Nussbaum gain adaptive neural control for stochastic pure-feedback nonlinear time-delay systems with full-state constraints

2018 ◽  
Vol 292 ◽  
pp. 130-141 ◽  
Author(s):  
Wenjie Si ◽  
Xunde Dong ◽  
Feifei Yang
Keyword(s):  
Time Delay ◽  
Neural Control ◽  
State Constraints ◽  
Delay Systems ◽  
Time Delay Systems ◽  
Adaptive Neural Control ◽  
Full State ◽  
Full State Constraints

Command filtered adaptive neural tracking control of uncertain nonlinear time-delay systems with asymmetric time-varying full state constraints and actuator saturation

2020 ◽  
pp. 095965182097526
Author(s):  
Yuxiang Wu ◽  
Tian Xu ◽  
Haoran Fang
Keyword(s):  
Time Delay ◽  
Tracking Control ◽  
State Constraints ◽  
Actuator Saturation ◽  
Small Neighborhood ◽  
Delay Systems ◽  
Time Varying ◽  
Time Delay Systems ◽  
Full State ◽  
Full State Constraints

This article investigates the command filtered adaptive neural tracking control for uncertain nonlinear time-delay systems subject to asymmetric time-varying full state constraints and actuator saturation. To stabilize such a class of systems, the radial basis function neural networks and the backstepping technique are used to structure an adaptive controller. The command filter is utilized to overcome the complexity explosion problem in backstepping. By employing the Lyapunov–Krasovskii functionals, the effect of time-delay is eliminated. The asymmetric time-varying barrier Lyapunov functions are designed to ensure full state constraint satisfaction. Moreover, the hyperbolic tangent function and an instrumental variable are introduced to deal with actuator saturation. All signals in the closed-loop system are proved to be bounded and the tracking error converges to a small neighborhood of the origin. Finally, two examples are provided to illustrate the effectiveness of the proposed method.

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