CN110340894A - A Fuzzy Logic-Based Adaptive Multilateral Control Method for Teleoperation System - Google Patents

Abstract

The invention discloses an adaptive multilateral control method based on fuzzy logic nonlinear remote control system. Based on the fuzzy logic function, the present invention estimates the non-power parameters of nonlinear environmental dynamics, and transmits them back to the master end through the communication channel with time delay to reconstruct the environmental force of the master end; Deterministic problem, the present invention is based on the fuzzy logic system, by designing the adaptive rate to update the parameters of the nonlinear function containing the unknown system model information online; for the position tracking performance of the system, the present invention adopts the nonlinear adaptive multilateral function based on the fuzzy logic system The control method, when there is a communication delay in the system, enables the slave robot to accurately track the trajectory signal of the master robot; for the problem of working force distribution during the collaborative operation between multiple robots, the present invention realizes the Work force distribution of multiple slave robots.

Description

A Fuzzy Logic-Based Adaptive Multilateral Control Method for Teleoperation System

technical field

The invention belongs to the field of teleoperation control, and specifically relates to a fuzzy-logic-based self-adaptive multilateral control method for a teleoperation system, while ensuring the stability and transparency of a non-linear multilateral teleoperation system and the collaborative operation performance of multi-slave robots.

Background technique

With the continuous development of electromechanical technology, the research of robot system has become a hot topic at this stage. Among them, the teleoperation robot technology relying on human-computer interaction has made staged progress, and has a wide range of applications in military, industrial and medical fields. Applications.

However, with the development of complex and fine-grained tasks, it is necessary to have multiple robots with multiple degrees of freedom in the working environment for collaborative work. Such robots often have nonlinearities and various uncertainties; in addition, with the collaborative With the increase in the number of working robots, the signal communication between multiple robots will make the signal transmission in the communication channel with time delay more complicated, and even deteriorate the stability and transparency of the teleoperation system.

Contents of the invention

The purpose of the present invention is to propose a fuzzy logic-based teleoperation system adaptive multilateral control method to solve the stability and transparency trade-offs in the traditional multilateral teleoperation system, the nonlinearity and various uncertainties of the master-slave robot, And technical issues such as multi-robot collaborative operation.

In order to achieve the above object, the specific content of the technical solution of the present invention is as follows:

A fuzzy logic-based adaptive multilateral control method for remote control systems, comprising the following steps:

(1) Establish a nonlinear dynamic model of the multilateral teleoperation system.

(2) Estimation of operating environment and reconstruction of master-end environment based on fuzzy logic system.

(3) Design the adaptive multilateral controller of the main robot based on the fuzzy logic system.

(4) Design the adaptive multilateral controller of the slave robot based on the fuzzy logic system.

Compared with the prior art, the present invention has the following beneficial effects:

1. Based on the fuzzy logic system, the non-power parameters of nonlinear environmental dynamics are estimated, and transmitted back to the main end through the communication channel with time delay to reconstruct the environmental force of the main end, thus avoiding the power signal in the communication channel The instability problem of the remote control system caused by the transmission in the vehicle, and provide the operator with accurate force feedback information.

2. Based on the fuzzy logic system, the parameters of the nonlinear function containing unknown system model information are updated online by designing the adaptive rate, thus solving various uncertainties existing in the master-slave robot.

3. Through the non-linear adaptive multilateral control method based on the fuzzy logic system, when there is a communication delay in the system, the slave robot can accurately track the trajectory signal of the master robot, thereby improving the position tracking performance of the system.

4. Through the design of a multi-robot collaborative control algorithm, the distribution of the working force of multiple slave robots is realized, thereby improving the collaborative operation performance of multiple slave robots on the task.

5. By designing Lyapunov functions, the boundedness of all signals in the nonlinear multilateral teleoperation system is guaranteed, thereby maintaining the global asymptotic stability of the system;

Description of drawings

Fig. 1 is a block diagram of the self-adaptive multilateral control of the non-linear teleoperation system based on the fuzzy logic system proposed by the present invention.

Detailed ways

In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not constitute a conflict with each other.

Now in conjunction with embodiment, accompanying drawing 1 the present invention will be further described:

Implementation technical scheme of the present invention is:

1) Establish the nonlinear dynamic model of the multilateral teleoperation system, specifically:

1-1) Establish the nonlinear dynamic model of master robot, slave robot and working environment

Among them, q _m,i , and q _s,i , Indicates the i-th master-slave robot position, velocity and acceleration signals, x _m,i , Indicates the end position of the i-th master robot, x _s,o , Indicates the position of the center of mass of the grasping target in the task, M _{m, i} and M _s represent the mass inertia matrix, C _{m, i} and C _s represent the Coriolis force/centripetal force matrix, G _{m, i} and G _s represent the gravity matrix, D _m,i and D _s represent external disturbances and modeling errors, u _m,i and u _s represent control inputs, F _h,i represent the operating force of the i-th operator, F _e represent the environment from the robot and the job task Force, i=1,2,...,n.

The above system has the following characteristics:

①0<M _m,i ≤δ _m0,i I, 0<M _s ≤δ _s0 I, where δ _m0,i ,δ _s0 >0 represents the scaling factor of the identity matrix I;

② and is a skew symmetric matrix;

③ Part of the dynamic equations in formulas (1) and (2) can be written in the form of the following linear equations:

Among them, θ _{m, i} and θ _s represent the model unknown parameters of the master-slave robot, and ζ represents the fuzzy logic matrix.

1-2) Establish a nonlinear dynamic model of the operating environment

Among them, _θe represents an unknown non-power environmental parameter.

2) Fuzzy logic system-based operating environment estimation and master-end environment reconstruction, specifically:

2-1) Write the dynamic model (3) of the slave-end operation environment into the form of radial basis neural network function, then:

F _e ＝ζ ^T (x _ew )θ _e (4)

Among them, x _ew represents the input quantity of the fuzzy logic function, and is related to x _s,o , relevant.

2-2) Definition is the optimal estimation parameter of the environment, Ω _e and Ω _e0 represent the bounded sets of x _ew and We respectively _, and the online estimation of the working environment from the end can be realized through the fuzzy logic toolbox of MATLAB.

2-3) Due to the existence of communication delay T(t), in order to prevent the transmission of power signals between communication channels from affecting the stability of the multilateral teleoperation system, the estimated values of non-power environmental parameters Pass it to the master end, so that the reconstruction environment force of the master end is:

Among them, x _emw represents the input quantity of the fuzzy logic function, and is related to x _md,i , relevant.

3) Design the adaptive multilateral controller of the main robot based on the fuzzy logic system, specifically:

3-1) Design the ideal trajectory generator for the master robot as follows:

where i=1,2,...,n, M _d , C _d , G _d represent the optimized parameters of the trajectory generator. By selecting appropriate optimization coefficients, (6)-(7) can generate the passive ideal trajectory signal x _md,i of the master robot.

3-2) Define x _m1,i = x _m,i , Then the nonlinear dynamic model (1) of the i-th master robot can be rewritten as:

3-3) Define the tracking error of the i-th master robot as:

Among them, α _m1,i represents the virtual tracking amount of the main robot.

3-4) Define the Lyapunov function V _m1,i of the first subsystem in (8) as follows:

By selecting the virtual tracking amount α _m1,i is but

3-5) Define the Lyapunov function V _m2,i of the second subsystem in (8) as follows:

3-6) Based on (8) and (9), the derivative of z _{m2, i} can be obtained as

Then, the derivative of V _m2,i can be obtained as

in, represents the unknown master robot system dynamics function.

3-7) Design the main controller according to (14) to ensure the stability of the main terminal system. The designed controller u _m,i is:

u _m,i ＝-μ _m2,i z _m2,i -z _m1,i -Φ _m,i -F _h,i (15)

Among them, μ _m2,i >0 represents the performance adjustment parameters of the main controller.

In the slave controller (15), Φ _m,i represents a fuzzy logic function for estimating η _m,i , which can be defined as:

Among them, θ _m,i represents the unknown dynamic parameters of the main robot system, Indicates the input quantity of the fuzzy logic function, Denotes the jth local fuzzy logic function.

3-8) Design the Lyapunov function V _m,i of the master system as:

Among them, γ _m,i >0 means the coefficient of Lyapunov function V _m,i , Indicates the estimation error of the fuzzy logic function, represents the best estimated parameter. .

The adaptive rate of θ _m,i is designed based on Lyapunov function V _m,i :

Among them, k _m,i >0 and Γ _m,i >0 represent the performance adjustment parameters of the adaptive rate.

4) Design the adaptive multilateral controller of the slave robot based on the fuzzy logic system, specifically:

4-1) Since the transmission of signals in the communication channel will inevitably generate communication delay, the position signal x _m,i (t) of the master robot is transmitted to the slave end through the communication channel to obtain the delayed position signal x _m,i ( tT(t)), the ideal trajectory generator designed from the robot is H _f (s)=1/(o _f s+1) ² , where, o _f represents the time constant, and the average position signal through the input time delay It can output the ideal tracking trajectory from the robot x _sd,o (t), Among them, l _{o, i} represent the relationship conversion between the grasping target and the end position of the robot, and T(t) is the communication delay of the system.

4-2) Define x _s1 = x _s,o , Then the nonlinear dynamic model (2) can be rewritten as:

4-3) Define the tracking error between the robot and the grasping target as:

where α _s1 represents the amount of virtual tracking from the robot.

4-4) The Lyapunov function V _s1 of the first subsystem in definition (19) is as follows:

By selecting the virtual tracking amount α _s1 as but

4-5) Define the Lyapunov V _s2 of the second subsystem in (19) as follows:

4-6) Based on (19) and (20), the derivative of z _s2 can be obtained as

Then, the derivative of V _s2 can be obtained as

in, Represents the unknown slave robot system dynamics function.

4-7) Design the slave controller according to (25) to ensure the stability of the slave terminal system. The designed controller u _s is:

u _s ＝-μ _s2 z _s2 -z _s1 -Φ _s +F _e (26)

Among them, μ _s2 >0 indicates that the parameter is adjusted from the performance of the controller.

In the slave controller (26), Φ _s represents a fuzzy logic function for estimating η _s , which can be defined as:

where θ _s represents the unknown slave robot system dynamic parameters, Indicates the input quantity of the fuzzy logic function, Denotes the jth local fuzzy logic function.

4-8) Design the Lyapunov function V _s of the slave system as:

Among them, γ _s > 0 means the coefficient of Lyapunov function V _s , Indicates the estimation error of the fuzzy logic function, represents the best estimated parameter.

The adaptive rate of θ _s is designed based on the Lyapunov function V _s :

Among them, k _s >0 and Γ _s >0 represent the performance adjustment parameters of the adaptive rate.

4-9) According to the slave controller (26), in order to obtain the control input u _s,i of each slave robot, design a multi-robot collaborative control algorithm as follows:

in, represents the partition coefficient, and W represents the weight coefficient of different job requirements, represents the internal force of each slave robot and the grasping target, and

Claims (6)

1. a teleoperation system self-adaptive multilateral control method based on fuzzy logic is characterized by comprising the following steps:

1) establishing a nonlinear dynamics model of a multilateral teleoperation system, which specifically comprises the following steps:

1-1) establishing a nonlinear dynamic model of a master robot, a slave robot and a working environment

Wherein q is_m,i,And q is_s,i,Representing the ith master-slave robot position, velocity and acceleration signals, x_m,i,Indicates the terminal position, x, of the ith main robot_s,o,Representing the position of the center of mass, M, of a grabbed object in a job task_m,iAnd M_sRepresenting the mass inertia matrix, C_m,iAnd C_sRepresenting a Coriolis force/centripetal force matrix, G_m,iAnd G_sRepresenting a gravity matrix, D_m,iAnd D_sRepresenting external interference and modeling error, u_m,iAnd u_sRepresenting a control input, F_h,iIndicates the operation force of the ith operator, F_eRepresents the environmental forces from the robot and the work task, i 1, 2.

The above system has the following characteristics:

①0<M_m,i≤δ_m0,iI，0<M_s≤δ_s0i, wherein, δ_m0,i,δ_s0>0 represents a scaling factor of the identity matrix I;

②andis an oblique symmetric matrix;

③ the partial kinetic equations in equations (1) and (2) can be written in the form of the following linear equations:

wherein, theta_m,iAnd theta_sShowing unknown parameters of models of the master robot and the slave robot, wherein zeta represents a fuzzy logic matrix;

1-2) establishing a nonlinear dynamic model of a slave-end working environment

Wherein, theta_eRepresenting an unknown non-power environmental parameter;

2) the method comprises the following steps of estimating a working environment and reconstructing a main end environment based on a fuzzy logic system:

2-1) writing the nonlinear dynamical model (3) of the slave-end working environment into the form of a radial basis function, then:

F_e＝ζ^T(x_ew)θ_e (4)

wherein x is_ewAn input quantity representing a fuzzy logic function;

2-2) definition ofFor optimal estimation of the parameters of the environment, Ω_eAnd Ω_e0Respectively represent x_ewAnd W_eThe online estimation of the slave-end working environment is realized through a fuzzy logic toolbox of MATLAB;

2-3) reconstructing the environmental force of the main end;

3) the self-adaptive multi-edge controller of the main robot is designed based on a fuzzy logic system, and specifically comprises the following steps:

3-1) designing an ideal track generator of the main robot to generate a passive ideal track signal x of the main robot_md,i；

3-2) definition of x_m1,i＝x_m,i，The non-linear dynamical model (1) of the ith master robot can be rewritten as:

3-3) defining the tracking error of the ith main robot as:

wherein alpha is_m1,iRepresenting a virtual tracking quantity of the master robot;

3-4) defining the Lyapunov function V of the first subsystem in (8)_m1,iThe following were used:

by selecting a virtual tracking quantity alpha_m1,iThen, then

3-5) defining the Lyapunov function V of the second subsystem in (8)_m2,iThe following were used:

3-6) based on (8) and (9), z can be obtained_m2,iIs a derivative of

Thus, V can be obtained_m2,iIs a derivative of

Wherein,representing unknown primary robot system dynamics functions;

3-7) designing a main controller according to the step (14) to ensure the stability of a main end subsystem, and designing a controller u_m,iComprises the following steps:

u_m,i＝-μ_m2,iz_m2,i-z_m1,i-Φ_m,i-F_h,i (15)

wherein, mu_m2,i>0 represents a master controller performance tuning parameter;

in the slave controller (15), phi_m,iRepresenting a method for estimating eta_m,iIs defined as:

wherein, theta_m,iRepresenting unknown primary robot system dynamics parameters,representing the input quantity of the fuzzy logic function,representing the jth local fuzzy logic function;

3-8) design of Lyapunov function V of master-end system_m,iComprises the following steps:

wherein, γ_m,i>0 represents the Lyapunov function V_m,iThe coefficient of (a) is determined,representing the estimation error of the fuzzy logic function,representing the optimal estimation parameters;

based on Lyapunov function V_m,iDesign theta_m,iThe self-adaptive rate is as follows:

wherein k is_m,i>0 and gamma_m,i>0 represents a performance adjustment parameter of the adaptation rate;

4) the self-adaptive multi-edge controller of the slave robot is designed based on a fuzzy logic system, and specifically comprises the following steps:

4-1) position signal x of the main robot due to communication delay inevitably generated by signal transmission in the communication channel_m,i(t) transmitting the time-delayed position signal x to the slave end via a communication channel_m,i(t-T (t)), designing an ideal trajectory generator of the slave robot as H_f(s)＝1/(o_fs+1)²Wherein o is_fAverage position signal representing time constant by input time delayCapable of outputting ideal slave robot tracking track x_sd,o(t),Wherein l_o,iRepresenting the relationship conversion between the grabbing target and the tail end position of the robot, wherein T (t) is the communication time delay of the system;

4-2) definition of x_s1＝x_s,o，The nonlinear dynamical model (2) can be rewritten as:

4-3) defining the tracking error between the robot and the grabbing target as:

wherein alpha is_s1Representing a virtual tracking amount from the robot;

4-4) defining the Lyapunov function V of the first subsystem in (19)_s1The following were used:

by selecting a virtual tracking quantity alpha_s1Then, then

4-5) definition of Lyapunov V of the second subsystem in (19)_s2The following were used:

4-6) based on (19) and (20), z can be obtained_s2Is a derivative of

Thus, V can be obtained_s2Is a derivative of

Wherein,representing unknown slave robot system dynamics functions;

4-7) designing the slave controller according to (25), ensuring the stability of the slave terminal system, andcontroller u_sComprises the following steps:

u_s＝-μ_s2z_s2-z_s1-Φ_s+F_e (26)

wherein, mu_s2>0 represents a slave controller performance adjustment parameter;

in the slave controller (26), phi_sRepresenting a method for estimating eta_sIs defined as:

wherein, theta_sRepresenting unknown kinetic parameters of the slave robotic system,representing the input quantity of the fuzzy logic function,representing the jth local fuzzy logic function;

4-8) designing Lyapunov function V of slave end system_sComprises the following steps:

wherein, γ_s>0 represents the Lyapunov function V_sThe coefficient of (a) is determined,representing the estimation error of the fuzzy logic function,representing the optimal estimation parameters;

based on Lyapunov function V_sDesign theta_sThe self-adaptive rate is as follows:

wherein k is_s>0 and gamma_s>0 represents a performance adjustment parameter of the adaptation rate;

4-9) according to the slave controller (26), for obtaining a control input u for each slave robot_s,iAnd designing a multi-robot cooperative control algorithm.

2. The method according to claim 1, wherein in step 2-3), due to the existence of the communication delay t (t), in order to avoid the transmission of the power signal between the communication channels from affecting the stability of the multi-edge teleoperation system, the estimated value of the non-power environment parameter is used to estimate the non-power environment parameterAnd transmitting the environment reconstruction force to the main end, so that the reconstruction environment force of the main end is:

wherein x is_emwRepresenting the input quantity of the fuzzy logic function.

3. The method for adaptive multilateral control of a fuzzy logic-based teleoperation system according to claim 1, wherein in step 3-1), the designed ideal trajectory generator of the main robot is as follows:

wherein, i is 1, 2., n,M_d,C_d,G_drepresents the optimized parameters of the trajectory generator.

4. The method as claimed in claim 1, wherein the virtual tracking amount α selected in step 3-4) is a virtual tracking amount_m1,iIs composed ofWherein, mu_m1,i>0 denotes an adjustment parameter of the virtual tracking amount.

5. The method according to claim 1, wherein the virtual tracking amount α selected in step 4-4) is a virtual tracking amount_s1Is composed ofWherein, mu_s1>0 denotes an adjustment parameter of the virtual tracking amount.

6. The method of claim 1, wherein the step 4-9) is performed according to the slave controller (26) to obtain the control input u of each slave robot_s,iDesigning a cooperative control algorithm of multiple robots as follows:

wherein,represents a distribution coefficient, andw represents a weighting factor for different job requirements,represents the internal force of each slave robot and the grasping object, and