Noncertainty-equivalent multi-variable adaptive control of submersibles using filtered signals

Abstract

Based on the attractive manifold design approach, a noncertainty-equivalent adaptive autopilot for the dive-plane control of submersibles, equipped with bow and stern hydroplanes, is developed. The parameters of the model are unknown and disturbance inputs are assumed to be present. The objective is to control the depth and pitch angle of the vehicle. For the vehicle in the presence of disturbance input and parameter uncertainties, it is shown that in the closed-loop system with σ-modification, the pitch angle and depth tracking errors are uniformly ultimately bounded. Also it is proven that for the disturbance-free submarine model with unknown parameters, precise asymptotic tracking of the reference trajectories is accomplished. Furthermore the control system accomplishes regulation of the system trajectories to a manifold, and asymptotically achieves the performance of a deterministic controller. Simulation results are presented which show that the submarine performs the specified dive-plane maneuvers in spite of large parameter uncertainties and random disturbance forces.