Optimal hybrid control of linear and nonlinear systems

A genetic algorithm (GA)-based optimized hybrid controller which is suitable for controlling both linear and nonlinear systems is proposed. The proposed controller, consisting of a linear proportional-integral-derivative (PID) controller and a linear fuzzy logic controller, employs a GA to facilitate optimal tuning of controller gains. A two-input normalized fuzzy logic controller with a linearly-defined fuzzy space is developed to replace the conventional PI controller in the PID connective structure. A closed-form analysis shows that the proposed fuzzy logic controller is capable of generating nonlinear output. Simulation results for a typical second-order over-damped process and a tactical missile model demonstrate that the proposed controller outperforms other existing controllers, is robust and has great potential in many industrial applications