Adaptive tuning of fuzzy logic identifier for unknown non-linear systems

✍ Scribed by Kai Liu; Frank L. Lewis

• Publisher: John Wiley and Sons
• Year: 1994
• Tongue: English
• Weight: 650 KB
• Volume: 8
• Category: Article
• ISSN: 0890-6327
• DOI: 10.1002/acs.4480080605

✦ Synopsis

An on-line approximator using a linear fuzzy logic model with automatic tuning mechanism is presented in this paper. The proposed approximation method can be used to model a class of unknown non-linear systems as long as they are Caratheodory ones. The structure of the fuzzy model is fixed but the parameters are tuned by the state errors. The fuzzy 'model is composed of several semi-closed, continuous, totally ordered and well-defined fuzzy numbers defined in each state variable dimension and a number of appropriately defined condition-action rules. Either the min-inference or product-inference technique is utilized to generate the weighted average of the linear coefficients which make the whole unknown non-linear system piecewise linearized. No off-line preprocessing is needed. The initial values of the parameters of the fuzzy model can be arbitrarily assigned. Then they are tuned to their true values through adaptive update law and therefore it is guaranteed that the unknown non-linear system is linearized and approximated to any degree of accuracy by the linear fuzzy logic model.