基于分段线性神经模型的非线性对象预测控制

Daniel Honc, P. Doležel, L. Gago
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引用次数: 2

摘要

本文提出了一种特殊形式的预测控制器。在前人研究的基础上,采用被控对象的分段线性神经模型进行局部线性化。然后将线性化模型用于使用预测控制器进行控制动作评估。采用分段线性神经网络进行线性化虽然简单有效,但其模型形式不规范。因此,所提出的预测控制器是为了在不进行任何定制的情况下处理非标准模型。在论文的最后,通过实例说明了所引入的解决方案的主要特点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predictive control of nonlinear plant using piecewise-linear neural model
A special form of a predictive controller is presented in this paper. Based on previous authors' work, a piecewise-linear neural model of nonlinear plant to be controlled is adopted to local linearization. The linearized model is then used for control action evaluation using a predictive controller. Although the linearization using piecewise-linear neural network is simple and efficient, it provides the model in a nonstandard form. Therefore, the proposed predictive controller is designed in order to handle that nonstandard model without any customization. At the end of the paper, the illustrative example demonstrates the main features of the introduced solution.
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