一大类系统自适应控制的内在鲁棒性

IF 3.9 4区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS
Mohamad T. Shahab, Daniel E. Miller
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引用次数: 0

摘要

最近的研究表明,如何对线性时变(LTI)工厂进行自适应控制,使初始条件的影响以指数形式衰减为零,并使输入输出行为具有卷积约束。这反过来又在一些特殊情况下证明了闭环系统的鲁棒性,即在存在少量参数时变和未模拟动态的情况下,上述两个特性都能保持不变。本文表明,对于具有正确特性的通用自适应控制器来说,这种鲁棒性是真实的:如果我们能证明指数稳定性和固定植物参数情况下的卷积约束,那么鲁棒性就是免费的。我们还将这些结果应用于文献中各种自适应控制问题的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Inherent robustness in the adaptive control of a large class of systems

Recently it has been shown how to carry out adaptive control for a linear time-invariant (LTI) plant so that the effect of the initial condition decays exponentially to zero and so that the input-output behavior enjoys a convolution bound. This, in turn, has been leveraged to prove, in several special cases, that the closed-loop system is robust in the sense that both of these properties are maintained in the presence of a small amount of parameter time-variation and unmodelled dynamics. This paper shows that this robustness property is true for a general adaptive controller with the right properties: if we are able to prove exponential stability and a convolution bound for the case of fixed plant parameters, then robustness comes for free. We also apply the results to solutions to various adaptive control problems in the literature.

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来源期刊
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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