{"title":"用于具有规定性能和输入量化的多机器人系统的预定义时间自构造神经网络 H∞ 协同控制","authors":"","doi":"10.1016/j.jfranklin.2024.107242","DOIUrl":null,"url":null,"abstract":"<div><p>In this paper, a predefined-time adaptive command filter <em>H<sub>∞</sub></em> controller with a self-adjusting performance function is proposed for multirobot systems. It guarantees that the tracking error meets the desired performance requirements and solves the vulnerability problem that arises in traditional prescribed performance. First, an asymmetric tan-type barrier Lyapunov function is introduced to establish asymmetric barrier constraints under input saturation and input quantization. Second, a prescribed performance with self-adjustment is introduced in the asymmetric tan-type barrier Lyapunov function, which limits the position error and changes the performance envelope based on its state. Third, a predefined-time adaptive command filter is introduced to address the \"complexity explosion\" issue and improve the convergence speed. Fourth, a predefined-time self-structuring neural network is introduced to fit the model uncertainty and time-varying disturbances, and a predefined-time <em>H<sub>∞</sub></em> control strategy is designed to address the strong sudden disturbances. Finally, some simulation examples are provided to test the validity of the above algorithms.</p></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":null,"pages":null},"PeriodicalIF":3.7000,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predefined-time self-structuring neural network H∞ cooperative control for multirobot systems with prescribed performance and input quantization\",\"authors\":\"\",\"doi\":\"10.1016/j.jfranklin.2024.107242\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>In this paper, a predefined-time adaptive command filter <em>H<sub>∞</sub></em> controller with a self-adjusting performance function is proposed for multirobot systems. It guarantees that the tracking error meets the desired performance requirements and solves the vulnerability problem that arises in traditional prescribed performance. First, an asymmetric tan-type barrier Lyapunov function is introduced to establish asymmetric barrier constraints under input saturation and input quantization. Second, a prescribed performance with self-adjustment is introduced in the asymmetric tan-type barrier Lyapunov function, which limits the position error and changes the performance envelope based on its state. Third, a predefined-time adaptive command filter is introduced to address the \\\"complexity explosion\\\" issue and improve the convergence speed. Fourth, a predefined-time self-structuring neural network is introduced to fit the model uncertainty and time-varying disturbances, and a predefined-time <em>H<sub>∞</sub></em> control strategy is designed to address the strong sudden disturbances. Finally, some simulation examples are provided to test the validity of the above algorithms.</p></div>\",\"PeriodicalId\":17283,\"journal\":{\"name\":\"Journal of The Franklin Institute-engineering and Applied Mathematics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2024-09-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of The Franklin Institute-engineering and Applied Mathematics\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S001600322400663X\",\"RegionNum\":3,\"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}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of The Franklin Institute-engineering and Applied Mathematics","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S001600322400663X","RegionNum":3,"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
摘要
本文为多机器人系统提出了一种具有自调整性能函数的预定义时间自适应指令滤波器 H∞ 控制器。它能保证跟踪误差满足所需的性能要求,并解决了传统规定性能中出现的脆弱性问题。首先,引入了非对称 tan 型壁垒 Lyapunov 函数,以建立输入饱和和输入量化条件下的非对称壁垒约束。其次,在非对称 tan 型壁垒 Lyapunov 函数中引入了具有自调整功能的规定性能,它可以限制位置误差,并根据其状态改变性能包络线。第三,引入了预定义时间自适应指令滤波器,以解决 "复杂性爆炸 "问题并提高收敛速度。第四,引入了预定义时间自构造神经网络来拟合模型的不确定性和时变扰动,并设计了预定义时间 H∞ 控制策略来解决强突发扰动问题。最后,提供了一些仿真实例来检验上述算法的有效性。
Predefined-time self-structuring neural network H∞ cooperative control for multirobot systems with prescribed performance and input quantization
In this paper, a predefined-time adaptive command filter H∞ controller with a self-adjusting performance function is proposed for multirobot systems. It guarantees that the tracking error meets the desired performance requirements and solves the vulnerability problem that arises in traditional prescribed performance. First, an asymmetric tan-type barrier Lyapunov function is introduced to establish asymmetric barrier constraints under input saturation and input quantization. Second, a prescribed performance with self-adjustment is introduced in the asymmetric tan-type barrier Lyapunov function, which limits the position error and changes the performance envelope based on its state. Third, a predefined-time adaptive command filter is introduced to address the "complexity explosion" issue and improve the convergence speed. Fourth, a predefined-time self-structuring neural network is introduced to fit the model uncertainty and time-varying disturbances, and a predefined-time H∞ control strategy is designed to address the strong sudden disturbances. Finally, some simulation examples are provided to test the validity of the above algorithms.
期刊介绍:
The Journal of The Franklin Institute has an established reputation for publishing high-quality papers in the field of engineering and applied mathematics. Its current focus is on control systems, complex networks and dynamic systems, signal processing and communications and their applications. All submitted papers are peer-reviewed. The Journal will publish original research papers and research review papers of substance. Papers and special focus issues are judged upon possible lasting value, which has been and continues to be the strength of the Journal of The Franklin Institute.