Adaptive tracking control for a class of stochastic non‐linear systems with input saturation constraint using multi‐dimensional Taylor network

The randomness and input saturation increase computational complexity and impede the tracking performance of stochastic non-linear systems, therefore, it is necessary to build a simple but effective controller. Based on this, a multi-dimensional Taylor network (MTN) is first applied to a class of stochastic non-linear systems with input saturation constraint in this study. Aiming to solve the tracking control problem, a novel MTN-based controller is proposed via backstepping, and the proposed control scheme has some advantages such as simple structure, good real-time performance and easy realisation. With the help of the hyperbolic tangent function approximating the symmetric saturation non-linearity, an adaptive MTN tracking control scheme is constructively designed via backstepping technique. The simulation results are given to illustrate the validity and accuracy of the model of the proposed approach.

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