基于极限学习机的时滞非线性系统广义预测控制

Li Muwei, Zhou Ying, Wu Qiang
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引用次数: 0

摘要

针对一类时滞非线性被控对象,提出了一种基于极限学习机的广义预测自整定控制方法。在广义预测自整定控制(GPC)中,通过极限学习机(ELM)建立非线性被控对象的预测模型,并不断修正预测输出数据以提高预测精度。控制器采用GPC隐式校正算法,无需辨识模型参数,大大减少了计算量。仿真结果表明,本文方法具有优越性和实用性,预测输出比常用的PID自整定方法更能跟踪参考轨迹。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Generalized predictive control of time-delay nonlinear systems based on extreme learning machine
For a class of nonlinear controlled objects with time-delay, this paper proposes a generalized predictive self-tuning control method based on extreme learning machine. In the generalized predictive self-tuning control (GPC), the predictive model of the nonlinear controlled object is established by the extreme learning machine (ELM), and constantly revising forecast output data to improve the accuracy of the prediction. The controller adopts a GPC implicit correction algorithm, without to identify the model parameters, the calculated amount is greatly reduced. The simulation shows that the method in this paper is superior and practical, the prediction output track the reference trajectory better than the commonly used PID self-tuning method.
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