| 不确定非线性系统全局渐近自适应神经网络控制 |
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| 引用本文： | 罗隆,罗飞,许玉格.不确定非线性系统全局渐近自适应神经网络控制[J].控制理论与应用,2014,31(9):1268-1273. |
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| 作者姓名： | 罗隆 罗飞 许玉格 |
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| 作者单位： | 1. 华南理工大学 自动化科学与工程学院自主系统与网络控制教育部重点实验室,广东 广州510640;广州铁路职业技术学院,广东广州510430
2. 华南理工大学 自动化科学与工程学院自主系统与网络控制教育部重点实验室,广东 广州,510640 |
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| 基金项目： | 中央高校基本科研业务费专项重点资助项目(2014ZZ0037); 广州市珠江科技新星项目(2011J2200084); 广州市“节能减排(水处理)自动 化技术应用研究创新学术团队”项目(穗教科2009[11]号); 惠州市产学研结合项目(2011C010002004). |
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| 摘 要： | 针对一类控制增益为一般函数形式的不确定仿射非线性系统,提出一种能够确保全局渐近稳定的自适应神经控制(adaptiveneural control,ANC)方法.为了保证神经网络逼近的适用性,设计一种可变控增益的比例微分(proportionaldifferential,PD)控制器以全局镇定被控对象.利用状态变换解决由未知控制增益函数导致的控制奇异问题.提出一种连续的自适应鲁棒控制项实现闭环系统的渐近跟踪.与现有的全局渐近跟踪ANC方法相比较,本文方法不仅简化了PD增益的选择,而且减轻了控制输入的颤振问题.仿真结果表明了本文方法的有效性.
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| 关 键 词： | 自适应控制 渐近跟踪 神经网络 全局稳定 非线性系统 |
| 收稿时间： | 2013/12/23 0:00:00 |
| 修稿时间： | 4/7/2014 12:00:00 AM |
Global asymptotic adaptive neural control of uncertain nonlinear systems |
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| LUO Long,LUO Fei and XU Yu-ge.Global asymptotic adaptive neural control of uncertain nonlinear systems[J].Control Theory & Applications,2014,31(9):1268-1273. |
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| Authors: | LUO Long LUO Fei and XU Yu-ge |
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| Affiliation: | Key Laboratory of Autonomous Systems and Network Control, Ministry of Education, College of Automation Science and Technology, South China University of Technology; Guangzhou Institute of Railway Technology,Key Laboratory of Autonomous Systems and Network Control, Ministry of Education, College of Automation Science and Technology, South China University of Technology,Key Laboratory of Autonomous Systems and Network Control, Ministry of Education, College of Automation Science and Technology, South China University of Technology |
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| Abstract: | We present an adaptive neural control (ANC) strategy that guarantees globally asymptotic tracking for a class of uncertain nonlinear systems with function-type control gains. A proportion differential (PD) control term with variable gain is employed to globally stabilize the plant so that neural network approximation is applicable. A state transformation is applied to solve the control singularity problem resulting from the unknown control gain function. A robust control term is developed to achieve asymptotic tracking of the closed-loop system. Compared with previous global asymptotic tracking ANC approaches, the proposed approach not only simplifies the selection of PD gain, but also relaxes chattering at the control input. Simulation results have demonstrated the effectiveness of the proposed approach. |
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| Keywords: | adaptive control asymptotic tracking neural network global stability nonlinear system |
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