A Robust Predictive Control Approach for Underwater Robotic Vehicles

Shahab Heshmati-Alamdari, George C. Karras, Panos Marantos, Kostas J. Kyriakopoulos

Research output: Contribution to journalArticlepeer-review


This article presents a robust nonlinear model predictive control (NMPC) scheme for autonomous navigation of underwater robotic vehicles operating in a constrained workspace including the static obstacles. In particular, the purpose of the controller is to guide the vehicle toward specific way points with guaranteed input and state constraints. Various constraints, such as obstacles, workspace boundaries, predefined upper bounds for the velocity of the robotic vehicle, and thruster saturations, are considered during the control design. Moreover, the proposed control scheme is designed at dynamic level, and it incorporates the full dynamics of the vehicle in which the ocean currents are also involved. Hence, taking the thrusts as the control inputs of the robotic system and formulating them accordingly, the vehicle exploits the ocean current dynamics when these are in favor of the way-point tracking mission, resulting in reduced energy consumption by the thrusters. The robustness of the closed-loop system against parameter uncertainties has been analytically guaranteed with convergence properties. The performance of the proposed control strategy is experimentally verified using a 4 degrees of freedom (DoF) underwater robotic vehicle inside a constrained test tank with sparse static obstacles.

Original languageEnglish (US)
Article number8858028
Pages (from-to)2352-2363
Number of pages12
JournalIEEE Transactions on Control Systems Technology
Issue number6
StatePublished - Nov 2020


  • Autonomous underwater vehicles (AUVs)
  • model predictive control (MPC)
  • robust motion control

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering


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