具有全局滑动行为的数据驱动自适应控制：一种动态参数化方法

IF 3.9 4区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

International Journal of Adaptive Control and Signal Processing Pub Date : 2025-02-08 DOI:10.1002/acs.3983

Mingxuan Sun, Shengxiang Zou, Wei Li

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引用次数: 0

摘要

在这篇文章中，数据驱动的自适应控制问题是针对一类一般的非线性系统。为所承担的系统提供了输入/输出差分表示，并应用动态参数化来处理所涉及的非线性。本文以有限差分原理为基础，通过对时变参数的估计和预测误差的补偿，提出了一种保证全局滑动行为的数据驱动自适应控制设计方法。通过用功率速率规则对闭环系统进行评估，给出了系统的滑动行为特征。给出了绝对吸引层、稳态误差带和误差动力学单调递减区的推导。通过数值仿真验证了误差行为，验证了所提控制方案的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Data-Driven Adaptive Control With Global Sliding Behavior: A Dynamic Parametrization Approach

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Data-Driven Adaptive Control With Global Sliding Behavior: A Dynamic Parametrization Approach

In this article, the problem of data-driven adaptive control is addressed for a general class of non-linear systems. Input/output difference representation is provided for the system undertaken, with which dynamic parametrization is applied for handling the involved non-linearity. By underlining finite difference principle, this article proposes a design method of data-driven adaptive control with guaranteed global sliding behavior, through estimation for the time-varying parameters and compensation for the prediction error. The sliding behavior characterization is presented through an assessment of the resultant closed-loop system by power-rate rule. The derivations for the absolute attracting layer, steady-state error band, and monotone decreasing region of the error dynamics are presented in detail. Numerical simulation is carried out to examine the error behavior and validate the effectiveness of the proposed control scheme.

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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.