基于非线性函数仿真的复杂系统鲁棒自适应控制

G. Dimirovski, Yuanwei Jing, Yanxin Zhang, M. Vukobratovic
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引用次数: 1

摘要

针对一类具有相似特性的复杂非线性机电系统,提出了一种结合高阶神经网络和数学分析结果的鲁棒自适应控制设计综合方法。该方法充分利用复合相似系统和神经网络的结构特点,通过在线更新权值来解决不确定性互连和子系统增益的表示问题。这种综合确实保证了闭环的真正稳定性,但需要技巧来获得更大的吸引域。以具有不确定性的轴盘驱动系统为例,说明了该方法的可行性
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Robust Adaptive Control for Complex Systems Employing ANN Emulation of Nonlinear Functions
A new robust adaptive control design synthesis, which employs both high-order neural networks and math-analytical results, for a class of complex nonlinear mechatronic systems possessing similarity property has been derived. This approach makes an adequate use of the structural feature of composite similarity systems and neural networks to resolve the representation issue of uncertainty interconnections and subsystem gains by on-line updating the weights. This synthesis does guarantee the real stability in closed-loop but requires skills to obtain larger attraction domains. Mechatronic example of an axis-tray drive system, possessing uncertainties, is used to illustrate the proposed technique
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