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Fuzzy controllers belong to the class of knowledge based systems. Their main goal is to implement human know-how or heuristic rules in the form of a computer program. Fuzzy logic provides a mathematical formalism for this goal. An original method for the design and analysis of a fuzzy controller is proposed in this book. The main idea is to apply an on-line adaptive algorithm to automatically adjust the parameters of the fuzzy controller, depending on its inputs and desired outputs. It is shown that a fuzzy controller can learn to approximate non-linear functions arbitrarily well. The author also introduces a new method for the extraction of linguistic rules from the adapted parameters. The knowledge acquired during learning can be represented in a clear and intuitive syntax. This gives the designer a comprehensive understanding of the new rule base, i.e., the controller features. In the last part of the book, the application of fuzzy and neuro-fuzzy controllers to the navigation of a mobile robot is investigated. Several experiments with the miniature mobile Khepera are reported. The proposed design method for fuzzy controller is tested, showing that it provides the designer with a technique to handle complex tasks: the robot can successfully learn the desired behaviours, even with noisy sensors and large rule bases. The extraction of rules reveals to be extremely useful for practical applications because it helps the engineer to learn more on the complete system.