Soft Computing Optimizer For Intelligent Control Systems Design: The Structure And Applications

• Main Authors: Sergey A. Panfilov, Ludmila V. Litvintseva, Ilya S. Ulyanov, Kazuki Takahashi, Serguei V. Ulyanov, Alexander V. Yazenin, Takahide Hagiwara
• Format: Article
• Language: English
• Published: International Institute of Informatics and Cybernetics 2003-10-01
• Series: Journal of Systemics, Cybernetics and Informatics
• Subjects: knowledge base; fitness function; intelligent control; robust fuzzy controller; soft computing optimizer
• Online Access: http://www.iiisci.org/Journal/CV$/sci/pdfs/P900699.pdf

Soft Computing Optimizer (SCO) as a new software tool for design of robust intelligent control systems is described. It is based on the hybrid methodology of soft computing and stochastic simulation. It uses as an input the measured or simulated data about the modeled system. SCO is used to design an optimal fuzzy inference system, which approximates a random behavior of control object with the certain accuracy. The task of the fuzzy inference system construction is reduced to the subtasks such as forming of the linguistic variables for each input and output variable, creation of rule data base, optimization of rule data base and refinement of the parameters of the membership functions. Each task by the corresponding genetic algorithm (with an appropriate fitness function) is solved. The result of SCO application is the design of Knowledge Base of a Fuzzy Controller, which contains the value information about developed fuzzy inference system. Such value information can be downloaded into the actual fuzzy controller to perform online fuzzy control. Simulations results of robust fuzzy control of nonlinear dynamic systems and experimental results of application on automotive semi-active suspension control are demonstrated.