Two-phase optimization of fuzzy controller by evolutionary programming

In this paper, a two-phase evolutionary optimization scheme is proposed for obtaining optimal structure of fuzzy control rules and their associated weights, using evolutionary programming (EP) and the principle of maximum entropy (PME). The scheme consists of two phases: in the first phase, the rule structure and the scale factors for error, change of error and input are found by EP. The rule structure and the scale factors are encoded by integer and real number string, then varied by the proposed adjacent mutation and Gaussian mutation, respectively. In the second phase, the PME is employed to determine the weights of each rule so that all the fuzzy control rules can be utilized to the greatest extent. The optimization of the second phase can be regarded as fine tuning for the output response of the controlled system. Only several decades of generation is needed for determining the weights in the second phase, so the time-varying plant or online adjustment can be dealt with. The effectiveness of the proposed scheme is demonstrated by computer simulations.