控制理论与应用2016，Vol.33Issue(3)：387-397,11.DOI:10.7641/CTA.2016.50103
基于自组织小波小脑模型关节控制器的不确定非线性系统鲁棒自适应终端滑模控制
A robust adaptive integral terminal sliding mode control for uncertain nonlinear systems using self-organizing wavelet cerebella model articulation controller
摘要
Abstract
We propose a robust adaptive integral terminal sliding mode control method using self-organizing wavelet cerebella model articulation controller (SOWCMAC) for a class of uncertain nonlinear systems with modeling error, parameter uncertainty and external disturbances to achieve the desired tracking performance and strong robustness. Firstly, we make use of the advantages of cerebella model articulation controller, self-organizing neural networks and wavelet function in developing the SOWCMAC to ensure the fast learning ability and desirable generalization ability. Next, we design two kinds of improved integral terminal sliding surfaces and express their convergence time in the analytical form. With the SOWCMAC and improved integral terminal sliding surfaces, we develop the robust adaptive nonsingular terminal controller for the uncertain nonlinear systems. The adaptive robust term can offset the impact of the approximation errors for the system. The stability of the closed-loop system is proved by using the Lyapunov theory. The method is applied to control the attitude system of a near space vehicle. The results show that the proposed method is effective.关键词
terminal滑模控制/自适应控制/有限时间收敛/小脑模型/自组织神经网络Key words
terminal sliding mode control/adaptive control/finite-time convergence/cerebellar model articulation controller (C-MAC)/self-organizing neural networks (SONN)分类
信息技术与安全科学引用本文复制引用
张强,于宏亮,许德智,于美娟..基于自组织小波小脑模型关节控制器的不确定非线性系统鲁棒自适应终端滑模控制[J].控制理论与应用,2016,33(3):387-397,11.基金项目
国家自然科学基金项目(61403161,61503156),山东省自然科学基金项目(ZR2012FQ030),济南大学博士基金项目(XBS1459)资助.Supported by National Natural Science Foundation of China (61403161，61503156), Natural Science Foundation of Shandong Province (ZR2012FQ030) and Doctoral Foundation of University of Jinan (XBS1459) （61403161,61503156）
