Tracking Control of Autonomous Underwater Vehicles with Acoustic Localization and Extended Kalman Filter

The absence of global positioning system (GPS) signals and the influence of ocean currents are two of the main challenges facing the autonomy of autonomous underwater vehicles (AUVs). This paper proposes an acoustic localization-based tracking control method for AUVs. Particularly, three buoys that emit acoustic signals periodically are deployed over the surface. Times of arrivals of these acoustic signals at the AUV are then obtained and used to calculate an estimated position of the AUV. Moreover, the uncertainties involved in the localization and ocean currents are handled together in the framework of the extended Kalman filter. To deal with system physical constraints, model predictive control relying on online repetitive optimizations is applied in the tracking controller design. Furthermore, due to the different sampling times between localization and control, the dead-reckoning technique is utilized considering detailed AUV dynamics. To avoid using the highly nonlinear and complicated AUV dynamics in the online optimizations, successive linearizations are employed to achieve a trade-off between computational complexity and control performance. Simulation results show that the proposed algorithms are effective and can achieve the AUV tracking control goals.

Automated summary

This paper proposes an acoustic localization-based tracking control method for AUVs to address the challenges of GPS signal absence and ocean currents' influence. The method involves deploying buoys emitting acoustic signals, using extended Kalman filter for handling uncertainties, and applying model predictive control and dead-reckoning technique for tracking controller design. Successive linearizations are employed to balance computational complexity and control performance, showing effectiveness in achieving AUV tracking control goals in simulation results.