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

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