Adaptive Multi-dimensional Taylor Network Control for Stochastic Nonlinear Systems with Full State Constraints and Dead-zone Input

In this paper, the stochastic nonlinear systems with full state constraints and dead-zone input are considered in the same framework for the first time, and a new adaptive controller based on MTN is designed. Firstly, for the dead-zone input constraint problem, the characteristic function is introduced to avoid the nonlinearity of the dead-zone. Secondly, in the process of adaptive control design, MTN technology is employed to approximate the nonlinear structure generated in backstepping, and a simple controller is designed. By constructing the barrier Lyapunov functions (BLFs) to deal with the full state constraint problem, the proposed scheme ensures that all states of the closed-loop system do not exceed the constraint limit. Then, Lyapunov stability theory is used to analyze the stability. Finally, two simulation examples are given to illustrate the effectiveness and applicability of the proposed scheme.