CN112571424B - Rehabilitation robot shaft speed direct constraint control based on SCN walking force estimation - Google Patents

Rehabilitation robot shaft speed direct constraint control based on SCN walking force estimation Download PDF

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CN112571424B
CN112571424B CN202011363054.6A CN202011363054A CN112571424B CN 112571424 B CN112571424 B CN 112571424B CN 202011363054 A CN202011363054 A CN 202011363054A CN 112571424 B CN112571424 B CN 112571424B
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trainer
speed
axis
force
walking force
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CN112571424A (en
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孙平
王鑫瑶
王殿辉
王硕玉
李树江
李宏亮
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Shenyang University of Technology
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J11/00—Manipulators not otherwise provided for
    • B25J11/008—Manipulators for service tasks
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61H—PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY
    • A61H3/00—Appliances for aiding patients or disabled persons to walk about
    • A61H3/04—Wheeled walking aids for patients or disabled persons
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/02—Programme-controlled manipulators characterised by movement of the arms, e.g. cartesian coordinate type
    • B25J9/023—Cartesian coordinate type
    • 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
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1656—Programme controls characterised by programming, planning systems for manipulators
    • B25J9/1664—Programme controls characterised by programming, planning systems for manipulators characterised by motion, path, trajectory planning
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61H—PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY
    • A61H3/00—Appliances for aiding patients or disabled persons to walk about
    • A61H3/04—Wheeled walking aids for patients or disabled persons
    • A61H2003/043—Wheeled walking aids for patients or disabled persons with a drive mechanism

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  • Engineering & Computer Science (AREA)
  • Robotics (AREA)
  • Mechanical Engineering (AREA)
  • Health & Medical Sciences (AREA)
  • Epidemiology (AREA)
  • Pain & Pain Management (AREA)
  • Physical Education & Sports Medicine (AREA)
  • Rehabilitation Therapy (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Animal Behavior & Ethology (AREA)
  • General Health & Medical Sciences (AREA)
  • Public Health (AREA)
  • Veterinary Medicine (AREA)
  • Automation & Control Theory (AREA)
  • Rehabilitation Tools (AREA)
  • Manipulator (AREA)

Abstract

The invention discloses a rehabilitation training robot shaft speed direct constraint control method based on random configuration network (Stochastic Configuration Networks, SCN) walking force estimation. The method is characterized in that: based on a dynamic model of the rehabilitation training robot, building a dynamic model of the rehabilitation robot with the walking force of a trainer by decomposing generalized input force; constructing a network estimation model of the walking force of the trainer based on the SCN method, taking a motion track and a motion speed as network input, and obtaining the walking force estimation of the trainer by continuously and randomly configuring hidden layer node parameters; the method for directly restraining the speed of each shaft is provided, the influence of the walking force of a trainer on the control precision is restrained, meanwhile, the actual movement speed of each shaft of the rehabilitation robot is directly restrained by the controller, the control precision is improved, and the safety of the trainer is guaranteed.

Description

Rehabilitation robot shaft speed direct constraint control based on SCN walking force estimation
Technical field:
the invention relates to the field of control of rehabilitation robots, in particular to a speed constraint control method of a rehabilitation robot.
The background technology is as follows:
In recent years, rehabilitation walking robots have received extensive attention from researchers. However, in practical application, the active walking force of the trainer seriously affects the control accuracy of the robot, thereby causing the tracking performance to be reduced; in addition, the rehabilitation robot is different from a general mechanical system, and the movement speed of the restraint robot is prevented from mutation, so that the rehabilitation robot has an important function of guaranteeing the safety of a trainer. Therefore, the research on the motion speed constraint method of the rehabilitation robot and the inhibition of the walking force of the trainer have important significance for improving the performance and the safety of the man-machine system.
The track tracking control of the rehabilitation walking robot has many research results, however, the results cannot solve the problem of direct constraint of the walking force and the speed of each axis of the trainer, which not only affects the tracking precision, but also threatens the safety of the trainer. So far, no control method for directly restricting the motion speed of each axis about walking force observation and a controller exists, the invention provides a walking force estimation method based on a new view angle, and researches the control method for directly restricting the motion speed of each axis, and the invention has important significance for guaranteeing the control precision and the safety of a man-machine system.
The invention comprises the following steps:
the invention aims to:
In order to solve the problems, the invention provides a direct constraint control method for each axis speed of a rehabilitation robot based on SCN walking force estimation.
The technical scheme is as follows:
the invention is realized by the following technical scheme:
A rehabilitation robot each axis speed direct constraint control method based on SCN (Stochastic Configuration Networks, SCN) walking force estimation is characterized in that:
1) Based on a dynamics model of the rehabilitation robot, building a dynamics model of the rehabilitation robot with the walking force of a trainer by decomposing generalized input force;
2) Constructing a network estimation model of the walking force of the trainer based on the SCN method, taking a motion track and a motion speed as network input, and obtaining the walking force estimation of the trainer by continuously and randomly configuring hidden layer node parameters;
3) The speed of each shaft is designed to directly restrain the controller, the influence of the walking force of a trainer on the control precision is restrained, and meanwhile, the controller directly restrains the actual movement speed of each shaft of the rehabilitation robot.
The method comprises the following steps:
Step 1) based on a dynamic model of a rehabilitation training robot, establishing the dynamic model of the rehabilitation robot with the walking force of a trainer by decomposing generalized input force. The kinetic model of the system is described below
Wherein the method comprises the steps of
X (t) = [ X (t) y (t) θ (t) ] T is the actual motion track of the X-axis, y-axis and rotation angle of the rehabilitation robot, u (t) represents the generalized input force, M represents the mass of the rehabilitation robot, M represents the mass of the rehabilitation person, I 0 represents the moment of inertia, M 0, K (θ),B (theta) is a coefficient matrix; θ represents the angle between the horizontal axis and the connection between the robot center and the first wheel center, i.e., θ=θ 1, known from the rehabilitation walker robot structure/>θ3＝θ+π，/>L i denotes the distance of the center of gravity of the system to the center of each wheel, r 0 denotes the center-to-center distance, phi i denotes the angle between the x' axis and the corresponding l i of each wheel, lambda i denotes the center-to-center distance of each wheel, i=1, 2,3,4; decomposing u (t) into tracking control force u 0 (t) to be designed and walking force Deltau 0 (t) of the trainer to be observed, and substituting the tracking control force u 0 (t) into a model (1) to obtain
Let X 1 (t) =x (t),Can obtain the rehabilitation robot dynamics model with the walking force of the trainer
Step 2) constructing a network estimation model of the walking force of the trainer based on the SCN method so as to realize the motion trail and speed of the robotAs a network input layer of the SCN, and is connected with the hidden layer through a weight ω and a threshold b, and a gaussian function is used to obtain a hidden layer output G (x (t)).
Wherein the method comprises the steps of
G(x(t))＝[g1(ω1x(t)+b1),...,gL(ωLx(t)+bL)]T，
G j(ωjx(t)+bj) is the output j= (1, 2,..and L) of the j-th node of the hidden layer, ω h,j is the weight of the j-th node of the hidden layer connected by the h-th input of the input layer, h= (1, 2,..6), and b j is the threshold of the j-th node of the hidden layer.
Then, the SCN hidden layer passes the weightsConnected with the output layer to obtain the network output of the trainer walking force estimationThe following are provided:
Wherein the method comprises the steps of
And connecting the weight g= (1, 2, 3) of the g output for the j hidden layer node.
Further, based on the training person walking force estimation error obtained when the node number of the hidden layer is L-1Randomly configuring the node parameters of the L hidden layer, and enabling the node parameters to meet the expression form delta L>0,δL as follows:
Due to
Wherein the method comprises the steps of
Therefore, it is
εL TεL-(r+μL)εL-1 TεL-1＝-δL
Wherein, the parameter 0< r <1, { mu L } is a non-negative real number sequence, mu L is less than or equal to (1-r). Epsilon L TεL＜(r+μL)εL-1 TεL-1 when delta L >0, as the number of hidden layer nodes in the random configuration increases, whenAt this time ε L TεL＜rεL-1 TεL-1, easily obtained/>I.e. the estimation of walking force of the trainer/>Step 3) designing a direct constraint controller for each shaft speed, inhibiting the influence of the walking force of a trainer on the control precision, and simultaneously, directly constraining the actual movement speed of each shaft of the rehabilitation robot by the controller, and is characterized in that: let X d(t)＝[xd(t) yd(t) θd(t)]T be the motion trail specified by doctor, define trail tracking error, speed tracking error, virtual speed tracking error as:
e(t)＝X(t)-Xd(t)＝x1(t)-Xd(t) (6)
z(t)＝x2(t)-α (8)
Wherein e (t) = [ e x(t) ey(t) eθ(t)]T ] is the track error of the rehabilitation robot in the x axis, y axis and rotation angle directions respectively; the actual speed errors of the rehabilitation robot in the x-axis, y-axis and rotation angle directions are respectively; α= [ α x(t) αy(t) αθ(t)]T ] is a virtual movement speed, and z (t) = [ z x(t) zy(t) zθ(t)]T) is a virtual speed error in x-axis, y-axis and rotation angle directions of the rehabilitation robot.
From (7) (8)
Order theAnd combined with (3) to obtain
The tracking error system for each axis direction of the x axis, the y axis and the rotation angle can be obtained by using the (10) and the (11) as follows:
The expression form of the virtual movement speed of the robot is designed as follows:
Wherein parameter c 11>0,c12>0,c13 >0.
Designing each shaft speed direct constraint controller as
Wherein the method comprises the steps ofAnd phi = phi 1+φ2+φ3, parameters c21>0,c22>0,c23>0,k11>0,k12>0,k13>0,k21>0,k22>0,k23>0.
From the controller (14), it can be seen that:
lyapunov functions for each axis direction of the x-axis, y-axis and rotation angle are designed as follows:
Deriving equation (16) along the tracking error system and substituting (9) (12) into the obtained
Substitution of formulas (13) (15) into formula (17) can be obtained
Obtainable from (18)The tracking error system (12) of each axis is asymptotically stable; and from Lyapunov function (16), it is known that/(Thereby obtaining the speed constraint range of each shaft As a result, the controller (14) suppresses the walking force of the trainer and directly restricts the movement speed of each axis.
Step 4) based on STM32F411 series singlechip provide output PWM signal to motor drive module, make recovered walking robot help the training person to keep track of doctor appointed motion track under the speed constraint, its characterized in that: STM32F411 series single-chip microcomputer is used as a main controller, and the input of the main controller is connected with a motor speed measuring module, and the output of the main controller is connected with a motor driving module; the motor driving module is connected with the direct current motor; the power supply system supplies power to the respective electrical devices. The control method of the main controller is to read the feedback signal of the motor encoder and the control command signal X d (t) and given by the main controllerAn error signal is calculated. According to the error signal, the main controller calculates the control quantity of the motor according to a preset control algorithm and sends the control quantity to the motor driving module, and the motor rotates to drive the wheels to maintain self balance and move in a specified mode.
The advantages and effects:
the invention relates to a direct constraint control method for each shaft speed of a rehabilitation robot based on SCN walking force estimation, which has the following advantages:
The invention combines the dynamics model to build a rehabilitation walking robot dynamics model with the walking force of a trainer; the network estimation model of the walking force is built based on the SCN method, and the direct constraint controller of each axis speed is designed to restrain the influence of the walking force on the control precision, and simultaneously, the motion speed of each axis is directly constrained, so that the control precision of the system is improved, and the safety of a trainer is ensured
Description of the drawings:
FIG. 1 is a block diagram of the operation of a controller according to the present invention;
FIG. 2 is a graph of a system of the present invention;
FIG. 3 shows a STM32F411 single-chip microcomputer minimum system according to the present invention;
FIG. 4 is a schematic diagram of the peripheral circuitry of MPU9250 of the present invention;
FIG. 5 is a schematic diagram of a peripheral circuit of a motor drive module according to the present invention;
Fig. 6 is a circuit of the general principles of the hardware of the present invention.
The specific embodiment is as follows:
The present invention will be further described with reference to the accompanying drawings, but the scope of the present invention is not limited by the examples.
A rehabilitation robot each axis speed direct constraint control method based on SCN walking force estimation is characterized in that:
1) Based on a dynamics model of the rehabilitation robot, building a dynamics model of the rehabilitation robot with the walking force of a trainer by decomposing generalized input force;
2) Constructing a network estimation model of the walking force of the trainer based on the SCN method, taking a motion track and a motion speed as network input, and obtaining the walking force estimation of the trainer by continuously and randomly configuring hidden layer node parameters;
3) The speed of each shaft is designed to directly restrain the controller, the influence of the walking force of a trainer on the control precision is restrained, and meanwhile, the controller directly restrains the actual movement speed of each shaft of the rehabilitation robot.
The method comprises the following steps:
Step 1) based on a dynamic model of a rehabilitation training robot, establishing the dynamic model of the rehabilitation robot with the walking force of a trainer by decomposing generalized input force. The kinetic model of the system is described below
Wherein the method comprises the steps of
X (t) = [ X (t) y (t) θ (t) ] T is the actual motion track of the X-axis, y-axis and rotation angle of the rehabilitation robot, u (t) represents the generalized input force, M represents the mass of the rehabilitation robot, M represents the mass of the rehabilitation person, I 0 represents the moment of inertia, M 0, K (θ),B (theta) is a coefficient matrix; θ represents the angle between the horizontal axis and the connection between the robot center and the first wheel center, i.e., θ=θ 1, known from the rehabilitation walker robot structure/>θ3＝θ+π，/>L i denotes the distance of the center of gravity of the system to the center of each wheel, r 0 denotes the center-to-center distance, phi i denotes the angle between the x' axis and the corresponding l i of each wheel, lambda i denotes the center-to-center distance of each wheel, i=1, 2,3,4; decomposing u (t) into tracking control force u 0 (t) to be designed and walking force Deltau 0 (t) of the trainer to be observed, and substituting the tracking control force u 0 (t) into a model (1) to obtain
Let X 1 (t) =x (t),Can obtain the rehabilitation robot dynamics model with the walking force of the trainer
Step 2) constructing a network estimation model of the walking force of a trainer based on an SCN method, taking a motion track and a speed as network input, and obtaining uncertainty estimation of a man-machine system by continuously and randomly configuring hidden layer node parameters, and is characterized in that: with the motion trail and speed of the robotAs a network input layer of the SCN, and is connected with the hidden layer through a weight ω and a threshold b, and a gaussian function is used to obtain a hidden layer output G (x (t)).
Wherein the method comprises the steps of
G(x(t))＝[g1(ω1x(t)+b1),...,gL(ωLx(t)+bL)]T，
G j(ωjx(t)+bj) is the output j= (1, 2,..and L) of the j-th node of the hidden layer, ω h,j is the weight of the j-th node of the hidden layer connected by the h-th input of the input layer, h= (1, 2,..6), and b j is the threshold of the j-th node of the hidden layer.
Then, the SCN hidden layer passes the weightsConnected with the output layer to obtain the network output of the trainer walking force estimationThe following are provided:
Wherein the method comprises the steps of
And connecting the weight g= (1, 2, 3) of the g output for the j hidden layer node.
Further, based on the training person walking force estimation error obtained when the node number of the hidden layer is L-1Randomly configuring the node parameters of the L hidden layer, and enabling the node parameters to meet the expression form delta L>0,δL as follows:
Wherein the parameter 0< r <1, { mu L } is a non-negative real number sequence, With the increasing number of hidden layer nodes of random configuration, the method is carried out until/>The estimation/> of the walking force of the trainer can be realizedStep 3) designing a direct constraint controller for each shaft speed, inhibiting the influence of the walking force of a trainer on the control precision, and simultaneously, directly constraining the actual movement speed of each shaft of the rehabilitation robot by the controller, and is characterized in that: let X d(t)＝[xd(t) yd(t) θd(t)]T be the motion trail specified by doctor, define trail tracking error, speed tracking error, virtual speed tracking error as:
e(t)＝X(t)-Xd(t)＝x1(t)-Xd(t) (6)
z(t)＝x2(t)-α (8)
Wherein e (t) = [ e x(t) ey(t) eθ(t)]T ] is the track error of the rehabilitation robot in the x axis, y axis and rotation angle directions respectively; the actual speed errors of the rehabilitation robot in the x-axis, y-axis and rotation angle directions are respectively; α= [ α x(t) αy(t) αθ(t)]T ] is a virtual movement speed, and z (t) = [ z x(t) zy(t) zθ(t)]T) is a virtual speed error in x-axis, y-axis and rotation angle directions of the rehabilitation robot.
From (7) (8)
Order theAnd combined with (3) to obtain
The tracking error system for each axis direction of the x axis, the y axis and the rotation angle can be obtained by using the (10) and the (11) as follows:
The expression form of the virtual movement speed of the robot is designed as follows:
Wherein parameter c 11>0,c12>0,c13 >0.
Designing each shaft speed direct constraint controller as
Wherein the method comprises the steps ofAnd phi = phi 1+φ2+φ3, parameters c21>0,c22>0,c23>0,k11>0,k12>0,k13>0,k21>0,k22>0,k23>0.
From the controller (14), it can be seen that:
lyapunov functions for each axis direction of the x-axis, y-axis and rotation angle are designed as follows:
Deriving equation (16) along the tracking error system and substituting (9) (12) into the obtained
Substitution of formulas (13) (15) into formula (17) can be obtained
Obtainable from (18)The tracking error system (12) of each axis is asymptotically stable; and from Lyapunov function (16), it is known that/(Thereby obtaining the speed constraint range of each shaft As a result, the controller (14) suppresses the walking force of the trainer and directly restricts the movement speed of each axis.
Step 4) based on STM32F411 series singlechip provide output PWM signal to motor drive module, make recovered walking robot help the training person to keep track of doctor appointed motion track under the speed constraint, its characterized in that: STM32F411 series single-chip microcomputer is used as a main controller, and the input of the main controller is connected with a motor speed measuring module, and the output of the main controller is connected with a motor driving module; the motor driving module is connected with the direct current motor; the power supply system supplies power to the respective electrical devices. The control method of the main controller is to read the feedback signal of the motor encoder and the control command signal X d (t) and given by the main controllerAn error signal is calculated. According to the error signal, the main controller calculates the control quantity of the motor according to a preset control algorithm and sends the control quantity to the motor driving module, and the motor rotates to drive the wheels to maintain self balance and move in a specified mode.
The invention solves the problem of walking force observation and direct constraint control of the speeds of all axes. A rehabilitation robot dynamics model with walking force is established; constructing a network estimation model of walking force based on an SCN method, taking a motion track and a speed as network input, and obtaining walking force estimation by continuously and randomly configuring hidden layer node parameters; the speed of each shaft is directly restricted by the controller, the influence of walking force on control precision is restrained, and meanwhile, the actual movement speed of each shaft is directly restricted, so that the safety of a trainer is ensured.

Claims (2)

1. The direct constraint control method for each shaft speed of the rehabilitation robot based on SCN walking force estimation is characterized by comprising the following steps of: based on a dynamics model of the rehabilitation robot, building a dynamics model of the rehabilitation robot with the walking force of a trainer by decomposing generalized input force; constructing a network estimation model of the walking force of the trainer based on the SCN method, taking a motion track and a motion speed as network input, and obtaining the walking force estimation of the trainer by continuously and randomly configuring hidden layer node parameters; the method comprises the steps of designing a direct constraint controller of each shaft speed, restraining the influence of the walking force of a trainer on control precision, and simultaneously, directly constraining the actual movement speed of each shaft of the rehabilitation robot by the controller; the method comprises the following steps:
1) Based on a dynamics model of the rehabilitation robot, building a dynamics model of the rehabilitation robot with the walking force of a trainer by decomposing generalized input force;
2) Constructing a network estimation model of the walking force of the trainer based on the SCN method, taking a motion track and a motion speed as network input, and obtaining the walking force estimation of the trainer by continuously and randomly configuring hidden layer node parameters;
3) The method comprises the steps of designing a direct constraint controller of each shaft speed, restraining the influence of the walking force of a trainer on control precision, and simultaneously, directly constraining the actual movement speed of each shaft of the rehabilitation robot by the controller;
based on the dynamics model of the rehabilitation robot, the dynamics model of the rehabilitation robot with the walking force of a trainer is established by decomposing generalized input force, and the dynamics model of the system is described as follows
Wherein the method comprises the steps of
u(t)＝[f1 f2 f3 f4]T，
X (t) = [ X (t) y (t) θ (t) ] T is the actual motion track of the X-axis, y-axis and rotation angle of the rehabilitation robot, u (t) represents the generalized input force, M represents the mass of the rehabilitation robot, M represents the mass of the rehabilitation person, I 0 represents the moment of inertia, M 0, K (θ),B (theta) is a coefficient matrix; θ represents the angle between the horizontal axis and the connection between the robot center and the first wheel center, i.e., θ=θ 1, known from the rehabilitation walker robot structure/>θ3＝θ+π，/>L i denotes the distance of the center of gravity of the system to the center of each wheel, r 0 denotes the center-to-center distance, phi i denotes the angle between the x-axis and the corresponding l i of each wheel, lambda i denotes the center-to-center distance of each wheel, i=1, 2,3,4; decomposing u (t) into tracking control force u 0 (t) to be designed and walking force Deltau 0 (t) of the trainer to be observed, and substituting the tracking control force u 0 (t) into a model (1) to obtain
Let X 1 (t) =x (t),Can obtain the rehabilitation robot dynamics model with the walking force of the trainer
The network estimation model of the walking force of the trainer is built based on the SCN method, the movement track and the speed are taken as network input, the hidden layer node parameters are continuously and randomly configured, the walking force estimation of the trainer is obtained, and the movement track and the speed of the robot are usedAs a network input layer of SCN, the SCN is connected with an hidden layer through a weight omega and a threshold b, and a Gaussian function is utilized to obtain hidden layer output G (x (t));
Wherein the method comprises the steps of
b＝[b1,b2,...,bL]T，
G(x(t))＝[g1(ω1x(t)+b1),...,gL(ωLx(t)+bL)]T，
G j(ωjx(t)+bj) is the output j= (1, 2,..l), omega h,j is the input layer h input connection hidden layer j
Weight of node, h= (1, 2,., 6), b j is threshold of the j-th node of the hidden layer;
then, the SCN hidden layer passes the weights Connected with the output layer to obtain the network output/>, of the trainer walking force estimationThe following are provided:
Wherein the method comprises the steps of
Connecting the weight g= (1, 2, 3) of the g output for the j hidden layer node;
based on the training person walking force estimation error obtained when the node number of the hidden layer is L-1 Randomly configuring the node parameters of the L hidden layer, and enabling the node parameters to meet the expression form delta L＞0,δL as follows:
wherein, the parameter 0 < r < 1, { mu L } is a non-negative real number sequence, With the increasing number of hidden layer nodes of random configuration, the method is carried out until/>The estimation/> of the walking force of the trainer can be realized
The method comprises the steps of designing a direct constraint controller for each axis speed, restraining the influence of the walking force of a trainer on control precision, simultaneously, directly constraining the actual motion speed of each axis of the rehabilitation robot by the controller, setting X d(t)＝[xd(t) yd(t) θd(t)]T as a motion track designated by a doctor, and defining a track tracking error, a speed tracking error and a virtual speed tracking error as follows:
e(t)＝X(t)-Xd(t)＝x1(t)-Xd(t) (6)
z(t)＝x2(t)-α (8)
Wherein e (t) = [ e x(t) ey(t) eθ(t)]T ] is the track error of the rehabilitation robot in the x axis, y axis and rotation angle directions respectively; The actual speed errors of the rehabilitation robot in the x-axis, y-axis and rotation angle directions are respectively; α= [ α x(t) αy(t) αθ(t)]T ] is a virtual movement speed, and z (t) = [ z x(t) zy(t) zθ(t)]T ] is virtual speed errors in x-axis, y-axis and rotation angle directions of the rehabilitation robot respectively;
From (7) (8)
Order theAnd combined with (3) to obtain
The tracking error system for each axis direction of the x axis, the y axis and the rotation angle can be obtained by using the (10) and the (11) as follows:
The expression form of the virtual movement speed of the robot is designed as follows:
Wherein parameter c 11＞0,c12＞0,c13 > 0;
Designing each shaft speed direct constraint controller as
Wherein the method comprises the steps ofAnd phi = phi 1+φ2+φ3, parameters c21＞0,c22＞0,c23＞0,k11＞0,k12＞0,k13＞0,k21＞0,k22＞0,k23＞0;
From the controller (14), it can be seen that:
lyapunov functions for each axis direction of the x-axis, y-axis and rotation angle are designed as follows:
Deriving equation (16) along the tracking error system and substituting (9) (12) into the obtained
Substitution of formulas (13) (15) into formula (17) can be obtained
Obtainable from (18)The tracking error system (12) of each axis is asymptotically stable; and from Lyapunov function (16), it is known that/(And further obtain the speed constraint range/> From this, it is clear that the controller (14) suppresses the walking force of the trainer and directly restricts the movement speed of each axis.
2. The direct constraint control method for each shaft speed of the rehabilitation robot based on SCN walking force estimation according to claim 1 is characterized in that an output PWM signal is provided for a motor driving module based on an STM32F411 series single-chip microcomputer, so that the rehabilitation walking robot helps a trainer to track a motion track appointed by a doctor under speed constraint, the STM32F411 series single-chip microcomputer is used as a main controller, and the input of the main controller is connected with a motor speed measuring module and the output of the main controller is connected with a motor driving module; the motor driving module is connected with the direct current motor; the power supply system supplies power to each electrical device; the control method of the main controller is to read the feedback signal of the motor encoder and the control command signal X d (t) and given by the main controllerCalculating to obtain an error signal; according to the error signal, the main controller calculates the control quantity of the motor according to a preset control algorithm and sends the control quantity to the motor driving module, and the motor rotates to drive the wheels to maintain self balance and move in a specified mode.
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