A Hybrid Robust Adaptive Sliding Mode Controller for partially modelled systems: Discrete-time Lyapunov stability analysis and application

控制理论(社会学) 稳健性(进化) 自适应控制 控制器(灌溉) 开环控制器 李雅普诺夫函数 计算机科学 滑模控制 控制工程 工程类 非线性系统 控制(管理) 人工智能 生物 基因 量子力学 物理 生物化学 化学 农学 闭环
作者
Deise Maria Cirolini Milbradt,Paulo Jefferson Dias de Oliveira Evald,Guilherme Vieira Hollweg,Hilton Abílio Gründling
出处
期刊:Nonlinear Analysis: Hybrid Systems [Elsevier BV]
卷期号:48: 101333-101333 被引量:20
标识
DOI:10.1016/j.nahs.2023.101333
摘要

This work presents a new discrete-time Hybrid Robust Adaptive Sliding Mode Controller, developed from the union of a Robust Model Reference Adaptive Controller, an Adaptive Sliding Mode Controller, and an Adaptive One Sample Ahead Preview Controller in an unique control structure. Robust Model Reference Adaptive Controller is an adequate direct adaptive control strategy to control partially known plants, but can present slow closed-loop response to ensure global stability. Therefore, an adaptive One Sample Ahead Preview controller is incorporated to accelerate transient regimes, once it tries to track reference signal in one sample. Furthermore, an adaptive Sliding Mode Controller is also merged in the controller structure to help controller performance in transient regime and it also improves relevantly the steady state response in a scenario of several unmodelled dynamics Stability analysis of this controller using Lyapunov criterion and its robustness proof are provided, considering the plant subjected to unmodelled dynamics, which provides controller design constraints. These proofs show the controller is globally stable, and the tracking error tends to a residual set in steady state, even in the presence of matched and unmatched dynamics. Numerical simulations of the Hybrid Robust Adaptive Sliding Mode Controller applied on an unstable nonminimum-phase plant are presented, where only part of the overall plant is take into consideration for controller design. Results corroborate the feasibility and robustness of the developed control strategy and the performance superiority when compared to an adaptive One Sample Ahead Preview controller, with a 75% tracking error reduction in a scenario of several unmodelled dynamics.
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