CN108227497A - A kind of control method for considering system performance and being limited lower network remote control system - Google Patents

A kind of control method for considering system performance and being limited lower network remote control system Download PDF

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CN108227497A
CN108227497A CN201810027682.3A CN201810027682A CN108227497A CN 108227497 A CN108227497 A CN 108227497A CN 201810027682 A CN201810027682 A CN 201810027682A CN 108227497 A CN108227497 A CN 108227497A
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CN108227497B (en
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杨亚娜
李军朋
华长春
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Yanshan University
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    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance

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Abstract

Consider that system performance is limited the control method of lower network remote control system the invention discloses a kind of, specially：Master and slave position synchronous error bound variable is defined based on nonlinear networked remote control system model under delay of communication；Error variance and neural network design remote control system neural network control strategy based on definition；Neural network is provided using Lyapunov Equation and parameter adaptive adjusts rule, ensures to meet set performance requirement while master-slave synchronisation error approach converges on zero.The method of the present invention passes through the stable operation under remote control system limited performance, it ensure that the safety of system, solve the problems, such as that system convergence speed is slow under existing remote operating control strategy and precision is low, it overcomes system model not knowing and influence of the external interference to system performance, and improves the temporary of system, steady-state behaviour and interference free performance.Simultaneously this invention simplifies controller design process, it is made to be more conducive to practical application.

Description

A kind of control method for considering system performance and being limited lower network remote control system
Technical field
The present invention relates to networking remote control system control technology field, especially a kind of system performance is limited lower network The control strategy design problem of remote control system.
Background technology
Typical networking remote control system is mainly made of five parts, is respectively operator, positioned at host nearby Device people, network information transfer channel, positioned at the slave robot of distal end and from the external environment residing for robot.Its operating mode Substantially it can be described as：Operator manipulates local host device people, and the information such as the position of main robot, speed are passed through the biographies such as network Defeated medium is sent to from robot, the position according to the main robot received and velocity information from robot, in specific environment The behavior of Imitating main robot will feed back to main side operation so as to complete various work from the working condition of robot Person, person easy to operation make correct decision according to from the motion state of robot.At present, although the control of remote control system takes Good progress was obtained, but remote control system still faces huge challenge in practical applications.One side robot sheet is as complexity Nonlinear system there is strong nonlinearity characteristic, another aspect remote control system be applied to mostly the complicated mankind can not or it is uncomfortable The environment that splice grafting touches such as habitata, the scenes such as outer space detection and hazardous environment rescue.The strong nonlinearity of system and the external world are multiple Miscellaneous unknown working environment brings the uncertain and external interference of system.In addition, it is contemplated that remote control system is in practical application Present in many restrictions such as safe operating range, equipment physical limit and other performance requirements, when ignore these limitation item The working performance for leading to system decline is even resulted in system during part to be seriously damaged.It is otherwise noted that by right The performance of system carries out advance limitation such as to the overshoot of the transient state of system and steady-state behaviour such as system, convergence time, receipts It holds back precision etc. to be preset, the convergence rate and convergence precision of system can be improved to a certain extent.
Consider system performance limitation problem, obstacle Liapunov function method and tanh letter based on logarithmic function Several pre-determined characteristics control control methods has obtained extensive concern and has achieved a large amount of achievement in research.However both the above side The problem of calculating complexity is respectively present in method and easily causes singular value, system mould is not based on derived from adaptive high-gain thought The funnel control of type provides good solution.However typical funnel control can be only applied to it is linear and nonlinear S class systems, the i.e. relative degree of system are 1 or 2, and there are known positive high-frequency gains.
Invention content
Present invention aims at provide it is a kind of consider that system performance is limited the control method of lower network remote control system, with Solve existing controller existing deficiency in terms of control performance.
To achieve the above object, following technical scheme is employed：The method of the invention includes the following steps：
Step 1, master and slave position is defined based on nonlinear networked remote control system model under delay of communication and synchronizes mistake Poor bound variable；
Step 2, the error variance based on definition and neural network design remote control system neural network control Strategy；
Step 3, it provides neural network using Lyapunov Equation and parameter adaptive adjusts rule, ensure master-slave synchronisation Error approach meets set performance requirement while converging on zero.
Further, in step 1, the remote control system being made of two non-linear robot systems is considered, according to universal The robot lagrangian dynamics model used, can obtain the remote operating kinetic model based on joint space
Wherein, qm,qs∈RnFor joint displacements matrix；For joint velocity matrix；Mm(qm),Ms(qs)∈Rn×n Positive definite inertial matrix for system；For coriolis force and the vector of centrifugal force；Gm(qm), Gs(qs)∈RnFor gravity torque；For frictional force unknown existing for system and have out-of-bounds It interferes on boundary；Fh∈RnAnd Fe∈RnThe torque that the power and environment that respectively human operator applies apply；τm∈RnAnd τs∈RnFor control The control moment that device processed provides；
Consider that system model exists uncertain in practical application, therefore
Mm(qm)=Mmo(qm)+ΔMm(qm),
Ms(qs)=Mso(qs)+ΔMs(qs),
Gm(qm)=Gmo(qm)+ΔGm(qm),
Gs(qs)=Gso(qs)+ΔGs(qs)；
Mmo(qm), Mso(qs),Gmo(qm), Gso(qs) represent system nominal section be Known portions, and Δ Mm(qm), Δ Ms(qs),ΔGm(qm) and Δ Gs(qs) represent system Uncertain part；
Therefore remote control system (1) can be done by writing again
Wherein,
It is regarded as the uncertain of system entirety；
Choose xm1=qm,xs1=qsWithAbove system is organized into strict feedback systems
Define master and slave system position synchronous error variable
em=xm1-xs1(t-Ts(t)),es=xs1-xm1(t-Tm(t)) (4)
Wherein, Tm(t) main side is represented to the network information transfer time delay from end, Ts(t) network from end to main side is represented to believe Cease propagation delay time；
Define new variable
pm1=ξm10exp(-am1t)+ξm1∞,pm2=ξm20exp(-am2t)+ξm2∞ (5)
ps1=ξs10exp(-as1t)+ξs1∞,ps2=ξs20exp(-as2t)+ξs2∞ (6)
Wherein, ξm10,ξm1∞,ξm20,ξm2∞,ξs10,ξs1∞,ξs20,ξs2∞It is normal number, and meets such as lower inequality；ξm10 ＞ ξm1∞,ξm20＞ ξm2∞,ξs10＞ ξs1∞,ξs20＞ ξs2∞αm1,αm2,αs1,αs2Equally it is chosen for normal number；
Constant ξm1∞,ξm2∞,ξs1∞,ξs2∞Represent permitted maximum synchronous error, p when system is stablizedm1(t),pm2 (t),ps1(t),ps2(t) the minimum convergence rate allowed in rate of descent Representative errors convergence process；It can be seen that pass through definition Suitable pre-determined characteristics equation can meet different performance requirements；
And then based on master and slave system synchronization error variance, it is as follows to provide new limited error variance
Wherein,
Further, in step 2, attribute is approached according to neural network, for uncertain continuity equation f (X):Rq→Rp, In the presence of
Wherein,It is chosen for gaussian radial basis function equation i.e. cj,bjCenter and the width of j-th neuron, W are represented respectively*∈Rn×p For ideal neural network weight, ε (X) ∈ RpFor neural network evaluated error, εNRepresent the maximum of approximation timates error ε (X) Value；
Based on parameter adaptive method, lower design Adaptive neural network control is limited in master and slave system position synchronous error System strategy is as follows：
Wherein, km2,ks2For just diagonal constant matrices, WithFor estimating ideal neural network weightWith With It is mainly used for estimating neural network evaluated error upper bound ρ for auto-adaptive parameterm=εmNAnd ρs=εsN；
αm1=[αm11,αm12,...,αm1n]T；
αs1=[αs11,αs12,...,αs1n]TTo assist middle control variable, it is designed specifically to
Wherein, km1jAnd ks1jIt is chosen for normal number, zm1j,zs1j,emj,esj,xm2j(t-Ts(t)),xs2j(t-Ts(t)) respectively Representation vector zm1,zs1,em,es,xm2(t-Ts(t)),xs2(t-Ts(t)) j-th of variable, ψm1j,ψm2j,ψs1j,ψs2jDefinition It will be provided below,Respectively information propagation delay time Tm(t),Ts(t) derivative, j=1,2 ..., n.
Further, it in the step 3, provides neural network using Lyapunov Equation and parameter adaptive is adjusted Rule, ensure master-slave synchronisation error level off to zero while meet set performance requirement, design process is divided into two steps：
It is as follows to choose Lyapunov Equation for the first step
Its derivative is
Wherein, pi1j,pi2j,Representation vector p respectivelyi1,pi2,J-th of variable；
And then it can obtain
Wherein,
It can further obtain
Introduce middle control variable αm1jAnd αs1j(11), it can obtain
Second step chooses following new Lyapunov Equation
Wherein, WithRespectively Represent just diagonal constant matrices ΓmAnd ΓsIt is inverse；ηmAnd ηsIt is chosen for normal number；
Lyapunov Equation V derivations can be obtained
Controller (10) based on the master and slave system designed in step 2,It can be further converted to
And then design neural network and parameter adaptive adjusting rule are as follows
It can finally obtain
According to the definition (17) of Lyapunov Equation and equation (21) it is found that all signals in closed loop remote control system Bounded, and as t → ∞ variations per hours zm1,zs1,xm2-αm1,xs2-αs1Go to zero；And then according to zm1,zs1Definition (7) and (8) - p can be obtainedm2(t) ＜ em(t) ＜ pm1(t) ,-ps2(t) ＜ es(t) ＜ ps1(t)；Realize the performance limitation being pre-designed in satisfaction Under, master and slave system synchronization error is asymptotic to tend to zero.
Compared with prior art, the method for the present invention has the following advantages that：
1st, auxiliary middle control variable is approached using neural network in controller method design, so as to reduce to auxiliary Computation complexity caused by middle control variable derivation.
2nd, it by the stable operation under remote control system limited performance, ensure that the safety of system, solve existing System convergence speed is slow and the problem of precision is low under remote operating control strategy, overcomes system model and does not know and external interference Influence to system performance, and improve the temporary of system, steady-state behaviour and interference free performance.
3rd, under the controller, according to practical application request by choosing suitable variable pm1(t),pm2(t),ps1(t), ps2(t), different system limitation requirements can be met.In addition the restrictive condition of asymmetric time-varying is had chosen in the controller, is simplified Controller design process, more meets practical application request.
4th, this method is suitable for the sorts of systems such as aircraft with second order property, mechanical arm, wheeled robot etc., and very It is easily extended to High Order Nonlinear System.
Description of the drawings
Fig. 1 is the structure diagram of general remote control system；
Fig. 2 is the basic conception figure of constrained control.
Fig. 3 is the control principle block diagram of the present invention.
Specific embodiment
The present invention will be further described below in conjunction with the accompanying drawings：
As shown in Figs. 1-3, the method for the invention includes the following steps：
Step 1, master and slave position is defined based on nonlinear networked remote control system model under delay of communication and synchronizes mistake Poor bound variable；
Consider the remote control system being made of two non-linear robot systems, it is bright according to the robot glug generally used Day kinetic model, can obtain the remote operating kinetic model based on joint space
Wherein, qm,qs∈RnFor joint displacements matrix；For joint velocity matrix；Mm(qm),Ms(qs)∈Rn×n Positive definite inertial matrix for system；For coriolis force and the vector of centrifugal force；Gm(qm), Gs(qs)∈RnFor gravity torque；For frictional force unknown existing for system and have out-of-bounds It interferes on boundary；Fh∈RnAnd Fe∈RnThe torque that the power and environment that respectively human operator applies apply；τm∈RnAnd τs∈RnFor control The control moment that device processed provides；
The reality of the present invention is enhanced to the considerations of frictional force unknown existing for system and bounded external interference to be applied Property.
And then consider that system model is establishing the hypothesis carried out in the process and system in practical applications in practical application System model uncertain problem caused by existing abrasion etc., therefore
Mm(qm)=Mmo(qm)+ΔMm(qm),
Ms(qs)=Mso(qs)+ΔMs(qs),
Gm(qm)=Gmo(qm)+ΔGm(qm),
Gs(qs)=Gso(qs)+ΔGs(qs)；
Mmo(qm), Mso(qs),Gmo(qm), Gso(qs) represent system nominal section be Known portions, and Δ Mm(qm), Δ Ms(qs),ΔGm(qm) and Δ Gs(qs) represent system Uncertain part；
Therefore remote control system (1) can be done by writing again
Wherein,
It is regarded as the uncertain of system entirety；
For convenience below based on the design of the control strategy of recursion control thought, by choosing xm1=qm, xs1=qsWithAbove system is arranged as strict feedback systems
And further define master and slave system position synchronous error variable
em=xm1-xs1(t-Ts(t)),es=xs1-xm1(t-Tm(t)) (4)
Wherein, Tm(t) main side is represented to the network information transfer time delay from end, Ts(t) network from end to main side is represented to believe Cease propagation delay time；It is obvious that the asymmetric time-vary delay system for more meeting real network environment is considered here.
According to practical application demand, such as to system operatio range, the requirement to system convergence speed, precision, overshoot, New bound variable is designed, by ensureing the boundedness of bound variable, so as to meet the performance requirement of systemic presupposition.It defines newly Limited function variable
pm1=ξm10exp(-am1t)+ξm1∞,pm2=ξm20exp(-am2t)+ξm2∞ (5)
ps1=ξs10exp(-as1t)+ξs1∞,ps2=ξs20exp(-as2t)+ξs2∞ (6)
Wherein, ξm10,ξm1∞,ξm20,ξm2∞,ξs10,ξs1∞,ξs20,ξs2∞It is normal number, and meets such as lower inequality；ξm10 ＞ ξm1∞,ξm20＞ ξm2∞,ξs10＞ ξs1∞,ξs20＞ ξs2∞αm1,αm2,αs1,αs2Equally it is chosen for normal number；
Constant ξm1∞,ξm2∞,ξs1∞,ξs2∞Represent permitted maximum synchronous error, p when system is stablizedm1(t),pm2 (t),ps1(t),ps2(t) the minimum convergence rate allowed in rate of descent Representative errors convergence process；It can be seen that pass through definition Suitable pre-determined characteristics equation can meet different performance requirements；
And then based on master and slave system synchronization error variance, it is as follows to provide new limited error variance
Wherein,
In order to describe brief introduction, the time suffix in variable is omitted in the case of no time delay.
Step 2, the error variance based on definition and neural network design remote control system neural network control Strategy；
Attribute is approached according to neural network, for uncertain continuity equation f (X):Rq→Rp, exist
Wherein,It is chosen for gaussian radial basis function equation i.e. cj,bjCenter and the width of j-th neuron, W are represented respectively*∈Rn×p For ideal neural network weight, ε (X) ∈ RpFor neural network evaluated error, εNRepresent the maximum of approximation timates error ε (X) Value；
Parameter adaptive method is based further on, lower design adaptive neural network is limited in master and slave system position synchronous error Network control strategy is as follows：
Wherein, km2,ks2For just diagonal constant matrices, WithFor estimating ideal neural network weightWith With It is mainly used for estimating neural network evaluated error upper bound ρ for auto-adaptive parameterm=εmNAnd ρs=εsN；
αm1=[αm11,αm12,...,αm1n]T；
αs1=[αs11,αs12,...,αs1n]TTo assist middle control variable, it is designed specifically to
Wherein, km1jAnd ks1jIt is chosen for normal number, zm1j,zs1j,emj,esj,xm2j(t-Ts(t)),xs2j(t-Ts(t)) respectively Representation vector zm1,zs1,em,es,xm2(t-Ts(t)),xs2(t-Ts(t)) j-th of variable, ψm1j,ψm2j,ψs1j,ψs2jDefinition It will be provided below,Respectively information propagation delay time Tm(t),Ts(t) derivative, j=1,2 ..., n.
Step 3, it provides neural network using Lyapunov Equation and parameter adaptive adjusts rule, ensure master-slave synchronisation Error approach meets set performance requirement while converging on zero.
Specific design process is divided into two steps：
It is as follows to choose Lyapunov Equation for the first step
Its derivative is
Wherein, pi1j,pi2j,Representation vector p respectivelyi1,pi2,J-th of variable；
And then it can obtain
Wherein,
It can further obtain
Introduce middle control variable αm1jAnd αs1j(11), it can obtain
Second step chooses following new Lyapunov Equation
Wherein, WithRespectively Represent just diagonal constant matrices ΓmAnd ΓsIt is inverse；ηmAnd ηsIt is chosen for normal number；
Lyapunov Equation V derivations can be obtained
Controller (10) based on the master and slave system designed in step 2,It can be further converted to
And then design neural network and parameter adaptive adjusting rule are as follows
It can finally obtain
According to the definition (17) of Lyapunov Equation and equation (21) it is found that all signals in closed loop remote control system Bounded, and as t → ∞ variations per hours zm1,zs1,xm2-αm1,xs2-αs1Go to zero；And then according to zm1,zs1Definition (7) and (8) - p can be obtainedm2(t) ＜ em(t) ＜ pm1(t) ,-ps2(t) ＜ es(t) ＜ ps1(t)；Realize the performance limitation being pre-designed in satisfaction Under, master and slave system synchronization error is asymptotic to tend to zero.
Embodiment described above is only that the preferred embodiment of the present invention is described, not to the model of the present invention It encloses and is defined, under the premise of design spirit of the present invention is not departed from, those of ordinary skill in the art are to the technical side of the present invention The various modifications and improvement that case is made should all be fallen into the protection domain that claims of the present invention determines.

Claims (4)

  1. A kind of 1. control method for considering system performance and being limited lower network remote control system, which is characterized in that the method packet Include following steps：
    Step 1, under delay of communication based on nonlinear networked remote control system model define master and slave position synchronous error by Limit variable；
    Step 2, the error variance based on definition and neural network design remote control system neural network control strategy；
    Step 3, it provides neural network using Lyapunov Equation and parameter adaptive adjusts rule, ensure master-slave synchronisation error Approach meets set performance requirement while converging on zero.
  2. 2. a kind of control method for considering system performance and being limited lower network remote control system according to claim 1, It is characterized in that：In step 1, the remote control system being made of two non-linear robot systems is considered, according to the machine generally used Device people's lagrangian dynamics model, can obtain the remote operating kinetic model based on joint space
    Wherein, qm,qs∈RnFor joint displacements matrix；For joint velocity matrix；Mm(qm),Ms(qs)∈Rn×nTo be The positive definite inertial matrix of system；For coriolis force and the vector of centrifugal force；Gm(qm),Gs(qs) ∈RnFor gravity torque；It is done for frictional force unknown existing for system and the bounded external world It disturbs；Fh∈RnAnd Fe∈RnThe torque that the power and environment that respectively human operator applies apply；τm∈RnAnd τs∈RnDevice in order to control The control moment of offer；
    Consider that system model exists uncertain in practical application, therefore
    Mm(qm)=Mmo(qm)+ΔMm(qm),
    Ms(qs)=Mso(qs)+ΔMs(qs),
    Gm(qm)=Gmo(qm)+ΔGm(qm),
    Gs(qs)=Gso(qs)+ΔGs(qs)；
    Mmo(qm), Mso(qs),Gmo(qm), Gso(qs) represent that the nominal section of system is i.e. known Part, and Δ Mm(qm), Δ Ms(qs),ΔGm(qm) and Δ Gs(qs) represent the not true of system Determine part；
    Therefore remote control system (1) can be done by writing again
    Wherein,
    It is regarded as the uncertain of system entirety；
    Choose xm1=qm,xs1=qsWithAbove system is organized into strict feedback systems
    Define master and slave system position synchronous error variable
    em=xm1-xs1(t-Ts(t)),es=xs1-xm1(t-Tm(t)) (4)
    Wherein, Tm(t) main side is represented to the network information transfer time delay from end, Ts(t) network information from end to main side is represented to pass Defeated time delay；
    Define new variable
    pm1=ξm10exp(-am1t)+ξm1∞,pm2=ξm20exp(-am2t)+ξm2∞ (5)
    ps1=ξs10exp(-as1t)+ξs1∞,ps2=ξs20exp(-as2t)+ξs2∞ (6)
    Wherein, ξm10,ξm1∞,ξm20,ξm2∞,ξs10,ξs1∞,ξs20,ξs2∞It is normal number, and meets such as lower inequality；ξm10＞ ξm1∞,ξm20＞ ξm2∞,ξs10＞ ξs1∞,ξs20＞ ξs2∞αm1,αm2,αs1,αs2Equally it is chosen for normal number；
    Constant ξm1∞,ξm2∞,ξs1∞,ξs2∞The permitted maximum synchronous error when system is stablized is represented,
    pm1(t),pm2(t),ps1(t),ps2(t) the minimum convergence rate allowed in rate of descent Representative errors convergence process；It can be with Different performance requirements can be met by defining suitable pre-determined characteristics equation by finding out；
    And then based on master and slave system synchronization error variance, it is as follows to provide new limited error variance
    Wherein,
  3. 3. a kind of control method for considering system performance and being limited lower network remote control system according to claim 1, It is characterized in that：In step 2, attribute is approached according to neural network, for uncertain continuity equation f (X):Rq→Rp, exist
    Wherein,It is chosen for gaussian radial basis function equation i.e.
    cj,bjCenter and the width of j-th neuron, W are represented respectively*∈ Rn×pFor ideal neural network weight, ε (X) ∈ RpFor neural network evaluated error, εNRepresent approximation timates error ε (X) most Big value；
    Based on parameter adaptive method, lower design neural network control plan is limited in master and slave system position synchronous error It is slightly as follows：
    Wherein, km2,ks2For just diagonal constant matrices,
    WithFor estimating ideal neural network weightWith WithIt is mainly used for estimating neural network evaluated error upper bound ρ for auto-adaptive parameterm=εmNAnd ρs=εsN；
    αm1=[αm11,αm12,...,αm1n]T；
    αs1=[αs11,αs12,...,αs1n]TTo assist middle control variable, it is designed specifically to
    Wherein, km1jAnd ks1jIt is chosen for normal number, zm1j,zs1j,emj,esj,xm2j(t-Ts(t)),xs2j(t-Ts(t)) it represents respectively Vectorial zm1,zs1,em,es,xm2(t-Ts(t)),xs2(t-Ts(t)) j-th of variable,When respectively information is transmitted Prolong Tm(t),Ts(t) derivative, j=1,2 ..., n.
  4. 4. a kind of control method for considering system performance and being limited lower network remote control system according to claim 1, It is characterized in that：In the step 3, provide neural network using Lyapunov Equation and parameter adaptive adjusts rule, ensure Master-slave synchronisation error level off to zero while meet set performance requirement, design process is divided into two steps：
    It is as follows to choose Lyapunov Equation for the first step
    Its derivative is
    Wherein, pi1j,pi2j,Representation vector p respectivelyi1,pi2,J-th of variable；
    And then it can obtain
    Wherein,
    It can further obtain
    Introduce middle control variable αm1jAnd αs1j(11), it can obtain
    Second step chooses following new Lyapunov Equation
    Wherein,WithIt represents respectively Just diagonal constant matrices ΓmAnd ΓsIt is inverse；ηmAnd ηsIt is chosen for normal number；
    Lyapunov Equation V derivations can be obtained
    Controller (10) based on the master and slave system designed in step 2,It can be further converted to
    And then design neural network and parameter adaptive adjusting rule are as follows
    It can finally obtain
    According to the definition (17) of Lyapunov Equation and equation (21) it is found that there is all signals in closed loop remote control system Boundary, and as t → ∞ variations per hours zm1,zs1,xm2-αm1,xs2-αs1Go to zero；And then according to zm1,zs1Definition (7) and (8) can Obtain-pm2(t) ＜ em(t) ＜ pm1(t) ,-ps2(t) ＜ es(t) ＜ ps1(t)；It realizes in the case where meeting the performance being pre-designed limitation, Master and slave system synchronization error is asymptotic to tend to zero.
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