基于Levenberg-Marquardt优化方法的模糊神经预测控制

2013 IEEE INISTA Pub Date : 2013-06-19 DOI:10.1109/INISTA.2013.6577624

Y. Todorov, Margarita Terzyiska, Sevil A. Ahmed, M. Petrov

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引用次数: 4

摘要

本文研究了Levenberg-Marquardt优化方法对非线性模型预测控制器控制动作计算的影响。为了预测未来植物的行为，使用了经典的Takagi-Sugeno推理。采用梯度下降法和Newton-Raphson优化法进行了比较。在MATLAB环境下对连续搅拌槽式反应器进行了控制实验，验证了所提优化策略的有效性。

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Fuzzy-neural predictive control using Levenberg-Marquardt optimization approach

It is proposed in this paper a study on the influence of the Levenberg-Marquardt optimization approach for computation of the control actions in Nonlinear Model Predictive Controller. To predict the future plant behavior, a classical Takagi-Sugeno inference is used. A comparison by applying the Gradient descent and the Newton-Raphson optimization approaches is made. The efficiency of the proposed optimization strategies is demonstrated by experiments in MATLAB environment to control a Continuous Stirred Tank Reactor.

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2013 IEEE INISTA

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