Open AccessAdaptive tracking control for a class of stochastic non‐linear systems with input saturation constraint using multi‐dimensional Taylor network
Author(s) -
Han YuQun
Publication year - 2020
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2019.0934
Subject(s) - backstepping , control theory (sociology) , randomness , computer science , hyperbolic function , controller (irrigation) , nonlinear system , mathematics , mathematical optimization , adaptive control , control (management) , artificial intelligence , mathematical analysis , physics , quantum mechanics , statistics , agronomy , biology
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.