Adaptive Control For The Control Performance Improvement Of Nonlinear Uncertain Systems

With the growing development of industry,the controlled systems have become more and more complex.In the practical engineering application,there are lots of uncertainties,such as time-delays,hysteresis,dead-zone nonlinearity and so on.These uncertainties will increase the design difficulty of controller and affect the performance of system.What is more,undesirable oscillation and instability could be introduced and it may result in out-of-operation and unexpected losses.Therefore,it is necessary to study and address the control problems of uncertain nonlinear systems for ensuring the safety of practical engineering.In addition,some engineer projects make requests for control performance.Thus,advanced control algorithms are needed to not only guarantee the stability of the controlled system but also improve the steady-state and transient performance.Moreover,the scope of application of the algorithm could be expanded.Based on the dynamic surface technique,in this paper adaptive control algorithms are developed to improve the control performance of uncertain nonlinear systems.Finally,the feasibility and effectiveness of proposed control schemes are demonstrated.The main content is concluded as follows:(1)An asymptotic tracking control problem is addressed for a class of uncertain nonlinear systems preceded by unknown backlash-like hysteresis.By introducing novel nonlinear filters,a novel adaptive control algorithm via dynamic surface approach is proposed.Moreover,a compensating term is considered in the filters to compensate the boundary layer errors in the traditional dynamic surface technique,which can improve the tracking performance by adjusting design parameters.Meanwhile,a smooth adaptive controller is designed based on hysteresis and external disturbance.It is proved that the constructed controller can guarantee stability of the closed-loop system and achieve the convergence of the tracking error to origin theoretically.Finally,the feasibility of the proposed control method is shown by the theoretical analysis and simulation example(2)An adaptive neural dynamic surface control is presented for a class of multiple-input multiple-output interconnected nonlinear time-varying delays systems with unknown backlash-like hysteresis and external disturbances.Based on the dynamic surface technique,the problem of "explosion of complexity" existing in the conventional backstepping control is solved,which is able to eliminate the effect of backlash-like hysteresis and simplify the design of controller.The neural networks are introduced to approximate the unknown terms obtained in the design procedure.Then the maximum of weighted vector of the neural networks and the maximum of strength of the interactions are direct estimated,which can reduce the number of adaptive laws and ease the computational burden greatly.The exponential Lyapunov-Krasovskii functions are constructed to compensate the unknown time-varying delay uncertainties.Furthermore,owing to the funnel control strategy,the transient and steady performance of the tracking error are evolved within the funnel boundary function all the time.The feasibility of the proposed control method is shown by the theoretical analysis and simulation examples(3)An adaptive output feedback control is investigated for a class of single-input single-output nonlinear uncertain systems with unknown dead-zone.The high-gain K-observer is frist proposed to reconstruct the unknown system states so that the design can be carried on.And then the nonlinear part of dead-zone is regarded as "disturbance-like" term And the dynamic surface control is developed,which can simplify the design procedure Besides,the adaptive update law is only used in the first design step.Furthermore,by introducing the initialization technique,the L_? tracking error performance is guaranteed.It is proved that the tracking error can be rendered to an arbitrary small region and the effect of dead-zone can be eliminated by choosing proper design parameters.In the end,the feasibility of the proposed control method is shown by the theoretical analysis and simulation examples.