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

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. (C) 2020 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.