Please use this identifier to cite or link to this item: https://hdl.handle.net/10216/116343
Author(s): Rui Gomes
Fernando Lobo Pereira
Title: Attainable-Set Model Predictive Control for AUV Formation Control
Issue Date: 2018-11-06
Abstract: In this article, we focus on the motion control of an AUV formation in order to track a given path along which data will be gathered. A computationally efficient architecture enables the conciliation of onboard resources optimization with state feedback control - to deal with the typical a priori high uncertainty - while managing the formation with a low computational and power budgets. To meet these very strict requirements, a novel Model Predictive Control (MPC) scheme is used. The key idea is to pre-compute data which is known to be time invariant for a number of likely scenarios and store it on-board in appropriate look-up tables. Then, as the mission proceeds, sampled motion sensor data, and communicated data is processed in each one of the AUVs and fed to the onboard proposed MPC scheme implemented with the dynamics of the formation that, by combining with information extracted from the pertinent on-board look-up tables, determine the best control action with inexpensive computational operations.
URI: https://hdl.handle.net/10216/116343
Source: AUV 2018 - 2018 IEEE/OES Autonomous Underwater Vehicle Workshop, Proceedings
Document Type: Artigo em Livro de Atas de Conferência Internacional
Rights: openAccess
Appears in Collections:FEUP - Artigo em Livro de Atas de Conferência Internacional

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