GA-optimized fuzzy controller for spacecraft attitude control

Spacecraft attitude control is approached as a nonlinear control problem, the robustness quality of the genetic algorithm (GA)-optimized fuzzy logic (FL) control is demonstrated using a nonlinear model of the Space Station Freedom. The main components of the GA-optimized fuzzy controller (GAoFC) are the GA adaptive learning module, the objective function module, and the FC (fuzzy control) module. Performance of the nonlinear GAoFC is verified using a nonlinear simulation of the Space Station. We study cases of manually generated parameters rule set, GA-optimized parameters, and GA-optimized rules and parameters. Results presented substantiate the feasibility of using GA-optimized fuzzy controllers in robust nonlinear control of spacecraft