This paper investigates a finite-time tracking problem for the uncertainty nonlinear systems in nonstrict-feedback form,in which the output signal is restricted in a region.Based on the barrier Lyapunov function and d...This paper investigates a finite-time tracking problem for the uncertainty nonlinear systems in nonstrict-feedback form,in which the output signal is restricted in a region.Based on the barrier Lyapunov function and dynamic surface control scheme,a novel adaptive neural controller is proposed by using the finite-time Lyapunov technology.Unlike the aforementioned literature on finite time tracking control,the violation of system output constraint is avoided by combining the barrier Lyapunov function method with finite-time theory.The structural characteristics of neural network is introduced to expand the adaptive neural finite-time backstepping method to the uncertainty nonlinear systems in the non-strict form.Correspondingly,the dynamic surface control is introduced to cope with the problem of“explosion of complexity”inherent in conventional backstepping scheme.It is shown that the designed controller can achieve finite-time tracking control and all the variables in the closed-loop system are bounded with output constraint guaranteed form stability analysis and simulation results.展开更多
基金supported by the National Key Research and Development Plan under Grant No.2017YFB1303503the National Natural Science Foundation of China under Grant No.61973179+1 种基金Taishan Scholar Special Project Fund under Grant No.TSQN20161026Qingdao key research and development special project under Grant No.21-1-2-6-nsh。
文摘This paper investigates a finite-time tracking problem for the uncertainty nonlinear systems in nonstrict-feedback form,in which the output signal is restricted in a region.Based on the barrier Lyapunov function and dynamic surface control scheme,a novel adaptive neural controller is proposed by using the finite-time Lyapunov technology.Unlike the aforementioned literature on finite time tracking control,the violation of system output constraint is avoided by combining the barrier Lyapunov function method with finite-time theory.The structural characteristics of neural network is introduced to expand the adaptive neural finite-time backstepping method to the uncertainty nonlinear systems in the non-strict form.Correspondingly,the dynamic surface control is introduced to cope with the problem of“explosion of complexity”inherent in conventional backstepping scheme.It is shown that the designed controller can achieve finite-time tracking control and all the variables in the closed-loop system are bounded with output constraint guaranteed form stability analysis and simulation results.
