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首页> 外文会议>International Conference on Industrial, Engineering, and Other Applications of Applied Intelligent Systems(IEA/AIE 2007); 20070626-29; Kyoto(JP) >Supervised Adaptive Control of Unknown Nonlinear Systems Using Fuzzily Blended Time-Varying Canonical Model
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Supervised Adaptive Control of Unknown Nonlinear Systems Using Fuzzily Blended Time-Varying Canonical Model

机译：模糊混合时变典范模型的未知非线性系统的监督自适应控制

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摘要

In spite of the prosperous literature in adaptive control, application of this promising control strategy has been restricted by the lack of assurance in closed-loop stability. This paper proposes an adaptive control architecture, which is augmented by a supervising controller, to enhance the robustness of an adaptive PID control system in the face of exaggerated variation in system parameters, disturbances, or parameter drift in the adaptation law. Importantly, the supervising controller is designed based on an on-line identified model in a fuzzily blended time-varying canonical form. This model largely simplified the identification process, and the design of both the supervising controller and the adaptation law. Numerical studies of the tracking control of an uncertain Duffing-Holmes system demonstrate the effectiveness of the proposed control strategy.
机译：尽管自适应控制方面的文献很丰富，但是由于缺乏闭环稳定性的保证，这种有前途的控制策略的应用受到了限制。本文提出了一种自适应控制体系结构，该结构由监督控制器进行了增强，以增强自适应PID控制系统的鲁棒性，以应对系统参数，干扰或自适应定律中参数漂移过大的情况。重要的是，基于模糊混合时变规范形式的在线识别模型来设计监督控制器。该模型极大地简化了识别过程，简化了监督控制器和适应律的设计。不确定的Duffing-Holmes系统的跟踪控制的数值研究证明了所提出的控制策略的有效性。

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