Insights into a data driven optimal control for energy efficient manipulation

Ignacio Carlucho, Dylan W. Stephens, Corina Barbalata

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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 languageEnglish
Title of host publication2020 Global Oceans 2020
Subtitle of host publicationSingapore - U.S. Gulf Coast
ISBN (Electronic)9781728154466
Publication statusPublished - 9 Apr 2021
Event2020 Global Oceans: Singapore - U.S. Gulf Coast - Biloxi, United States
Duration: 5 Oct 202030 Oct 2020


Conference2020 Global Oceans
Abbreviated titleOCEANS 2020
Country/TerritoryUnited States


  • Energy efficiency
  • Intelligent control
  • Model Predictive Control
  • Neural Networks
  • Underwater manipulation

ASJC Scopus subject areas

  • Oceanography
  • Automotive Engineering
  • Instrumentation
  • Signal Processing


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