570 research outputs found
Deep Reinforcement Learning Controller for 3D Path-following and Collision Avoidance by Autonomous Underwater Vehicles
Control theory provides engineers with a multitude of tools to design
controllers that manipulate the closed-loop behavior and stability of dynamical
systems. These methods rely heavily on insights about the mathematical model
governing the physical system. However, in complex systems, such as autonomous
underwater vehicles performing the dual objective of path-following and
collision avoidance, decision making becomes non-trivial. We propose a solution
using state-of-the-art Deep Reinforcement Learning (DRL) techniques, to develop
autonomous agents capable of achieving this hybrid objective without having \`a
priori knowledge about the goal or the environment. Our results demonstrate the
viability of DRL in path-following and avoiding collisions toward achieving
human-level decision making in autonomous vehicle systems within extreme
obstacle configurations
Distributed Robust Learning-Based Backstepping Control Aided with Neurodynamics for Consensus Formation Tracking of Underwater Vessels
This paper addresses distributed robust learning-based control for consensus
formation tracking of multiple underwater vessels, in which the system
parameters of the marine vessels are assumed to be entirely unknown and subject
to the modeling mismatch, oceanic disturbances, and noises. Towards this end,
graph theory is used to allow us to synthesize the distributed controller with
a stability guarantee. Due to the fact that the parameter uncertainties only
arise in the vessels' dynamic model, the backstepping control technique is then
employed. Subsequently, to overcome the difficulties in handling time-varying
and unknown systems, an online learning procedure is developed in the proposed
distributed formation control protocol. Moreover, modeling errors,
environmental disturbances, and measurement noises are considered and tackled
by introducing a neurodynamics model in the controller design to obtain a
robust solution. Then, the stability analysis of the overall closed-loop system
under the proposed scheme is provided to ensure the robust adaptive performance
at the theoretical level. Finally, extensive simulation experiments are
conducted to further verify the efficacy of the presented distributed control
protocol
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