle and the torque are converted into linear motion by a nut of the ball screw;
the linear position sensor obtains the track, the torque and the acting force of a person of the actual robot output to the outside by measuring the position of the sliding block.
4. The flexible robotic arm-based human-computer interaction adaptive control method according to claim 1, wherein the desired rotation angle θ of the servo motordThe expression of (a) is:
Figure FDA0003389674590000011
wherein q is the position of a connecting rod of the flexible mechanical arm; k is a stiffness matrix; sqIs a sliding mode vector;
Figure FDA0003389674590000012
is a dynamics regression matrix;
Figure FDA0003389674590000013
is a physical parameter; kqIs a positive definite diagonal matrix; f. ofeActing force applied to the connecting rod; w (-) is a known weighting function.
5. The flexible mechanical arm based human-computer interaction self-adaptive control method is characterized in that the expression of the output torque tau of the servo motor is as follows:
Figure FDA0003389674590000021
wherein theta is the current rotation angle of the servo motor; thetadIs the desired rotation angle of the servo motor;
Figure FDA0003389674590000022
estimating a velocity for the servo motor; q is the position of the connecting rod; k is a stiffness matrix; kθIs a positive definite matrix;
Figure FDA0003389674590000023
a sliding mode vector of the servo motor; b is an inertia matrix;
Figure FDA0003389674590000024
is a dynamics regression matrix;
Figure FDA0003389674590000025
are physical parameters.
6. The adaptive human-computer interaction control method based on the flexible mechanical arm as claimed in claim 5, wherein the deviation Δ x between the actual position and the expected position of the mechanical arm is as follows:
Δx＝x-xd
the sliding mode vector of the mechanical arm is as follows:
Figure FDA0003389674590000026
physical parameters
Figure FDA0003389674590000027
The update formula of (2) is:
Figure FDA0003389674590000028
wherein x is the actual position; x is the number ofdIs a desired position; j (q) is from the joint positionJacobian matrix of inter-to-Cartesian space, J+(q) is the pseudo-inverse of J (q); l isqA known positive definite matrix;
Figure FDA0003389674590000029
is a known regression matrix; e.g. of the typepIs a prediction error; alpha is alphaqIs a positive number constant.
7. The flexible robotic arm-based human-machine interaction adaptive control method of claim 6, wherein an observed value of a rotation angle of the servo motor deviates from an expected value thereof
Figure FDA00033896745900000210
Comprises the following steps:
Figure FDA00033896745900000211
the reference vectors are:
Figure FDA00033896745900000212
the sliding mode vector of the servo motor is as follows:
Figure FDA00033896745900000213
physical parameters
Figure FDA00033896745900000214
The update formula of (2) is:
Figure FDA00033896745900000215
wherein the content of the first and second substances,
Figure FDA00033896745900000216
in order to be an observer estimate,
Figure FDA00033896745900000217
is to
Figure FDA00033896745900000218
The integral of (a) is calculated,
Figure FDA00033896745900000219
is to
Figure FDA00033896745900000220
Differentiation of (1); alpha is alphaθIs a positive number constant; l isθIs a positive definite matrix.
8. A human-computer interaction adaptive control system based on a flexible mechanical arm is characterized by comprising:
module M1: calculating an expected rotation angle of the servo motor through a connecting rod position, a rigidity matrix, a sliding mode vector, a dynamic regression matrix, physical parameters, a positive definite diagonal matrix, an acting force applied to the connecting rod and a known weight function of the flexible mechanical arm;
module M2: calculating the output torque of the servo motor according to the current corner and the expected corner of the servo motor, the expected estimated speed of the servo motor, the position of a connecting rod, a rigidity matrix, a positive definite matrix, a sliding mode vector of the servo motor, an inertia matrix, a dynamic regression matrix and physical parameters;
module M3: the servo motor outputs torque to enable the mechanical arm to reach a new position, the return module M1 continues to execute, and adaptive control of human-computer interaction is conducted on the mechanical arm.
9. The human-computer interaction adaptive control system based on the flexible mechanical arm is characterized in that the flexible mechanical arm comprises a servo motor, a ball screw, a spring, a linear position sensor, a transmission belt and a connecting rod;
the ball screw is driven by a servo motor, and the connecting rod is driven by a transmission belt;
the transmission belt is connected to a nut of the ball screw through a spring, the connecting point of the transmission belt on the spring is connected with a sliding block of the linear position sensor in parallel, acting force is applied to the connecting rod, the connecting rod is forced to drive the transmission belt to move, and flexible output is realized through the buffering action of the spring and the torque adjustment of the servo motor;
the servo motor outputs a corner and a torque to drive the ball screw to rotate, and the rotation is converted into linear motion by a nut of the ball screw;
the linear position sensor obtains the track, the torque and the acting force of a person of the actual robot output to the outside by measuring the position of the sliding block.
10. The system of claim 8, wherein the desired rotation angle θ of the servo motor is a desired rotation angle θdThe expression of (a) is:
Figure FDA0003389674590000036
wherein q is the position of a connecting rod of the flexible mechanical arm; k is a stiffness matrix; sqIs a sliding mode vector;
Figure FDA0003389674590000037
is a dynamics regression matrix;
Figure FDA0003389674590000038
is a physical parameter; kqIs a positive definite diagonal matrix; f. ofeActing force applied to the connecting rod; w (-) is a known weight function;
the expression of the output torque tau of the servo motor is as follows:
Figure FDA0003389674590000031
wherein theta is the current rotation angle of the servo motor; thetadIs the desired rotation angle of the servo motor;
Figure FDA0003389674590000032
estimating a velocity for the servo motor; q is the position of the connecting rod; k is a stiffness matrix; kθIs a positive definite matrix;
Figure FDA0003389674590000033
a sliding mode vector of the servo motor; b is an inertia matrix;
Figure FDA0003389674590000034
is a dynamics regression matrix;
Figure FDA0003389674590000035
is a physical parameter;
the deviation Δ x of the actual position of the mechanical arm from the desired position is:
Δx＝x-xd
the sliding mode vector of the mechanical arm is as follows:
Figure FDA0003389674590000041
physical parameters
Figure FDA0003389674590000042
The update formula of (2) is:
Figure FDA0003389674590000043
wherein x is the actual position; x is the number ofdIs a desired position; j (q) is the Jacobian matrix from joint position space to Cartesian space, J+(q) is the pseudo-inverse of J (q); l isqA known positive definite matrix;
Figure FDA0003389674590000044
is a known regression matrix; e.g. of the typepIs a prediction error; alpha is alphaqIs a positive number constant;
deviation of observed value of servo motor corner from expected value
Figure FDA0003389674590000045
Comprises the following steps:
Figure FDA0003389674590000046
the reference vectors are:
Figure FDA0003389674590000047
the sliding mode vector of the servo motor is as follows:
Figure FDA0003389674590000048
physical parameters
Figure FDA0003389674590000049
The update formula of (2) is:
Figure FDA00033896745900000410
wherein the content of the first and second substances,
Figure FDA00033896745900000411
in order to be an observer estimate,
Figure FDA00033896745900000412
is to
Figure FDA00033896745900000413
Product ofThe method comprises the following steps of dividing,
Figure FDA00033896745900000414
is to
Figure FDA00033896745900000415
Differentiation of (1); alpha is alphaθIs a positive number constant; l isθIs a positive definite matrix.
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