{"title":"On the Stability Verification of Adaptive Uncertain and Coupled Dynamical Discrete-Time Systems","authors":"Atahan Kurttisi, Islam A. Aly, K. Merve Dogan","doi":"10.1002/acs.3954","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>In the controlled uncertain coupled dynamical systems employing standard model reference adaptive methods, stability is guaranteed either under minimally coupled dynamics or negligible system uncertainties. Thus, significant uncertainties and coupled dynamics in an uncertain system can lead to instability. In recent papers, continuous-time adaptive architectures are derived to compensate for uncertainties and coupled dynamics together. However, discretizing such algorithms doesn't guarantee the stability of the system directly since it complicates the Lyapunov analysis in discrete-time. This makes it challenging to generalize successful continuous-time adaptive control results to discrete-time environments. Thus, this article includes the stability verification for adaptive uncertain and coupled dynamical discrete-time systems and shows the efficacy of the novel controller over the standard ones through two illustrative examples.</p>\n </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"39 3","pages":"517-528"},"PeriodicalIF":3.8000,"publicationDate":"2024-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Adaptive Control and Signal Processing","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/acs.3954","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
In the controlled uncertain coupled dynamical systems employing standard model reference adaptive methods, stability is guaranteed either under minimally coupled dynamics or negligible system uncertainties. Thus, significant uncertainties and coupled dynamics in an uncertain system can lead to instability. In recent papers, continuous-time adaptive architectures are derived to compensate for uncertainties and coupled dynamics together. However, discretizing such algorithms doesn't guarantee the stability of the system directly since it complicates the Lyapunov analysis in discrete-time. This makes it challenging to generalize successful continuous-time adaptive control results to discrete-time environments. Thus, this article includes the stability verification for adaptive uncertain and coupled dynamical discrete-time systems and shows the efficacy of the novel controller over the standard ones through two illustrative examples.
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.