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

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.

[1]  P. Encarnacao,et al.  3D path following for autonomous underwater vehicle , 2000, Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187).

[2]  Brian D. O. Anderson,et al.  Gain scheduling using time-varying Kalman filter for a class of LPV systems , 2008 .

[3]  Gionata Cimini,et al.  Computationally efficient model predictive control for a class of linear parameter‐varying systems , 2018, IET Control Theory & Applications.

[4]  Alberto Bemporad,et al.  Model predictive control for pre-compensated voltage mode controlled DC–DC converters , 2017 .

[5]  Weidong Zhang,et al.  Fast Trajectory Tracking Control of Underactuated Autonomous Underwater Vehicles , 2018, 2018 IEEE 8th International Conference on Underwater System Technology: Theory and Applications (USYS).

[6]  Thor I. Fossen,et al.  Modeling and Control of Underwater Robots , 2016, Springer Handbook of Robotics, 2nd Ed..

[7]  Michael J. Grimble,et al.  Nonlinear Industrial Control Systems , 2020 .

[8]  Daqi Zhu,et al.  Underwater Dynamic Target Tracking of Autonomous Underwater Vehicle Based on MPC Algorithm , 2018, 2018 IEEE 8th International Conference on Underwater System Technology: Theory and Applications (USYS).

[9]  Chao Shen,et al.  Distributed implementation of nonlinear model predictive control for AUV trajectory tracking , 2020, Autom..

[10]  Timothy Prestero,et al.  Verification of a six-degree of freedom simulation model for the REMUS autonomous underwater vehicle , 2001 .

[11]  Gionata Cimini,et al.  A fast model predictive control algorithm for linear parameter varying systems with right invertible input matrix , 2017, 2017 25th Mediterranean Conference on Control and Automation (MED).

[12]  Robert Sutton,et al.  Pure pursuit guidance and model predictive control of an autonomous underwater vehicle for cable/pipeline tracking , 2003 .

[13]  Gianluca Ippoliti,et al.  Fault tolerant model predictive control for an over-actuated vessel , 2018, Ocean Engineering.

[14]  Ikuo Yamamoto,et al.  Concept of Autonomous Underwater Vehicle Docking Using 3D Imaging Sonar , 2019 .

[15]  Daqi Zhu,et al.  Adaptive sliding mode heading control for autonomous underwater vehicle including actuator dynamics , 2016, OCEANS 2016 - Shanghai.

[16]  Ge Guo,et al.  Adaptive formation control of autonomous underwater vehicles with model uncertainties , 2018 .