Abstract
In this paper, we propose a Nonlinear Model Predictive Control (NMPC) approach that is employed by an Autonomous Underwater Vehicle (AUV) to track and estimate a moving target using range measurements. Due to the nonlinearities in the observation model associated with range-only measurements, there exist state and input trajectories of the AUV that makes the position of the target unobservable. To address this problem, a standard stabilizing NMPC based approach augmented with an economic cost function is utilized to steer the system through highly observable trajectories in order to guarantee a good estimate of the position of the target. The efficacy of the proposed solution is demonstrated through simulations.
This project was supported by the European Unions Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 642153.
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Jain, R.P., Alessandretti, A., Aguiar, A.P., de Sousa, J.B. (2018). A Nonlinear Model Predictive Control for an AUV to Track and Estimate a Moving Target Using Range Measurements. In: Ollero, A., Sanfeliu, A., Montano, L., Lau, N., Cardeira, C. (eds) ROBOT 2017: Third Iberian Robotics Conference. ROBOT 2017. Advances in Intelligent Systems and Computing, vol 693. Springer, Cham. https://doi.org/10.1007/978-3-319-70833-1_14
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DOI: https://doi.org/10.1007/978-3-319-70833-1_14
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