Adaptive neural network finite-time tracking control of full state constrained pure feedback stochastic nonlinear systems

Abstract In this article, the finite-time tracking control scheme is established for uncertain pure feedback stochastic nonlinear systems with state constraints. To cope with the state constraints, the barrier Lyapunov functions are introduced to make all states maintain in the predefined regions. The mean value theorem is exploited to transform the pure feedback structure into affine form. Then, the adaptive neural network finite-time controller is recursively established by utilizing backstepping technique. The proposed neural network finite-time controller can guarantee that all internal signals of the closed-loop systems are bounded and the tracking error converges to a neighborhood of the origin in a finite time. Finally, simulation examples are provided to illustrate the validity of the designed control method.

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

[2]  Bing Chen,et al.  Adaptive neural tracking control for a class of stochastic nonlinear systems , 2014 .

[3]  Wenjie Si,et al.  Adaptive neural control for MIMO stochastic nonlinear pure-feedback systems with input saturation and full-state constraints , 2018, Neurocomputing.

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

[5]  Shuzhi Sam Ge,et al.  Adaptive neural network control of nonlinear systems with unknown time delays , 2003, IEEE Trans. Autom. Control..

[6]  Hamed Habibi,et al.  Backstepping Nussbaum gain dynamic surface control for a class of input and state constrained systems with actuator faults , 2019, Inf. Sci..

[7]  Guo-Xing Wen,et al.  Simplified optimized control using reinforcement learning algorithm for a class of stochastic nonlinear systems , 2020, Inf. Sci..

[8]  Chong Lin,et al.  Finite-Time Adaptive Fuzzy Tracking Control Design for Nonlinear Systems , 2018, IEEE Transactions on Fuzzy Systems.

[9]  Yongchao Liu,et al.  Adaptive neural network control for time-varying state constrained nonlinear stochastic systems with input saturation , 2020, Inf. Sci..

[10]  Feifei Yang,et al.  Nussbaum gain adaptive neural control for stochastic pure-feedback nonlinear time-delay systems with full-state constraints , 2018, Neurocomputing.

[11]  Shuzhi Sam Ge,et al.  Direct adaptive NN control of a class of nonlinear systems , 2002, IEEE Trans. Neural Networks.

[12]  Shaocheng Tong,et al.  Adaptive control-based Barrier Lyapunov Functions for a class of stochastic nonlinear systems with full state constraints , 2018, Autom..

[13]  Peng Shi,et al.  Sliding Mode Control of Singular Stochastic Markov Jump Systems , 2017, IEEE Transactions on Automatic Control.

[14]  Bing Chen,et al.  Finite time control of switched stochastic nonlinear systems , 2019, Fuzzy Sets Syst..

[15]  Wei He,et al.  Adaptive Neural Network Control of an Uncertain Robot With Full-State Constraints , 2016, IEEE Transactions on Cybernetics.

[16]  Min Tan,et al.  Adaptive Control of a Class of Nonlinear Pure-Feedback Systems Using Fuzzy Backstepping Approach , 2008, IEEE Transactions on Fuzzy Systems.

[17]  Shuzhi Sam Ge,et al.  Unified iterative learning control for flexible structures with input constraints , 2018, Autom..

[18]  Wei He,et al.  Adaptive Fuzzy Neural Network Control for a Constrained Robot Using Impedance Learning , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[19]  Yun Zhang,et al.  Observer-based finite time control of nonlinear systems with actuator failures , 2019, Inf. Sci..

[20]  Wei Lin,et al.  Non-Lipschitz continuous stabilizers for nonlinear systems with uncontrollable unstable linearization , 2001 .

[21]  Changyin Sun,et al.  Fuzzy Tracking Control for a Class of Uncertain MIMO Nonlinear Systems With State Constraints , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[22]  Xue-Jun Xie,et al.  Adaptive backstepping controller design using stochastic small-gain theorem , 2007, Autom..

[23]  Bing Chen,et al.  Adaptive neural control for a general class of pure-feedback stochastic nonlinear systems , 2014, Neurocomputing.

[24]  Dennis S. Bernstein,et al.  Finite-Time Stability of Continuous Autonomous Systems , 2000, SIAM J. Control. Optim..

[25]  Changyin Sun,et al.  Adaptive Neural Network Control of a Marine Vessel With Constraints Using the Asymmetric Barrier Lyapunov Function , 2017, IEEE Transactions on Cybernetics.

[26]  Xin Hu,et al.  Adaptive disturbance observer‐based control for stochastic systems with multiple heterogeneous disturbances , 2019, International Journal of Robust and Nonlinear Control.

[27]  Hang Su,et al.  Adaptive Fuzzy Control of Stochastic Nonlinear Systems With Fuzzy Dead Zones and Unmodeled Dynamics , 2020, IEEE Transactions on Cybernetics.

[28]  Shaocheng Tong,et al.  Neural Networks-Based Adaptive Finite-Time Fault-Tolerant Control for a Class of Strict-Feedback Switched Nonlinear Systems , 2019, IEEE Transactions on Cybernetics.

[29]  Shengyuan Xu,et al.  Adaptive finite-time control for stochastic nonlinear systems subject to unknown covariance noise , 2018, J. Frankl. Inst..

[30]  Chong Lin,et al.  Direct adaptive neural tracking control for a class of stochastic pure‐feedback nonlinear systems with unknown dead‐zone , 2013 .

[31]  Fuad E. Alsaadi,et al.  Adaptive Neural State-Feedback Tracking Control of Stochastic Nonlinear Switched Systems: An Average Dwell-Time Method , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[32]  Miroslav Krstic,et al.  Stabilization of stochastic nonlinear systems driven by noise of unknown covariance , 2001, IEEE Trans. Autom. Control..

[33]  Hamid Reza Karimi,et al.  Adaptive NN Dynamic Surface Controller Design for Nonlinear Pure-Feedback Switched Systems With Time-Delays and Quantized Input , 2018, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[34]  Shengyuan Xu,et al.  Output-Feedback Control for Stochastic Nonlinear Systems Subject to Input Saturation and Time-Varying Delay , 2019, IEEE Transactions on Automatic Control.