Adaptive control of a class of stochastic nonlinear systems with full state constraints and input saturation using multi-dimensional Taylor network

In the paper, a new control algorithm to realize the tracking control for a class of stochastic nonlinear systems with both full state constraints and input saturation is put forward using multi-dimensional Taylor network (MTN) approach. By introducing a continuous function, the input saturation is converted into a linear model with bounded error. With the help of approximation properties of MTN, by proposing barrier Lyapunov functions (BLFs) and involving the idea of state-constrained control into backstepping technique, a simple and efficient control scheme is developed. The proposed control scheme can guarantee the system states fall in the given constrained bounds as well as keeping the resulting control system stable. The effectiveness of the proposed control method is demonstrated through two examples. © 2021 Chinese Automatic Control Society and John Wiley & Sons Australia, Ltd.