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一类不确定非线性系统基于SVR的Backstepping自适应跟踪控制 被引量:1

Adaptive backstepping tracking control for a class of uncertain nonlinear systems using SVR
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摘要 针对含有不确定非线性扰动项的一类非线性系统,结合支持向量回归理论,采用Backstep-ping控制方法设计自适应非线性控制器.在分析满足Backstepping设计条件的一类非线性系统结构形式的基础上,应用支持向量回归辨识并补偿系统不确定项及未知扰动,基于Lyapunov稳定性理论,选取Lyapunov函数,证明闭环系统最终一致有界,且跟踪误差指数收敛.通过对典型系统仿真分析表明,相比于径向基神经网络自适应Backstepping控制方案,支持向量回归因其内部参数由训练优化产生,不依赖先验经验及外界干预,适应性较好,对参考指令信号跟踪收敛快,稳态误差小,控制方案有效,且系统具有一定鲁棒性. Abstract: An adaptive nonlinear controller is support vector regression theory (SVR) for developed combining backstepping technique with the a class of nonlinear systems which contain uncertain nonlinear disturbance. On the basis of analyzing the backstepping design conditions which nonlinear system should meet, the system uncertainties and uncertain disturbances are identified and compensa ted by using support vector regression. The closedloop system is guaranteed to be ultimately uni formly bounded and tracking errors are also proved to converge exponentially by choosing appropri ate Lyapunov function which is based on the Lyapunov theory. The simulation result shows that, compared with the adaptive backstepping control approach using radial basis function network, the proposed control scheme shows better tracking performance and the system output has faster conver gence speed and smaller static error. Because the parameters in support vector regression are trained and optimized by itself, and they do not rely on the experiences of the designer. So it has strong adaptability. Finally, the effectiveness and robustness of the proposed method can be concluded.
出处 《东南大学学报(自然科学版)》 EI CAS CSCD 北大核心 2012年第A01期20-24,共5页 Journal of Southeast University:Natural Science Edition
基金 国家高技术研究发展计划(863计划)资助项目(2011AA7053016)
关键词 反演控制 支持向量回归 自适应控制 非线性 backstepping control support vector regression adaptive control nonlinear
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  • 1Nam K, Arapostations A. A model reference adaptive control scheme for pure-feedback nonlinear systems EJ]. IEEE Transactions on Neural Networks, 1998, 33 (9) : 803 -811.
  • 2Labiod S, Boucherit M S, Guerra T M. Adaptive fuzzy control of a class of MIMO nonlinear systems[J]. Fuzzy Sets and Systems, 2005, 151 ( 1 ) : 59 - 77.
  • 3Yucelen T, Calise A J. Kalman Filter Modification in Adaptive Control [ J ]. Journal of Guidance, Control, and Dynamics, 2010, 33(2): 426-439.
  • 4董文瀚,孙秀霞,林岩.反推自适应控制的发展及应用[J].控制与决策,2006,21(10):1081-1086. 被引量:33
  • 5Wang C, Ge S S. Adaptive backstepping control of un- certain lorenz system [ J ]. International Journal of Bi- furcation and Chaos, 2001, 11 (4) : 1115 -1119.
  • 6董文瀚,孙秀霞,林岩,宋鸿飞.一类直接模型参考Backstepping自适应控制[J].控制与决策,2008,23(9):981-986. 被引量:11
  • 7贺乃宝,姜长生,高倩.一类不确定非线性系统基于Backstepping的自适应跟踪控制[J].哈尔滨工业大学学报,2009,41(5):169-171. 被引量:2
  • 8Kwan C, Lewis F L. Robust backstepping control of nonlinear systems using neural networks [ J ]. IEEE Transactions on Systems, Man, and Cybernetics, 2000, 30(6) : 753 -765.
  • 9Knohl T, Unbehauen H. ANNNAC -- extension of adaptive backstepping algorithm with artificial neural networks [ J ]. Control Theory and Applications, 2000, 147:177 - 183.
  • 10Zhang T, Ge S S, Hang C C. Adaptive neural network control for strict-feedback nonlinear systems using backstepping design [ J]. Automatica, 2000, 36(12) : 1835 - 1846.

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