Multivariable fuzzy genetic controller for stabilized platform

We proposed a systematic method to design a multivariable fuzzy logic controller for large-scale nonlinear systems. The paper created the stabilized platform model with kinematics and dynamics theory. In designing a fuzzy logic controller, the major task is to determine fuzzy rule bases, membership functions of input/output variables, and input/output scaling factors. In the paper, we designed by the generation of fuzzy rules using a rule-generated function, which was based on the negative gradient of a system performance index, and only the input/output scaling factors ware generated from a genetic algorithm (GA) based on a fitness function. Genetic algorithm was applied for the optimization of the fuzzy scaling factors. We were able to elegantly reject strong disturbances. The approach was validated through various simulations.