CN110340894B - Teleoperation system self-adaptive multilateral control method based on fuzzy logic - Google Patents
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Abstract
The invention discloses a self-adaptive multilateral control method of a nonlinear teleoperation system based on fuzzy logic. The method estimates the non-power parameters of the nonlinear environment dynamics based on the fuzzy logic function, and transmits the non-power parameters back to the main end through a communication channel with time delay to reconstruct the environment force of the main end; aiming at various uncertainty problems existing in a master robot and a slave robot, the invention is based on a fuzzy logic system, and the parameters of a nonlinear function containing unknown system model information are updated on line by designing a self-adaptive rate; aiming at the position tracking performance of the system, the invention leads the slave robot to accurately track the track signal of the master robot by a nonlinear self-adaptive multilateral control method based on a fuzzy logic system when the communication delay exists in the system; aiming at the problem of distributing the working force during the cooperative operation among multiple robots, the invention realizes the distribution of the working force of multiple slave robots by designing a cooperative control algorithm of the multiple robots.
Description
Technical Field
The invention belongs to the field of teleoperation control, and particularly relates to a teleoperation system self-adaptive multilateral control method based on fuzzy logic, which can simultaneously ensure the stability and transparency of a nonlinear multilateral teleoperation system and the cooperative operation performance of multiple slave robots.
Background
With the continuous development of electromechanical technology, the research of robot systems is becoming a hot topic in the present stage, and teleoperation robot technology relying on human-machine interaction has been advanced in stages and has been widely applied in the fields of military, industry and medical treatment.
However, as the task of work develops in a complicated and delicate direction, a plurality of robots with multiple degrees of freedom in the work environment are required to perform cooperative work, and such robots often have nonlinearity and various uncertainties; furthermore, as the number of cooperating robots increases, signal communication among multiple robots may complicate signal transmission in communication channels with time delay, and even deteriorate stability and transparency of the teleoperation system.
Disclosure of Invention
The invention aims to provide a teleoperation system self-adaptive multilateral control method based on fuzzy logic, which aims to solve the technical problems of balance between stability and transparency, nonlinearity and various uncertainties of a master robot and a slave robot, cooperative operation of multiple robots and the like in the traditional multilateral teleoperation system.
In order to achieve the purpose, the technical scheme of the invention comprises the following specific contents:
a teleoperation system self-adaptive multilateral control method based on fuzzy logic comprises the following steps:
and (I) establishing a nonlinear dynamical model of the multilateral teleoperation system.
And (II) estimating the working environment and reconstructing the environment of the main end based on the fuzzy logic system.
And (III) designing the self-adaptive multi-edge controller of the main robot based on the fuzzy logic system.
And (IV) designing an adaptive multi-edge controller of the slave robot based on a fuzzy logic system.
Compared with the prior art, the invention has the following beneficial effects:
1. based on a fuzzy logic system, estimating a non-power parameter of nonlinear environment dynamics, transmitting the non-power parameter to the main end through a communication channel with time delay, and reconstructing the environment force of the main end, thereby avoiding the instability problem of a teleoperation system caused by the transmission of a power signal in the communication channel, and providing accurate force feedback information for an operator.
2. Based on a fuzzy logic system, parameters of a nonlinear function containing unknown system model information are updated on line by designing a self-adaptive rate, so that various uncertainty problems existing in a master robot and a slave robot are solved.
3. By the nonlinear adaptive multilateral control method based on the fuzzy logic system, when the system has communication delay, the slave robot can accurately track the track signal of the master robot, so that the position tracking performance of the system is improved.
4. By designing a multi-robot cooperative control algorithm, the working force distribution of the multiple slave robots is realized, so that the cooperative working performance of the multiple slave robots on the working tasks is improved.
5. By designing the Lyapunov function, the boundedness of all signals in the nonlinear multilateral teleoperation system is ensured, so that the global progressive stability of the system is guaranteed;
drawings
Fig. 1 is a block diagram of adaptive multilateral control of a nonlinear teleoperation system based on a fuzzy logic system according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
The invention will now be further described with reference to the following examples, with reference to figure 1:
the implementation technical scheme of the invention is as follows:
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 ism,i,
And q iss,i,
Representing the ith master-slave robot position, velocity and acceleration signals, xm,i,
Indicates the terminal position, x, of the ith main robots,o,
Representing the position of the center of mass, M, of a grabbed object in a job taskm,iAnd MsRepresenting the mass inertia matrix, Cm,iAnd CsRepresenting a Coriolis force/centripetal force matrix, Gm,iAnd GsRepresenting a gravity matrix, Dm,iAnd DsRepresenting external interference and modeling error, um,iAnd usRepresenting a control input, Fh,iIndicates the operation force of the ith operator, FeDenotes the environmental forces from the robot and the work task, i 1, 2.
The above system has the following characteristics:
①0<Mm,i≤m0,iI，0<Ms≤s0i, wherein,m0,i,s0>0 represents a scaling factor of the identity matrix I;
②
and
is 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, thetam,iAnd thetasModel unknown parameters of the master-slave robot are represented, and zeta represents a fuzzy logic matrix.
1-2) establishing a non-linear dynamic model of a working environment
Wherein, thetaeRepresenting 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 dynamic model (3) of the slave end working environment into the form of a radial basis function, then:
Fe＝ζT(xew)θe(4)
wherein x isewRepresents the input quantity of the fuzzy logic function, and xs,o,
And (4) correlating.
2-2) definition of
For optimal estimation of the parameters of the environment, ΩeAnd Ωe0Respectively represent xewAnd WeThe on-line estimation of the slave working environment can be realized through a fuzzy logic tool box of MATLAB.
2-3) due to the existence of communication time delay T (t), in order to avoid the influence of the transmission of power signals between communication channels on the stability of the multilateral teleoperation system, estimating values of non-power environment parameters
And transmitting the environment reconstruction force to the main end, so that the reconstruction environment force of the main end is:
wherein x isemwRepresents the input quantity of the fuzzy logic function, and xmd,i,
And (4) correlating.
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) design the ideal trajectory generator of the main robot as follows:
wherein, i is 1, 2., n,
Md,Cd,Gdrepresents the optimized parameters of the trajectory generator. By selecting proper optimization coefficients, (6) to (7) can generate a passive main robot ideal track signal xmd,i。
3-2) definition of xm1,i＝xm,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, αm1,iRepresenting the virtual trace amount 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 αm1,iIs composed of
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 obtainedm2,iIs a derivative of
Thus, V can be obtainedm2,iIs a derivative of
Wherein the content of the first and second substances,
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 um,iComprises the following steps:
um,i＝-μm2,izm2,i-zm1,i-Φm,i-Fh,i(15)
wherein, mum2,i>0 represents a master controller performance tuning parameter.
In the slave controller (15), phim,iRepresenting a method for estimating ηm,iThe fuzzy logic function of (a) may be defined as:
wherein, thetam,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 systemm,iComprises the following steps:
wherein, γm,i>0 represents the Lyapunov function Vm,iThe coefficient of (a) is determined,
representing the estimation error of the fuzzy logic function,
representing the optimal estimated parameters. .
Based on Lyapunov function Vm,iDesign thetam,iThe self-adaptive rate is as follows:
wherein k ism,i>0 andm,i>0 denotes 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 channelm,i(t) transmitting the time-delayed position signal x to the slave end via a communication channelm,i(t-T (t)), designing an ideal trajectory generator of the slave robot as Hf(s)＝1/(ofs+1)2Wherein o isfAverage position signal representing time constant by input time delay
Capable of outputting ideal slave robot tracking track xsd,o(t),
Wherein lo,iAnd T (t) is communication time delay of the system.
4-2) definition of xs1＝xs,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, αs1Representing the virtual tracking volume 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 αs1Is composed of
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 obtaineds2Is a derivative of
Thus, V can be obtaineds2Is a derivative of
Wherein the content of the first and second substances,
representing unknown slave robotic system dynamics functions.
4-7) designing a slave controller according to (25), ensuring the stability of a slave terminal system, and designing a controller usComprises the following steps:
us＝-μs2zs2-zs1-Φs+Fe(26)
wherein, mus2>0 represents a slave controller performance adjustment parameter.
In the slave controller (26), phisRepresenting a method for estimating ηsThe fuzzy logic function of (a) may be defined as:
wherein, thetasRepresenting 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 systemsComprises the following steps:
wherein, γs>0 represents the Lyapunov function VsThe coefficient of (a) is determined,
representing the estimation error of the fuzzy logic function,
representing the optimal estimated parameters.
Based on Lyapunov function VsDesign thetasThe self-adaptive rate is as follows:
wherein k iss>0 ands>0 denotes 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 robots,iDesigning a cooperative control algorithm of multiple robots as follows:
wherein the content of the first and second substances,
represents a distribution coefficient, and
w represents a weighting factor for different job requirements,
represents the internal force of each slave robot and the grasping object, and
Claims (4)
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 ism,i,
And q iss,i,
Representing the ith master-slave robot position, velocity and acceleration signals, xm,i,
Representing the tip position, tip velocity and tip acceleration, x, of the ith master robots,o,
Representing the position, velocity and acceleration of the center of mass, M, of the center of mass of the grabbed object in the job taskm,iAnd MsRepresenting the mass inertia matrix, Cm,iAnd CsRepresenting a Coriolis force/centripetal force matrix, Gm,iAnd GsRepresenting a gravity matrix, Dm,iAnd DsRepresenting external interference and modeling error, um,iAnd usRepresenting a control input, Fh,iIndicates the operation force of the ith operator, FeTo representFrom the environmental forces in the robot and the work task, i 1, 2.
The above system has the following characteristics:
①0<Mm,i≤m0,iI，0<Ms≤s0i, wherein,m0,i,s0>0 represents a scaling factor of the identity matrix I;
②
and
is 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, thetam,iAnd thetasShowing 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, thetaeRepresenting 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:
Fe＝ζT(xew)θe(4)
wherein x isewAn input quantity representing a fuzzy logic function;
2-2) definition of
For optimal estimation of the parameters of the environment, ΩeAnd Ωe0Respectively represent xewAnd thetaeThe 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 robotmd,i(ii) a The ideal trajectory generator of the designed main robot is as follows:
wherein, i is 1, 2., n,
Md,Cd,Gdan optimization parameter representing a trajectory generator;
3-2) definition of xm1,i＝xm,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, αm1,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 αm1,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 obtainedm2,iIs a derivative of
Thus, V can be obtainedm2,iIs a derivative of
Wherein the content of the first and second substances,
representing unknown main robot system dynamics function, mum1,i>0 represents an adjustment parameter of the virtual tracking amount;
3-7) designing a main controller according to (14) to ensure that the main terminal systemStability, designed controller um,iComprises the following steps:
um,i＝-μm2,izm2,i-zm1,i-Φm,i-Fh,i(15)
wherein, mum2,i>0 represents a master controller performance tuning parameter;
in the slave controller (15), phim,iRepresenting a method for estimating ηm,iIs defined as:
wherein, thetam,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 systemm,iComprises the following steps:
wherein, γm,i>0 represents the Lyapunov function Vm,iThe coefficient of (a) is determined,
representing the estimation error of the fuzzy logic function,
representing the optimal estimation parameters;
based on Lyapunov function Vm,iDesign thetam,iThe self-adaptive rate is as follows:
wherein k ism,i>0 andm,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 channelm,i(t) transmitting the time-delayed position signal x to the slave end via a communication channelm,i(t-T (t)), designing an ideal trajectory generator of the slave robot as Hf(s)＝1/(ofs+1)2Wherein o isfAverage position signal representing time constant by input time delay
Capable of outputting ideal slave robot tracking track xsd,o(t),
Wherein lo,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 xs1＝xs,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, αs1Representing virtual slave robotsA quasi-tracking quantity;
4-4) defining the Lyapunov function V of the first subsystem in (19)s1The following were used:
by selecting a virtual tracking quantity αs1Then, 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 obtaineds2Is a derivative of
Thus, V can be obtaineds2Is a derivative of
Wherein the content of the first and second substances,
representing unknown slave robot system dynamics functions;
4-7) designing a slave controller according to (25), ensuring the stability of a slave terminal system, and designing a controller usComprises the following steps:
us＝-μs2zs2-zs1-Φs+Fe(26)
wherein, mus2>0 represents a slave controller performance adjustment parameter;
in the slave controller (26)，ΦsRepresenting a method for estimating ηsIs defined as:
wherein, thetasRepresenting 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 systemsComprises the following steps:
wherein, γs>0 represents the Lyapunov function VsThe coefficient of (a) is determined,
representing the estimation error of the fuzzy logic function,
representing the optimal estimation parameters;
based on Lyapunov function VsDesign thetasThe self-adaptive rate is as follows:
wherein k iss>0 ands>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 robots,iDesign of multiple machinesA human cooperative control algorithm, said cooperative control algorithm being as follows:
wherein the content of the first and second substances,
represents a distribution coefficient, and
w represents a weighting factor for different job requirements,
represents the internal force of each slave robot and the grasping object, and
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 parameter
And transmitting the environment reconstruction force to the main end, so that the reconstruction environment force of the main end is:
wherein x isemwRepresenting the input quantity of the fuzzy logic function.
3. The method of claim 1, wherein the method comprises performing adaptive multilateral control on the teleoperation system based on fuzzy logicIn the step 3-4), the selected virtual tracking amount αm1,iIs composed of
4. The method for adaptive multilateral control of a fuzzy logic-based teleoperation system according to claim 1, wherein in step 4-4), the selected virtual tracking amount α is selecteds1Is composed of
Wherein, mus1>0 denotes an adjustment parameter of the virtual tracking amount.
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