Designing fuzzy net controllers using GA optimization

As plant specifications become complicated, more robust controller design methodologies are needed. A genetic algorithm optimizer, which utilizes natural evolution strategies, offers a promising technology that supports optimization of the parameters of fuzzy logic and other parameterized non-linear controllers. This paper shows how GAs can effectively and efficiently optimize the performance of parameterized non-linear controllers, such as fuzzy net controllers in a multiprocessor simulation environment. Our results demonstrate the advantage of a Computer-Aided System Design technique for rapid prototyping of control systems