An adaptive control law for controlled Lagrangian particle tracking

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.

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