Time-varying asymmetrical BLFs based adaptive finite-time neural control of nonlinear systems with full state constraints

This paper concentrates on asymmetric barrier Lyapunov functions ( ABLFs ) based on finite-time adaptive neural network ( NN ) control methods for a class of nonlinear strict feedback systems with time-varying full state constraints. During the process of backstepping recursion, the approximation properties of NNs are exploited to address the problem of unknown internal dynamics. The ABLFs are constructed to make sure that the time-varying asymmetrical full state constraints are always satisfied. According to the Lyapunov stability and finite-time stability theory, it is proven that all the signals in the closed-loop systems are uniformly ultimately bounded ( UUB ) and the system output is driven to track the desired signal as quickly as possible near the origin. In the meantime, in the scope of finite-time, all states are guaranteed to stay in the pre-given range. Finally, a simulation example is proposed to verify the feasibility of the developed finite time control algorithm.

[1]  Shaocheng Tong,et al.  Adaptive Controller Design-Based ABLF for a Class of Nonlinear Time-Varying State Constraint Systems , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[2]  Peng Shi,et al.  Finite-time command filtered backstepping control for a class of nonlinear systems , 2018, Autom..

[3]  Shaocheng Tong,et al.  Barrier Lyapunov Functions-based adaptive control for a class of nonlinear pure-feedback systems with full state constraints , 2016, Autom..

[4]  Keng Peng Tee,et al.  Control of nonlinear systems with time-varying output constraints , 2009, 2009 IEEE International Conference on Control and Automation.

[5]  Shaocheng Tong,et al.  Fuzzy-Based Multierror Constraint Control for Switched Nonlinear Systems and Its Applications , 2019, IEEE Transactions on Fuzzy Systems.

[6]  Guang Li,et al.  Adaptive fuzzy control for a marine vessel with time‐varying constraints , 2018, IET Control Theory & Applications.

[7]  Tieshan Li,et al.  Finite-Time Formation Control of Under-Actuated Ships Using Nonlinear Sliding Mode Control , 2018, IEEE Transactions on Cybernetics.

[8]  L. Weiss,et al.  ON THE STABILITY OF SYSTEMS DEFINED OVER A FINITE TIME INTERVAL. , 1965, Proceedings of the National Academy of Sciences of the United States of America.

[9]  Shaocheng Tong,et al.  Barrier Lyapunov Function-Based Adaptive Fuzzy FTC for Switched Systems and Its Applications to Resistance–Inductance–Capacitance Circuit System , 2020, IEEE Transactions on Cybernetics.

[10]  P.V. Kokotovic,et al.  The joy of feedback: nonlinear and adaptive , 1992, IEEE Control Systems.

[11]  Jing Zhang,et al.  Finite-Time Adaptive Fuzzy Control for Nonlinear Systems With Full State Constraints , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[12]  Lei Liu,et al.  Actuator Failure Compensation-Based Adaptive Control of Active Suspension Systems With Prescribed Performance , 2020, IEEE Transactions on Industrial Electronics.

[13]  Xiaoping Liu,et al.  Robust Adaptive Neural Tracking Control for a Class of Stochastic Nonlinear Interconnected Systems , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[14]  Yongduan Song,et al.  Neuroadaptive Control of Strict Feedback Systems With Full-State Constraints and Unknown Actuation Characteristics: An Inexpensive Solution , 2018, IEEE Transactions on Cybernetics.

[15]  Yuqiang Wu,et al.  RETRACTED ARTICLE: Finite-time tracking control for strict-feedback nonlinear systems with time-varying output constraints , 2018, Int. J. Syst. Sci..

[16]  Yugang Niu,et al.  Finite-time sliding mode control synthesis under explicit output constraint , 2016, Autom..

[17]  Peter Xiaoping Liu,et al.  Adaptive Fuzzy Finite-Time Control of Nonlinear Systems With Actuator Faults , 2020, IEEE Transactions on Cybernetics.

[18]  Sara Scarritt.  Nonlinear Model Reference Adaptive Control for Satellite Attitude Tracking , 2008 .

[19]  Da-Peng Li,et al.  Adaptive Neural Network Control for Uncertain Time-Varying State Constrained Robotics Systems , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[20]  Shaocheng Tong,et al.  A Combined Backstepping and Stochastic Small-Gain Approach to Robust Adaptive Fuzzy Output Feedback Control , 2013, IEEE Transactions on Fuzzy Systems.

[21]  Shaocheng Tong,et al.  Approximation-Based Adaptive Neural Tracking Control of Nonlinear MIMO Unknown Time-Varying Delay Systems With Full State Constraints , 2017, IEEE Transactions on Cybernetics.

[22]  Lin Zhao,et al.  Barrier Lyapunov functions-based command filtered output feedback control for full-state constrained nonlinear systems , 2019, Autom..

[23]  Dong Eun Seo.  Noncertainty equivalent nonlinear adaptive control and its applications to mechanical and aerospace systems , 2007 .

[24]  C. L. Philip Chen,et al.  Adaptive Fuzzy Control for a Class of Stochastic Pure-Feedback Nonlinear Systems With Unknown Hysteresis , 2016, IEEE Transactions on Fuzzy Systems.

[25]  Fumin Zhang,et al.  Adaptive nonlinear boundary control of a flexible link robot arm , 1999, IEEE Trans. Robotics Autom..

[26]  Yongduan Song,et al.  Tracking control of MIMO nonlinear systems under full state constraints: A Single-parameter adaptation approach free from feasibility conditions , 2019, Autom..

[27]  Hongyi Li,et al.  Adaptive finite-time tracking control of full state constrained nonlinear systems with dead-zone , 2019, Autom..

[28]  Yongduan Song,et al.  Prescribed Performance Control of Uncertain Euler–Lagrange Systems Subject to Full-State Constraints , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[29]  Shuzhi Sam Ge,et al.  Direct Adaptive Neural Control for a Class of Uncertain Nonaffine Nonlinear Systems Based on Disturbance Observer , 2013, IEEE Transactions on Cybernetics.

[30]  Lei Liu,et al.  Adaptive Neural Network Control for a DC Motor System with Dead-Zone , 2012, Nonlinear Dynamics.

[31]  Renquan Lu,et al.  Finite-Time State Estimation for Coupled Markovian Neural Networks With Sensor Nonlinearities , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[32]  Youxian Sun,et al.  Adaptive Neural Control of Nonlinear MIMO Systems With Time-Varying Output Constraints , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[33]  Yongduan Song,et al.  Removing the Feasibility Conditions Imposed on Tracking Control Designs for State-Constrained Strict-Feedback Systems , 2019, IEEE Transactions on Automatic Control.

[34]  Li Tang,et al.  Time-varying Barrier Lyapunov Function Based Adaptive Neural Controller Design for Nonlinear Pure-feedback Systems with Unknown Hysteresis , 2019, International Journal of Control, Automation and Systems.

[35]  Xiaoping Liu,et al.  Approximation-Based Adaptive Fuzzy Tracking Control for a Class of Nonstrict-Feedback Stochastic Nonlinear Time-Delay Systems , 2015, IEEE Transactions on Fuzzy Systems.

[36]  Francis Eng Hock Tay,et al.  Barrier Lyapunov Functions for the control of output-constrained nonlinear systems , 2009, Autom..

[37]  Francesco Amato,et al.  Input-output finite-time stability of linear systems , 2009, 2009 17th Mediterranean Conference on Control and Automation.

[38]  Shaocheng Tong,et al.  Integral Barrier Lyapunov function-based adaptive control for switched nonlinear systems , 2020, Science China Information Sciences.

[39]  Xu Jin,et al.  Adaptive finite‐time fault‐tolerant tracking control for a class of MIMO nonlinear systems with output constraints , 2017 .