并联分布式神经模糊模型预测控制器在水轮发电机中的应用

M. Petrov, A. Taneva, T. Puleva, S. Ahmed
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引用次数: 2

摘要

模型预测控制(MPC)被认为是近二十年来过程控制领域最重要的发展。本文讨论了用非线性模型预测控制公式对非线性对象进行控制的问题。为了处理非线性,提出了一种Takagi-Sugeno神经模糊模型,作为一种根据操作区域对非线性对象进行建模的方法。将该模型作为一种并行分布式模型预测控制算法的预测模型。针对水轮发电机的非线性控制系统,提出了一种并联分布式神经模糊模型预测控制器。该技术已在模拟工业装置上进行了测试和评价。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Parallel distributed neuro-fuzzy model predictive controller applied to a hydro turbine generator
Model predictive control (MPC) has been considered as the most important development in the area of process control in the last two decades. This paper addresses the issue of controlling a nonlinear plant by the use of the nonlinear model predictive control formulation. To handle the nonlinearities, a Takagi-Sugeno neuro-fuzzy model is suggested as a means to model the plant with nonlinearities depending on the operating region. The developed model is used as a predictive model for a parallel distributed model predictive control algorithm. In this paper, the parallel distributed neuro-fuzzy model predictive controller has been proposed to control a non-linear control system of a hydro turbine generator. The proposed technique has been tested and evaluated using this simulated industrial plant.
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