An adaptive control law for controlled Lagrangian particle tracking

Abstract

Controlled Lagrangian particle tracking (CLPT) is a method that evaluates the accuracy of ocean models employed for the navigation of autonomous underwater vehicles (AUVs). The accuracy of ocean models can be represented by the discrepancy between the predicted and true trajectories of AUVs, called controlled Lagrangian prediction error (CLPE). To reduce CLPE, we develop an adaptive control law that enables AUVs to follow the predicted trajectory in the true flow field. Because CLPE is exponentially increasing and navigation performance is significantly degraded when previous controllers are used, we propose the adaptive control law that makes CLPE converges to zero. Although true flows are unknown, the proposed control law identifies the true flow field so that AUVs follows the predicted trajectory. We prove that CLPE is ultimately bounded under bounded disturbances. The proposed control law is verified by simulation results.

Bounded Cost Path Planning for Underwater Vehicles Assisted by a Time-Invariant Partitioned Flow Field Model

“…The term e x − z is the controlled Lagrangian Localization error (CLLE, Cho and Zhang, 2016;Cho et al, 2021), which describes how much the actual trajectory deviates from the estimated trajectory. A learning algorithm will then compute β(t), ξ(t), V L (t) so that the CLLE can be reduced.…”