基于轨迹线性化控制的自适应神经网络控制

2006 6th World Congress on Intelligent Control and Automation Pub Date : 2006-10-23 DOI:10.1109/WCICA.2006.1712350

Yong Liu, Rui Huang, Jim Zhu

{"title":"基于轨迹线性化控制的自适应神经网络控制","authors":"Yong Liu, Rui Huang, Jim Zhu","doi":"10.1109/WCICA.2006.1712350","DOIUrl":null,"url":null,"abstract":"In this paper, an adaptive neural network nonlinear control method is developed based on trajectory linearization control (TLC). The adaptive neural network TLC control (ANNTLC) compensates the model nonlinear uncertainty adaptively, and improves controller performance. ANNTLC can also be used to simplify the TLC control design procedure by using a simplified model. A stable neural network learning rule is developed. The simulation result shows the feasibility of the proposed method","PeriodicalId":375135,"journal":{"name":"2006 6th World Congress on Intelligent Control and Automation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"17","resultStr":"{\"title\":\"Adaptive Neural Network Control Based on Trajectory Linearization Control\",\"authors\":\"Yong Liu, Rui Huang, Jim Zhu\",\"doi\":\"10.1109/WCICA.2006.1712350\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, an adaptive neural network nonlinear control method is developed based on trajectory linearization control (TLC). The adaptive neural network TLC control (ANNTLC) compensates the model nonlinear uncertainty adaptively, and improves controller performance. ANNTLC can also be used to simplify the TLC control design procedure by using a simplified model. A stable neural network learning rule is developed. The simulation result shows the feasibility of the proposed method\",\"PeriodicalId\":375135,\"journal\":{\"name\":\"2006 6th World Congress on Intelligent Control and Automation\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-10-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"17\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 6th World Congress on Intelligent Control and Automation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WCICA.2006.1712350\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 6th World Congress on Intelligent Control and Automation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WCICA.2006.1712350","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 17

摘要

提出了一种基于轨迹线性化控制(TLC)的自适应神经网络非线性控制方法。自适应神经网络TLC控制(ANNTLC)自适应补偿模型的非线性不确定性，提高了控制器的性能。ANNTLC也可以通过简化模型来简化TLC控制设计过程。提出了一种稳定的神经网络学习规则。仿真结果表明了该方法的可行性

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

Adaptive Neural Network Control Based on Trajectory Linearization Control

In this paper, an adaptive neural network nonlinear control method is developed based on trajectory linearization control (TLC). The adaptive neural network TLC control (ANNTLC) compensates the model nonlinear uncertainty adaptively, and improves controller performance. ANNTLC can also be used to simplify the TLC control design procedure by using a simplified model. A stable neural network learning rule is developed. The simulation result shows the feasibility of the proposed method

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

2006 6th World Congress on Intelligent Control and Automation

自引率

0.00%

发文量