Retrospective-cost-based model reference adaptive control of nonminimum-phase systems

IF 3.9 4区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS International Journal of Adaptive Control and Signal Processing Pub Date : 2024-04-16 DOI:10.1002/acs.3810

Nima Mohseni, Dennis S. Bernstein

引用次数: 0

Abstract

This paper presents a novel approach to model reference adaptive control inspired by the adaptive pole-placement controller (APPC) of Elliot and based on retrospective cost optimization. Retrospective cost model reference adaptive control (RC-MRAC) is applicable to nonminimum-phase (NMP) systems assuming that the NMP zeros are known. Under this assumption, the advantage of RC-MRAC is a reduced need for persistency. The present paper compares APPC and RC-MRAC under various levels of persistency in the command for minimum-phase and NMP systems. It is shown numerically that the model-following performance of RC-MRAC is less sensitive to the persistency of the command compared to APPC at the cost of knowledge of the NMP zeros. RC-MRAC is also shown to be applicable for disturbance rejection under unknown harmonic disturbances.

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基于回溯成本的非最小相位系统模型参考自适应控制

本文受埃利奥特自适应极点置放控制器（APPC）的启发，提出了一种基于追溯成本优化的新型模型参考自适应控制方法。回溯成本模型参考自适应控制（RC-MRAC）适用于非最小相位（NMP）系统，前提是已知 NMP 的零点。在这一假设条件下，RC-MRAC 的优势在于减少了对持续性的需求。本文比较了 APPC 和 RC-MRAC 在最小相位和 NMP 系统指令中不同程度的持续性。数值结果表明，与 APPC 相比，RC-MRAC 的模型跟随性能对指令持续性的敏感性更低，但代价是需要了解 NMP 的零点。RC-MRAC 还适用于未知谐波干扰下的干扰抑制。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Adaptive Control and Signal Processing 工程技术-工程：电子与电气

CiteScore

5.30

自引率

16.10%

发文量

163

审稿时长

5 months

期刊介绍： The International Journal of Adaptive Control and Signal Processing is concerned with the design, synthesis and application of estimators or controllers where adaptive features are needed to cope with uncertainties.Papers on signal processing should also have some relevance to adaptive systems. The journal focus is on model based control design approaches rather than heuristic or rule based control design methods. All papers will be expected to include significant novel material. Both the theory and application of adaptive systems and system identification are areas of interest. Papers on applications can include problems in the implementation of algorithms for real time signal processing and control. The stability, convergence, robustness and numerical aspects of adaptive algorithms are also suitable topics. The related subjects of controller tuning, filtering, networks and switching theory are also of interest. Principal areas to be addressed include: Auto-Tuning, Self-Tuning and Model Reference Adaptive Controllers Nonlinear, Robust and Intelligent Adaptive Controllers Linear and Nonlinear Multivariable System Identification and Estimation Identification of Linear Parameter Varying, Distributed and Hybrid Systems Multiple Model Adaptive Control Adaptive Signal processing Theory and Algorithms Adaptation in Multi-Agent Systems Condition Monitoring Systems Fault Detection and Isolation Methods Fault Detection and Isolation Methods Fault-Tolerant Control (system supervision and diagnosis) Learning Systems and Adaptive Modelling Real Time Algorithms for Adaptive Signal Processing and Control Adaptive Signal Processing and Control Applications Adaptive Cloud Architectures and Networking Adaptive Mechanisms for Internet of Things Adaptive Sliding Mode Control.

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