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.
Highlights
The control of an Autonomous Underwater Vehicle (AUV) is considered
The nonlinear dynamics model of the AUV was developed for the vehicle dynamics, actuators and sensor characteristics, and the effect of tidal currents acting on the vehicle was treated as an unmeasured disturbance
A Thrust Allocation (TA) algorithm subsystem was based on the models of the actuators
Summary
The control of an Autonomous Underwater Vehicle (AUV) is considered. This has no tether and is used for large-area subsea survey or seabed mapping. In Reference [7], the MPC is applied to the trajectory tracking of an AUV and the equation of motion is limited to the motion in the horizontal plane It is linearized around a constant forward speed to reduce the computational load. The three forces (moments) are used to control the three degrees of freedom (u, v, r) distributed models, and ways to reduce computational load are considered for nonlinear MPC problems. In both these cases, the control problem is limited to the horizontal plane.
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