Adaptive decentralized prescribed performance control for a class of large-scale stochastic nonlinear systems subject to input saturation and full state constraints

This paper focuses on an adaptive decentralized prescribed performance control problem for a class of large-scale stochastic nonlinear systems with asymmetric input saturation and full state constraints. Firstly, the obstacle of input saturation is overcome by introducing the Gaussian error functions. Secondly, the transient performance of the system output is realized by introducing the asymmetric error transfer functions. Thirdly, the full state constraints are considered in the backstepping control process, and the boundary of state constraints is ensured by constructing barrier Lyapunov functions. Then, the multidimensional Taylor network is employed to approximate the unknown nonlinearity, and an adaptive decentralized controller is designed. Finally, it is shown that the proposed control strategy can ensure that the closed-loop system is semi-global ultimately uniformly bounded in probability, and the tracking error of the system can be kept within an adjustable neighborhood of the origin. Two simulation examples are provided to illustrate the feasibility of the proposed control strategy.