Determining the Parameters of a Sugeno Fuzzy Controller Using a Parallel Genetic Algorithm

Developed in the mid 1970s, the technique based on genetic algorithms proved its usefulness in finding optimal or near optimal solutions to problems for which accurate solving strategies are either non-existent or require excessively long running time. We implemented a genetic algorithm to determine the parameters of a Sugeno fuzzy controller for the Truck Backer-Upper problem (This problem is considered an acknowledged benchmark in nonlinear system identification.). Less known at first than Mamdami fuzzy controllers, Sugeno fuzzy controllers became popular once they were included into the ANFIS neuro-fuzzy Matlab library. By their nature, Sugeno controllers can be regarded as interpolation functions. As we know, an interpolation function approximates a function with an unknown expression, yet whose values taken at a number of points in the definition domain (called Interpol nodes) are known. Normally, the interpolation function performs very well in the interpolation nodes, it but can have a totally unsatisfactory behaviour in other points of the definition domain. In our case, we considered interpolation nodes as the set of values used to assess chromosomes in order to apply genetic algorithm operations. We aimed to determine a set of values so as to obtain the most efficient controllers.