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面向控制性能的一类非线性关联系统输出反馈动态面控制
引用本文:余修端,孙秀霞,林岩,董文瀚.面向控制性能的一类非线性关联系统输出反馈动态面控制[J].控制理论与应用,2011,28(12):1754-1762.
作者姓名:余修端  孙秀霞  林岩  董文瀚
作者单位:1. 空军工程大学工程学院,陕西西安,710038
2. 北京航空航天大学自动化科学与电气工程学院,北京,100191
基金项目:国家自然科学基金资助项目(60904038, 60874044).
摘    要:针对一类含有完全未知关联项的多输入/多输出非线性系统,提出了输出反馈动态面自适应控制方案,克服了反推控制中的微分爆炸问题;利用神经网络逼近系统中的未知关联项,对于每个子系统只需对一个参数设计自适应律;引入性能函数和输出误差变换,跟踪误差信号的收敛速率、最大超调量和稳态误差等控制性能指标均可得到保证.理论证明了闭环系统的所有信号半全局一致有界,仿真结果验证了所提方案的有效性.

关 键 词:动态面控制  反推控制  神经网络  性能函数  输出误差变换
收稿时间:2011/1/17 0:00:00
修稿时间:2011/6/21 0:00:00

Control performance-oriented output feedback dynamic surface control for a class of interconnected nonlinear systems
YU Xiu-duan,SUN Xiu-xia,LIN Yan,DONG Wen-han.Control performance-oriented output feedback dynamic surface control for a class of interconnected nonlinear systems[J].Control Theory & Applications,2011,28(12):1754-1762.
Authors:YU Xiu-duan  SUN Xiu-xia  LIN Yan  DONG Wen-han
Affiliation:Engineering Institute, Air Force Engineering University,Engineering Institute, Air Force Engineering University,School of Automation Science and Electrical Engineering, Beihang University,Engineering Institute, Air Force Engineering University
Abstract:An output feedback adaptive dynamic surface control(DSC) scheme is proposed for a class of MIMO nonlinear systems with completely unknown interconnections. In this scheme, the explosion of complexity problem inherent in traditional backstepping design is eliminated. The radial-basis-function(RBF) neural network(NN) is employed to approximate the uncertain interconnected items. The advantage is that there is only one parameter needed to be updated online for each subsystem. Moreover, performance function and output error transformation are introduced to guarantee the convergence rate of the tracking errors, the allowable maximum overshoot, and the steady-state error, etc. It is proved that all signals in the closed-loop system are semi-globally uniformly ultimately bounded. Simulation results show the effectiveness of the proposed scheme.
Keywords:dynamic surface control  backstepping control  neural networks  performance function  output error transformation
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