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Pattern-moving-based Robust Model-free Adaptive Control for a Class of Nonlinear Systems with Disturbance and Data Dropout

Xiangquan Li, Zhengguang Xu*, Cheng Han, and Jiarui Cui
International Journal of Control, Automation, and Systems, vol. 20, no. 11, pp.3501-3511, 2022

Abstract : The ability to deal with the system disturbance and/or data dropout is often referred to as the robustness of model-free or data-driven control theory. This paper addresses a novel pattern-moving-based partial-form dynamic linearization intermittent model-free adaptive control scheme for a class of nonlinear discrete-time systems with disturbance and random measurement data dropout. Furthermore, the bounded convergence of the tracking error of the closed-loop system is proved by the statistical approach with contraction mapping principle. The basic idea is to consider the pattern-moving-based partial-form dynamic linearization model-free adaptive control method under the condition of missing data which may be caused by network failure, failing sensor or actuator. The designed scheme mainly includes an improved intermittent tracking control law, an intermittent classification-metric bias estimation algorithm and a modified intermittent pseudo gradient vector estimation algorithm. The bounded convergence and effectiveness of the proposed scheme are demonstrated by both the rigorous mathematical inference and two numerical examples.

Keyword : "Data dropout, model-free adaptive control (MFAC), partial-form dynamic linearization (PFDL), pattern moving, pseudo gradient vector. "

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