Abstract
To enable underwater manipulators with long-lasting autonomy, designing an energy efficient controller is of utmost importance. In this regard, an optimal control technique is a suitable approach as the cost function allows optimization for different metrics, such as energy consumption, minimum velocity changes, or zero position errors. However, the need for an accurate model makes optimal control strategies less enticing for underwater systems where models are difficult to obtain due to unknown dynamics. A solution for this limitation is the usage of data driven techniques for model prediction, as they solely rely on the observed behaviour of the system for generating dynamic models. In this paper, we study the capabilities of a data driven model predictive controller for energy-efficient underwater manipulation tasks. A data driven model of the underwater manipulator based on a neural network is integrated into the formulation of a well known Model Predictive Control (MPC). The proposed architecture is implemented on a four Degrees-of-Freedom (DOF) underwater manipulator in a simulated environment and the results are presented in comparison with a classical MPC controller, showcasing the benefits of the proposed data driven strategy.
Original language | English |
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Title of host publication | 2020 Global Oceans 2020 |
Subtitle of host publication | Singapore - U.S. Gulf Coast |
Publisher | IEEE |
ISBN (Electronic) | 9781728154466 |
DOIs | |
Publication status | Published - 9 Apr 2021 |
Event | 2020 Global Oceans: Singapore - U.S. Gulf Coast - Biloxi, United States Duration: 5 Oct 2020 → 30 Oct 2020 |
Conference
Conference | 2020 Global Oceans |
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Abbreviated title | OCEANS 2020 |
Country/Territory | United States |
City | Biloxi |
Period | 5/10/20 → 30/10/20 |
Keywords
- Energy efficiency
- Intelligent control
- Model Predictive Control
- Neural Networks
- Underwater manipulation
ASJC Scopus subject areas
- Oceanography
- Automotive Engineering
- Instrumentation
- Signal Processing