Application of PID and Norm Optimal Iterative Learning Control to Swash Mass Helicopter

In this paper, a combination of both PID and norm optimal iterative learning control (NOILC) is designed and analyzed for the swash mass helicopter (SMH). The translational and rotational subsystems of the helicopter are highly coupled therefore, change of coordinate and feedback linearization methodologies are applied to address the problem and achieve the canonical form of the model. External disturbances or noises as wind power or aerodynamic disturbance affect SMH performance during tracking a flight path. Accordingly, we propose repetitive learning as a new solution to the problem. In fact, the performance of the ILC that is based on optimal approach, namely norm optimal ILC, is combined with PID controller. The Lyapunov theory illustrates that the proposed control strategy can stabilize the system despite the external disturbances or noises. The results show the good trajectory tracking, suitable convergence speed and the ability to cope with exogenous disturbances in comparison with another method.