Neural-Network- and L2-Gain-Based Cascaded Control of Underwater Robot Thrust

Abstract

This paper presents a robust cascaded control strategy to underwater robot thrust. The dynamics of surge motion, of propeller axial flow, of propeller shaft, and of electrically driven circuit in the motor constitute a cascaded system with respect to propeller thrust. Instead of the usual parameter perturbation, generalized modeling errors are considered in the plant, which may be parametric errors, ignored high-order modes, or some unmodeled dynamics in the underwater thrust system. External disturbances are also taken into account, which may be the random noises from mechanical or electrical equipment, or the environmental forces possibly induced by nonuniform currents, ocean internal wave, or cable tension. Combined with state feedback control, an online neural network (NN) compensator is introduced to identify the modeling errors, while L2-gain design is used to suppress the externally continuous or instantaneous disturbances. The Lyapunov's second method is applied to instruct the controller design, which guarantees the uniformly ultimately bounded (UUB) stability of the error system. By analyzing the tracking errors, it is recommended how to properly select the controller parameters. Good tracking performance and reasonable control inputs are illustrated by numerical simulations.