Neural-networks-based adaptive asymptotic tracking control of MIMO stochastic non-strict-feedback nonlinear systems with full state constraints and unknown control gains

2022 ◽  
Author(s):  
Wei Su ◽  
Xudong Zhao ◽  
Ben Niu ◽  
Guangju Zhang ◽  
Huanqing Wang
Keyword(s):  
Neural Networks ◽  
Nonlinear Systems ◽  
Tracking Control ◽  
State Constraints ◽  
Full State ◽  
Asymptotic Tracking ◽  
Unknown Control ◽  
Full State Constraints ◽  
Strict Feedback

Tracking control for strict-feedback nonlinear systems with time-varying full state constraints

2018 ◽  
Vol 40 (14) ◽  
pp. 3964-3977 ◽  
Author(s):  
Chunxiao Wang ◽  
Yuqiang Wu ◽  
Zhongcai Zhang
Keyword(s):  
Nonlinear Systems ◽  
Tracking Control ◽  
State Constraints ◽  
Tracking Error ◽  
Input Saturation ◽  
Constrained Systems ◽  
Time Varying ◽  
Full State ◽  
Full State Constraints ◽  
Strict Feedback

This paper focuses on the tracking control problem for strict-feedback nonlinear systems subject to asymmetric time-varying full state constraints. Time-varying asymmetric barrier Lyapunov functions are employed to ensure time-varying constraint satisfaction. By allowing the barriers to vary with the desired trajectory in time, the initial condition requirements are relaxed. High-order coupling terms caused by backstepping are cancelled through a novel variable substitution for the first time. Besides the normal case, where the full knowledge of the system is available, we also handle scenarios of parametric uncertainties. Asymptotic tracking is achieved without violation of any constraints, and all signals in the closed-loop system are ultimately bounded. State-constrained systems with input saturation and bounded disturbances are also considered; the tracking error converges to a bounded set around zero. The performance of the asymmetric-barrier-Lyapunov-function-based control is illustrated through a numerical example.

Sign in / Sign up

Export Citation Format

Share Document