Designing fuzzy net controllers using genetic algorithms

As control system tasks become more demanding, more robust controller design methodologies are needed. A genetic algorithm (GA) optimizer, which utilizes natural evolution strategies, offers a promising technology that supports optimization of the parameters of fuzzy logic and other parameterized nonlinear controllers. This article shows how GAs can effectively and efficiently optimize the performance of fuzzy net controllers employing high performance simulation to reduce the design cycle time from hours to minutes. Our results demonstrate the robustness of a GA-based computer-aided system design methodology for rapid prototyping of control systems