C3D: Cascade Control with Change Point Detection and Deep Koopman Learning for Autonomous Surface Vehicles
CoRR(2024)
Abstract
In this paper, we discuss the development and deployment of a robust
autonomous system capable of performing various tasks in the maritime domain
under unknown dynamic conditions. We investigate a data-driven approach based
on modular design for ease of transfer of autonomy across different maritime
surface vessel platforms. The data-driven approach alleviates issues related to
a priori identification of system models that may become deficient under
evolving system behaviors or shifting, unanticipated, environmental influences.
Our proposed learning-based platform comprises a deep Koopman system model and
a change point detector that provides guidance on domain shifts prompting
relearning under severe exogenous and endogenous perturbations. Motion control
of the autonomous system is achieved via an optimal controller design. The
Koopman linearized model naturally lends itself to a linear-quadratic regulator
(LQR) control design. We propose the C3D control architecture Cascade Control
with Change Point Detection and Deep Koopman Learning. The framework is
verified in station keeping task on an ASV in both simulation and real
experiments. The approach achieved at least 13.9 percent improvement in mean
distance error in all test cases compared to the methods that do not consider
system changes.
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