Fuzzy model predictive control of non-linear processes using genetic algorithms

✍ Scribed by Haralambos Sarimveis; George Bafas

• Publisher: Elsevier Science
• Year: 2003
• Tongue: English
• Weight: 493 KB
• Volume: 139
• Category: Article
• ISSN: 0165-0114
• DOI: 10.1016/s0165-0114(02)00506-7

✦ Synopsis

This paper introduces a new fuzzy control technique, which belongs to the popular family of control algorithms, called Model Predictive Controllers. The method is based on a dynamic fuzzy model of the process to be controlled, which is used for predicting the future behavior of the output variables. A non-linear optimization problem is then formulated, which minimizes the di erence between the model predictions and the desired trajectory over the prediction horizon and the control energy over a shorter control horizon. The problem is solved on line using a specially designed genetic algorithm, which has a number of advantages over conventional non-linear optimization techniques. The method can be used with any type of fuzzy model and is particularly useful when a direct fuzzy controller cannot be designed due to the complexity of the process and the di culty in developing fuzzy control rules. The method is illustrated via the application to a non-linear single-input single-output reactor, where a Takagi-Sugeno model serves as a predictor of the process future behavior.