Linear Parameter-Varying Model Predictive Control of AUV for Docking Scenarios

Abstract

A control system for driving an Autonomous Underwater Vehicle (AUV) performing docking operations in presence of tidal current disturbances is proposed. The nonlinear model of the vehicle has been modelled in a Linear Parameter-Varying (LPV) form. This is suitable for the design of the control system using a model-based approach. The LPV model was used for a Model Predictive Control (MPC) design for computing the set of forces and moments driving the nonlinear vehicle model. The LPV-MPC control action is mapped into the reference signals for the actuators by using a Thrust Allocation (TA) algorithm. This was based on the nonlinear models for the actuators and their position and orientation on the vehicle’s hull. The structural decomposition of MPC and TA reduces the computational burden involved in computing the control law on-line on an embedded control board. Both MPC and TA algorithms use the vehicle’s linear and angular positions, and velocities that are estimated by an LPV based Kalman Filter (KF). The proposed control system has been tested in different docking scenarios using various tidal current disturbances acting on the vehicle as an unmeasured disturbance. The simulation results show the controller is effective in controlling the AUV over the range of control scenarios meeting the constraints and specifications.