A performance analysis of various neuro-fuzzy approaches for controlling nonlinear systems

1994 Proceedings of IEEE International Conference on Control and Applications Pub Date : 1994-08-24 DOI:10.1109/CCA.1994.381230

E. Teixeira, G. Laforga, H. Azevedo

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Abstract

Nonlinear systems are becoming an area of great interest in the control engineering community. Many interesting problems such as controllability, input-output decoupling, feedback linearization, have been approached with success. On the other hand, not so many results have been achieved in the solution of the problem of identification and control of unknown nonlinear systems. The application of linear methods to nonlinear systems is not very successful when wide control ranges are necessary. For those cases non-conventional methods, such as the use of neural networks, have been investigated. Another promising approach is the application of fuzzy logic to the control of some classes of nonlinear systems. The method is simple, not time consuming, and requires little knowledge of the system equations. The combination of these two methods have been tried with success and is known as a neuro-fuzzy system. This paper presents an overview of the various neuro-fuzzy approaches, and their application to the control of nonlinear systems.<>

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各种控制非线性系统的神经模糊方法的性能分析

非线性系统正成为控制工程界非常感兴趣的一个领域。许多有趣的问题，如可控性，输入输出解耦，反馈线性化，已经成功地进行了探讨。另一方面，对于未知非线性系统的辨识与控制问题的研究成果并不多。当需要较宽的控制范围时，线性方法在非线性系统中的应用并不十分成功。对于这些情况，非传统的方法，如使用神经网络，已经进行了研究。另一种有前途的方法是将模糊逻辑应用于某些非线性系统的控制。该方法简单，不耗时，对系统方程的知识要求不高。这两种方法的结合已经被成功地尝试过，并被称为神经模糊系统。本文概述了各种神经模糊方法及其在非线性系统控制中的应用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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1994 Proceedings of IEEE International Conference on Control and Applications

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