Adaptive neural output-feedback tracking control for a class of stochastic nonlinear systems with output constraint and unknown control coefficients

This article addresses adaptive neural output-feedback tracking control problem for a class of stochastic nonlinear systems with output constraint and unknown control coefficients. A state observer is designed to estimate the unmeasurable system states. Nussbaum gain technique is employed to deal with the problem of unknown control coefficients. Simultaneously, aiming at the output constraint requirements of stochastic systems, the tan-type barrier Lyapunov function (BLF) structure is proposed. The constraint requirement on the system output trace error is not violated during the operation in the sense of probability. An adaptive output-feedback controller is designed to ensure that the output tracking error converges to a small region of the origin. The control scheme ensures that all signals in the closed-loop systems are semi-global uniformly ultimately bounded. Results of simulation are presented to prove the effectiveness of the theoretical analysis.