Adaptive Multi-Dimensional Taylor Network Tracking Control for a Class of Stochastic Nonlinear Systems With Unknown Input Dead-Zone

In this paper, a multi-dimensional Taylor network (MTN) tracking control scheme is proposed for a class of stochastic nonlinear systems with unknown input dead-zone. The MTNs are used to approximate the nonlinearities, and then, an adaptive MTN controller is constructed via a backstepping technique. It is proved that the design MTN controller ensures that all signals of the closed-loop system remain bounded in probability, and the tracking error eventually converges to an arbitrarily small neighborhood around the origin in the sense of a mean quartic value. Finally, two numerical examples and one practical example are given to demonstrate the effectiveness of the proposed design method.