Nonlinear identification and MIMO control of a laboratory helicopter using genetic algorithm

Closed loop identification of nonlinear model and control of a laboratory helicopter using genetic algorithm is proposed in this paper. The derived model has a nonlinear structure. Using the previous results of the physical modeling of the studied plant, a nonlinear model is considered based on the physical dynamics of the system. However, there is no need to perform numerous physical experiments to estimate the model parameters. Instead, genetic algorithm as a nonlinear optimization technique is used to obtain the parameters of the model. Therefore, the advantage of both modeling and identification methods are employed. In the next step, the parameters of a multi input-multi output (MIMO) PID controller for the derived model will be tuned by GA using the obtained nonlinear model as a simulator of the plant. Applying the controller to both the real plant and the simulation model, the accuracy of the model and the performance of the controller is examined. The results demonstrate that the achieved model accurately fits to the behavior of the real plant and the controller designed based on this model, can control the real system appropriately.