This paper proposes an automatic method to design a fuzzy logic controller for the automated car parking problem. To tackle the problem, design of a fuzzy logic controller is solved under the multi-objective evolutionary optimization framework, which requires three factors: an encoding scheme, design of multi-objective evaluation criteria, and design of proper evolutionary operations. Along with the parameters of antecedent membership functions, consequent parameter vectors are defined in a chromosome so that a fuzzy logic controller can modify its antecedent/consequent parameters and rule structure simultaneously during evolution. Three optimization criteria are proposed to find a set of good fuzzy logic controllers using Pareto optimality. The proposed algorithm is applied to the well-known car parking problem and produces a set of good fuzzy logic controllers that can control the motion of a vehicle for automated parking. Each fuzzy logic controller in the set has unique characteristics and can be selected according to users' preferences, which is one of the major advantages of using the multi-objective evolutionary optimization.


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    Title :

    Multi-objective evolutionary design of fuzzy logic controllers for car parking problem


    Contributors:


    Publication date :

    2006


    Size :

    6 Seiten, 17 Quellen



    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English





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