Differential Evolution Based Tuning of Fuzzy Automatic Train Operation for Mass Rapid Transit System


Train performance of mass rapid transit systems call be improved with the use of fuzzy controllers in automatic train operation (ATO) systems. The tuning of these fuzzy controllers is presented using the algorithm of differential evolution (PE). The basic DE algorithm is modified to optimise a multiobjective function comprising punctuality, riding comfort and energy usage. Using this algorithm, the fuzzy ATO controller is tuned for each interstation train run. In operation, the controller adjusts each train's control according to the current operating conditions. A fuzzy ATO controller model was previously developed by the authors and is used to demonstrate the effectiveness of the tuning scheme.