Perfect Tracking of Supercavitating Non-minimum Phase Vehicles Using a New Robust and Adaptive Parameter-optimal Iterative Learning Control

In this manuscript, a new method is proposed to provide a perfect tracking of the supercavitation system based on a new two-state model. The tracking of the pitch rate and angle of attack for fin and cavitator input is of the aim. The pitch rate of the supercavitation with respect to fin angle is found as a non-minimum phase behavior. This effect reduces the speed of command pitch rate. Control of such non-minimum phase in a specific time interval and improving the speed response with respect to fin control reaction is still an open problem. To overcome the problem a feed-forward control is proposed to apply on the cavitator as a control in the feed-forward configuration. The idea of this paper is to provide a certain signal for the cavitator in order to improve the tracking performance in presence of uncertainty using iterative learning control. Moreover, this paper proposes a new method based on parameter-optimal iterative learning control to solve a perfect tracking problem of systems for indefinite (not sign-definite) system. This technique provides an updating control law through applying adaptive Lyapunov gain for monotonic zero convergence of tracking error in sense of 2-norm. The simulation results verify performance and robustness of the proposed modification of iterative learning control in comparison with classical controller of the supercavitating vehicle.