Neural network adaptive finite-time control of stochastic nonlinear systems with full state constraints

2020 ◽  
Author(s):  
Qidan Zhu ◽  
Yongchao Liu
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Finite Time ◽  
State Constraints ◽  
Stochastic Nonlinear Systems ◽  
Time Control ◽  
Full State ◽  
Full State Constraints

scholarly journals Finite-Time Tracking Control for Nonstrict-Feedback State-Delayed Nonlinear Systems with Full-State Constraints and Unmodeled Dynamics

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Yangang Yao ◽  
Jieqing Tan ◽  
Jian Wu
Keyword(s):  
Nonlinear Systems ◽  
Finite Time ◽  
Tracking Control ◽  
State Constraints ◽  
Unmodeled Dynamics ◽  
Time Varying ◽  
Cubic Form ◽  
Time Control ◽  
Full State ◽  
Full State Constraints

The problem of finite-time tracking control is discussed for a class of uncertain nonstrict-feedback time-varying state delay nonlinear systems with full-state constraints and unmodeled dynamics. Different from traditional finite-control methods, a C 1 smooth finite-time adaptive control framework is introduced by employing a smooth switch between the fractional and cubic form state feedback, so that the desired fast finite-time control performance can be guaranteed. By constructing appropriate Lyapunov-Krasovskii functionals, the uncertain terms produced by time-varying state delays are compensated for and unmodeled dynamics is coped with by introducing a dynamical signal. In order to avoid the inherent problem of “complexity of explosion” in the backstepping-design process, the DSC technology with a novel nonlinear filter is introduced to simplify the structure of the controller. Furthermore, the results show that all the internal error signals are driven to converge into small regions in a finite time, and the full-state constraints are not violated. Simulation results verify the effectiveness of the proposed method.

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