Intelligent Supervisory Switching Control of Unmanned Surface Vehicles

novel approach to extend the decision-making capabilities of unmanned surface vehicles
(USVs) is presented in this work. A multi-objective framework is described where separate
controllers command different behaviors according to a desired trajectory. Three behaviors
are examined – transiting, station-keeping and reversing. Given the desired trajectory, the
vehicle is able to autonomously recognize which behavior best suits a portion of the
trajectory. The USV uses a combination of a supervisory switching control structure and a
reinforcement learning algorithm to create a hybrid deliberative and reactive approach to
switch between controllers and actions. Reinforcement learning provides a deliberative
method to create a controller switching policy, while supervisory switching control acts
reactively to instantaneous changes in the environment. Each action is restricted to one
controller. Due to the nonlinear effects in these behaviors, two underactuated backstepping
controllers and a fully-actuated backstepping controller are proposed for each transiting, reversing and station-keeping behavior, respectively, restricted to three degrees of freedom.
Field experiments are presented to validate this system on the water with a physical USV
platform under Sea State 1 conditions. Main outcomes of this work are that the proposed
system provides better performance than a comparable gain-scheduled nonlinear controller
in terms of an Integral of Absolute Error metric. Additionally, the deliberative component
allows the system to identify dynamically infeasible trajectories and properly
accommodate them.