Adaptive neural network output feedback control for stochastic nonlinear systems with full state constraints. (June 2020) - Zhu, Qidan; Liu, Yongchao; Wen, Guoxing
Abstract: This paper presents an adaptive neural network output feedback control method for stochastic nonlinear systems with full state constraints. The barrier Lyapunov functions are used to conquer the effect of state constraints to system performance. The neural network state observer is established to estimate the unmeasured states. By using dynamic surface control technique, the "explosion of complexity" issue existing in the backstepping design is overcome. The proposed control scheme can guarantee that all signals of the system are bounded and the system output can follow the desired signal. Finally, two examples are given to verify the effectiveness of our control method. Highlights: The controller design for stochastic system with full state constraints is achieved. The neural network state observer is constructed to estimate the unmeasured state. Dynamic surface control scheme is used to simplify the structure of controller.