Robust adaptive output sampled control of uncertain system with unknown time-varying control coefficient based on characteristic model
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- 英文论文题名：Robust adaptive output sampled control of uncertain system with unknown time-varying control coefficient based on characteristic model
- 论文作者：Yafei Chang ; Huang Huang
- 英文论文作者：Yafei Chang ; Huang Huang ; Beijing Institute of Control Engineering ; Science and Technology on Space Intelligent Control Laboratory
- 年：2017
- 作者机构：Beijing Institute of Control Engineering;Science and Technology on Space Intelligent Control Laboratory;
- 英文论文关键词：robust adaptive control ; time-varying ; characteristic model ; neural network estimator
- 会议召开时间：2017-07-26
- 会议录名称：第36届中国控制会议论文集（A）
- 英文会议录名称：Proceedings of the 36th Chinese Control Conference（A）
- 语种：英文
- 分类号：TP273
- 学会代码：KZLL
- 会议名称：第36届中国控制会议
- 会议地点：中国辽宁大连
- 主办单位：中国自动化学会控制理论专业委员会
- 学会名称：中国自动化学会控制理论专业委员会
- 页数：6
- 文件大小：1138k
- 原文格式：D
- 会议级别：全国
摘要
The robust adaptive control of uncertain system with unknown time-varying control coefficient is discussed. A novel output sampled control scheme based on characteristic model with neural network estimator is proposed. The design of the control scheme includes characteristic modeling, estimation for the characteristic parameters, and characteristic model-based adaptive control. The estimation method is designed based on discrete neural network aiming at solving estimation difficulties caused by fast time-variation of the parameter, and its convergence is also analyzed. Then a discrete-time sliding mode control law with a recursive form is proposed on the basis of the characteristic model, to enhance the control performance and system robustness to inevitable uncertainties. The stability analysis is conducted for the overall closed-loop system. Simulation results demonstrate the effectiveness of the proposed robust adaptive control scheme.
The robust adaptive control of uncertain system with unknown time-varying control coefficient is discussed. A novel output sampled control scheme based on characteristic model with neural network estimator is proposed. The design of the control scheme includes characteristic modeling, estimation for the characteristic parameters, and characteristic model-based adaptive control. The estimation method is designed based on discrete neural network aiming at solving estimation difficulties caused by fast time-variation of the parameter, and its convergence is also analyzed. Then a discrete-time sliding mode control law with a recursive form is proposed on the basis of the characteristic model, to enhance the control performance and system robustness to inevitable uncertainties. The stability analysis is conducted for the overall closed-loop system. Simulation results demonstrate the effectiveness of the proposed robust adaptive control scheme.
The robust adaptive control of uncertain system with unknown time-varying control coefficient is discussed. A novel output sampled control scheme based on characteristic model with neural network estimator is proposed. The design of the control scheme includes characteristic modeling, estimation for the characteristic parameters, and characteristic model-based adaptive control. The estimation method is designed based on discrete neural network aiming at solving estimation difficulties caused by fast time-variation of the parameter, and its convergence is also analyzed. Then a discrete-time sliding mode control law with a recursive form is proposed on the basis of the characteristic model, to enhance the control performance and system robustness to inevitable uncertainties. The stability analysis is conducted for the overall closed-loop system. Simulation results demonstrate the effectiveness of the proposed robust adaptive control scheme.
引文
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[8]Y Zhang,C Wen,YC Soh,Discrete-time robust backstepping adaptive control for nonlinear time-varying systems,IEEE Trans.on Automatic Control,45(9):1749-1755,2000.
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[11]H Wu,J Hu,Y Xie,B Meng,Z Lin.Theory and applications of the characteristic modeling:An introductory overview.International Journal of Intelligent Control and Systems,20:1-8,2015.
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[2]PA Ioannou,PV Kokotovic,Adaptive Systems with Reduced Models.Springer Berlin Heidelberg,1983,47.
[3]CE Rohrs,L Valavani,M Athans,G Stein,Robustness of continuous-time adaptive control algorithms in the presence of unmodeled dynamics,IEEE Trans.on Automatic Control,30(9):881-889,1983.
[4]Y Zhang,PA Ioannou,Adaptive control of linear time varying systems,in Proceedings of 35th Conference on Decision and Control,1996:837-842.
[5]AA Bobtsov AG Nagovitsina,Adaptive output control of linear time-varying systems,Adaptation&Learning in Control&Signal Processing,40(13):334-341.
[6]R Marino P Tomei,Robust Adaptive Regulation of Linear Time-Varying Systems,IEEE Trans.on Automatic Control,45(7):1301-1311,2000.
[7]SS Ge,J Wang,Robust Adaptive Tracking for Time-Varying Uncertain Nonlinear Systems with Unknown Control Coefficients,IEEE Trans.on Automatic Control,48(8):1463-1469,2003.
[8]Y Zhang,C Wen,YC Soh,Discrete-time robust backstepping adaptive control for nonlinear time-varying systems,IEEE Trans.on Automatic Control,45(9):1749-1755,2000.
[9]K Abidi,A robust discrete-time adaptive control approach for systems with almost periodic time-varying parameters,International Journal of Robust and Nonlinear Control,24(1):166–1785,2014.
[10]H Wu,J Hu,Y Xie.Characteristic Model Based Intelligent and Adaptive Control(in Chinese).Beijing:China Science and Technology Press,2009.
[11]H Wu,J Hu,Y Xie,B Meng,Z Lin.Theory and applications of the characteristic modeling:An introductory overview.International Journal of Intelligent Control and Systems,20:1-8,2015.
[12]F Lewis,A Yesildirek,K Liu.Multilayer neural-net robot controller with guaranteed tracking performance.IEEE Trans.on Neural Networks,7:388-399,1996.
[13]M Polycarpou,P Ioannou.Neural Networks as on-line approximators of nonlinear system in Proceedings of 31th Conference on Decision and Control,1:7-12,1992.