Advanced genetic algorithms applied in MRFACS for fuzzy rules set optimization

We propose a methodology of genetic algorithms (GAs) for the rules searching of model reference fuzzy adaptive control system (MRFACS). We choose a second order reference model as an ideal output, then adjust the proportional sensitivity by using fuzzy adaptive controller to make the plant´s output follows the reference signal. We apply modified GAs for rules searching because that the rules set constructed by cut and try works unsatisfactory. In our study, we offer two types of fuzzy controllers (one is constructed with state error ε & change rate of error Δε and the other is constructed with state error ε & plant output y_p). The conclusions we get from simulation results are: (1) Modified GAs can find population with higher fitness values since it select better populations by multiple-point crossover and multiple-point mutation, (2) Fuzzy controller with ε and y_p shows higher performance indices than that with ε& Δε for the reason that the front controller can avoid using same rules for different time delay constants, and (3) System remains controllable when the time delay constants exceed the expected margin, it proves that our fuzzy controller contains the characteristic of robustness