{"title":"Neural Network-Based Optimal Fault-Tolerant Control for Interconnected Nonlinear Systems With Actuator Failures","authors":"Yujia Wang;Tong Wang;Chuang Li;Jiae Yang","doi":"10.1109/TETCI.2024.3358981","DOIUrl":null,"url":null,"abstract":"In this study, we present a decentralized optimal fault-tolerant control (FTC) framework using neural networks (NNs) for interconnected nonlinear systems. This approach addresses challenges arising from unknown drift functions, interconnections, and multiple faults, including lock-in-place, loss of effectiveness, and float. Specifically, we propose a novel NN-based approximation scheme that utilizes a learning algorithm and a differentiator to estimate unknown information within the system. Additionally, our developed optimal control framework, in contrast to the conventional adaptive dynamic programming (ADP) approach, eliminates the need to separately design the optimal tracking controller into two parts, i.e., the steady-state controller and the feedback controller. Moreover, in the simulation section, control parameters are designed using the presented search algorithm, which demonstrates advantages in terms of both time efficiency and convenience. Finally, comparative simulations are conducted to illustrate the effectiveness of the proposed decentralized optimal fault-tolerant tracking control strategy.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":5.3000,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Emerging Topics in Computational Intelligence","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10430194/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
In this study, we present a decentralized optimal fault-tolerant control (FTC) framework using neural networks (NNs) for interconnected nonlinear systems. This approach addresses challenges arising from unknown drift functions, interconnections, and multiple faults, including lock-in-place, loss of effectiveness, and float. Specifically, we propose a novel NN-based approximation scheme that utilizes a learning algorithm and a differentiator to estimate unknown information within the system. Additionally, our developed optimal control framework, in contrast to the conventional adaptive dynamic programming (ADP) approach, eliminates the need to separately design the optimal tracking controller into two parts, i.e., the steady-state controller and the feedback controller. Moreover, in the simulation section, control parameters are designed using the presented search algorithm, which demonstrates advantages in terms of both time efficiency and convenience. Finally, comparative simulations are conducted to illustrate the effectiveness of the proposed decentralized optimal fault-tolerant tracking control strategy.
在本研究中,我们针对互连非线性系统提出了一种使用神经网络(NN)的分散优化容错控制(FTC)框架。这种方法可以解决未知漂移函数、互连和多重故障(包括锁定、失效和浮动)带来的挑战。具体来说,我们提出了一种新颖的基于 NN 的近似方案,利用学习算法和微分器来估计系统内的未知信息。此外,与传统的自适应动态编程(ADP)方法相比,我们开发的最优控制框架无需将最优跟踪控制器分为稳态控制器和反馈控制器两部分进行设计。此外,在仿真部分,利用所介绍的搜索算法设计了控制参数,该算法在时间效率和便利性方面都具有优势。最后,通过对比仿真说明了所提出的分散式最优容错跟踪控制策略的有效性。
期刊介绍:
The IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI) publishes original articles on emerging aspects of computational intelligence, including theory, applications, and surveys.
TETCI is an electronics only publication. TETCI publishes six issues per year.
Authors are encouraged to submit manuscripts in any emerging topic in computational intelligence, especially nature-inspired computing topics not covered by other IEEE Computational Intelligence Society journals. A few such illustrative examples are glial cell networks, computational neuroscience, Brain Computer Interface, ambient intelligence, non-fuzzy computing with words, artificial life, cultural learning, artificial endocrine networks, social reasoning, artificial hormone networks, computational intelligence for the IoT and Smart-X technologies.