PID control using presearched genetic algorithms for a MIMO system

This correspondence presents a new approach that utilizes evolutionary computation and proportional-integral differential (PID) control to a multi-input multioutput (MIMO) nonlinear system. This approach is demonstrated through a laboratory helicopter called the twin rotor MIMO system (TRMS). The goals of control are to stabilize the TRMS in significant cross-couplings, reach a desired position, and track a specified trajectory efficiently. The proposed control scheme includes four PID controllers with independent input. In order to reduce total error and control energy, all parameters of the controller are obtained by a real-value-type genetic algorithm (RGA) with a system performance index as the fitness function. The system performance index was applied to the integral of time multiplied by the square error criterion to build a suitable fitness function in the RGA. We also investigated a new method for the RGA to solve more than ten parameters in the control scheme. The initial search range of the RGA was obtained by a nonlinear control design (NCD) technique. The NCD provided a narrow initial search range for the RGA. This new method led chromosomes to converge to optimal solutions more quickly in a complicated hyperplane. Computer simulations show that the proposed control scheme conquers system nonlinearities and influence between two rotors successfully.