一种基于遗传算法的迭代学习控制系统优化方法

Proceedings of the Third International Workshop on Robot Motion and Control, 2002. RoMoCo '02. Pub Date : 2002-11-09 DOI:10.1109/ROMOCO.2002.1177143

V. Hatzikos, D. Owens

{"title":"一种基于遗传算法的迭代学习控制系统优化方法","authors":"V. Hatzikos, D. Owens","doi":"10.1109/ROMOCO.2002.1177143","DOIUrl":null,"url":null,"abstract":"In this paper genetic algorithms are proposed as a method to implement optimality based iterative learning control algorithms. The strength of the proposed method is that it can cope with nonlinearities and hard constraints in the problem definition whereas most of the existing algorithms would fail. Simulation examples show that this approach results in fast convergence for linear plants.","PeriodicalId":213750,"journal":{"name":"Proceedings of the Third International Workshop on Robot Motion and Control, 2002. RoMoCo '02.","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"A genetic algorithm based optimisation method for iterative learning control systems\",\"authors\":\"V. Hatzikos, D. Owens\",\"doi\":\"10.1109/ROMOCO.2002.1177143\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper genetic algorithms are proposed as a method to implement optimality based iterative learning control algorithms. The strength of the proposed method is that it can cope with nonlinearities and hard constraints in the problem definition whereas most of the existing algorithms would fail. Simulation examples show that this approach results in fast convergence for linear plants.\",\"PeriodicalId\":213750,\"journal\":{\"name\":\"Proceedings of the Third International Workshop on Robot Motion and Control, 2002. RoMoCo '02.\",\"volume\":\"12 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-11-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Third International Workshop on Robot Motion and Control, 2002. RoMoCo '02.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ROMOCO.2002.1177143\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Third International Workshop on Robot Motion and Control, 2002. RoMoCo '02.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ROMOCO.2002.1177143","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 9

摘要

本文提出了遗传算法作为实现基于最优性的迭代学习控制算法的方法。该方法的优点在于能够处理问题定义中的非线性和硬约束，而现有的大多数算法都无法解决问题。仿真实例表明，该方法对线性对象具有较快的收敛速度。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

A genetic algorithm based optimisation method for iterative learning control systems

In this paper genetic algorithms are proposed as a method to implement optimality based iterative learning control algorithms. The strength of the proposed method is that it can cope with nonlinearities and hard constraints in the problem definition whereas most of the existing algorithms would fail. Simulation examples show that this approach results in fast convergence for linear plants.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Proceedings of the Third International Workshop on Robot Motion and Control, 2002. RoMoCo '02.

自引率

0.00%

发文量