Abstract
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.
Highlights
Autonomous underwater vehicles (AUVs) have been widely used for various purposes, e.g., military, commercial, and marine scientific survey applications [1]
The AUV is positioned at the initial point [100, 100, 300] m and is controlled to reach the destination [600, 600, 300] m along the following curved reference path xd(t) = 600 − 500 cos(πt/1200), yd(t) = 100 + 500 sin(πt/1200), for t ∈ [0, 600] s
The larger tracking errors are partly due to the modeling errors, i.e., the error between the model used for the controller design and the model used for simulations
Summary
Autonomous underwater vehicles (AUVs) have been widely used for various purposes, e.g., military, commercial, and marine scientific survey applications [1]. Measurement noises play an important role in the localization accuracy Improvement techniques such as the least square method and Kalman filter [7] have been proposed to overcome this issue. With the information on localization and ocean current, the controller computes the input to the AUV system so as to achieve certain tasks. Lyapunov-based techniques such as backstepping [19] and sliding mode control [20] are frequently used in AUV control to handle nonlinearities and guarantee convergence These methods are often associated with tedious tuning procedures, and the control performance differs when the environment, load, or tasks change. A novel integrated acoustic localization and predictive tracking control approach is proposed for AUVs; 2.
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