2021 · 21 citations · 14 references
EngineeringAutonomous SystemsMarine EngineeringAutonomous VehiclesMarine RoboticsShip ControlAutonomous Ship ManeuveringMarine Robot ControlMission PlanningModel Predictive ControlOptimal ControlReinforcement LearningAutonomous Ship ControlAutonomous Surface Vehicle ControlIntelligent Control Systems For ShipsNonlinear Model Predictive ControlNonlinear Ship ControlRobust Control For ShipsRobust Model Predictive ControlRobust Reinforcement Learning
In this paper, we present the use of Reinforcement Learning (RL) based on Robust Model Predictive Control (RMPC) for the control of an Autonomous Surface Vehicle (ASV). The RL-MPC strategy is utilized for obstacle avoidance and target (set-point) tracking. A scenario-tree robust MPC is used to handle potential failures of the ship thrusters. Besides, the wind and ocean current are considered as unknown stochastic disturbances in the real system, which are handled via constraints tightening. The tightening and other cost parameters are adjusted by RL, using a Q-Iearning technique. An economic cost is considered, minimizing the time and energy required to achieve the ship missions. The method is illustrated in simulation on a nonlinear 3-DOF model of a scaled version of the Cybership II.
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Reinforcement Learning: An Introduction
Richard S. Sutton, Andy Barto · IEEE Transactions on Neural Networks · 1998 · 27.1K citations
Reinforcement Learning: An Introduction
IEEE Transactions on Neural Networks · 2005 · 25.8K citations