Fuzzy model predictive control: techniques, stability issues, and examples

Proceedings of the 1999 IEEE International Symposium on Intelligent Control Intelligent Systems and Semiotics (Cat. No.99CH37014) Pub Date : 1900-01-01 DOI:10.1109/ISIC.1999.796692

H. Nounou, Kevin M. Passino

引用次数: 32

Abstract

Fuzzy model predictive control (FMPC) algorithms presented here are model-based control schemes in which the models used for prediction are Takagi-Sugeno fuzzy systems (TSFS). Three approaches to FMPC design are discussed. The fuzzy model in the first approach can be represented as a time-varying affine model that is used for control. In the second approach, the fuzzy system is a convex combination of multiple affine models, where the control is a convex combination of multiple controllers. Lastly, the control of the third algorithm is obtained when only the model with the highest certainty is used in the design. Also, we extend the idea to have an adaptive controller for the first algorithm, where the parameters of the fuzzy model are updated online.

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模糊模型预测控制:技术、稳定性问题和例子

本文提出的模糊模型预测控制(FMPC)算法是基于模型的控制方案，其中用于预测的模型是Takagi-Sugeno模糊系统(TSFS)。讨论了FMPC的三种设计方法。第一种方法中的模糊模型可以表示为用于控制的时变仿射模型。在第二种方法中，模糊系统是多个仿射模型的凸组合，其中控制是多个控制器的凸组合。最后，在只使用确定性最高的模型进行设计时，得到了第三种算法的控制。此外，我们扩展了该思想，为第一种算法提供了自适应控制器，其中模糊模型的参数在线更新。

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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Proceedings of the 1999 IEEE International Symposium on Intelligent Control Intelligent Systems and Semiotics (Cat. No.99CH37014)

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