The Input-Mapping-Based Online Learning Sliding Mode Control Strategy With Low Computational Complexity
Abstract
This paper proposes a low-complexity input-mapping-based online-learning sliding-mode control strategy for uncertain multi-input multi-output systems. A sliding surface is formed online from a convex combination of predesigned surfaces, while an adaptive reaching law reduces chattering. Historical input-output data and their mapping to future dynamics are used to compensate for model mismatch without increasing the number of optimization variables with system dimension. The paper establishes closed-loop stability and demonstrates improved convergence and computational applicability on a MIMO system.
Yaru Yu
Phd
Aoyun MA
Assistant Professor
Dewei LI
Professor | Lab Leader
Currently serves as the Director of the Technical Committee on Predictive Control and Intelligent Decision of Chinese Association of Automation, Member of the Control Theory and Applications Education Working Group, and Editorial Member of Control Engineering Practice. He has led over 20 national-level projects, including Key Projects, General Projects and International Cooperation Projects from NSFC, Key Research and Development Programs from MOST. He has also undertaken more than 20 commissioned projects from key enterprises.
