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Cited by 117Underwater Vehicles and Communication SystemsAdvanced Control Systems OptimizationControl and Dynamics of Mobile Robots

A Robust Predictive Control Approach for Underwater Robotic Vehicles

Heshmati-Alamdari, Shahab, Karras, George C., Marantos, Panos, Kyriakopoulos, Kostas J.·2020·IEEE Transactions on Control Systems Technology
DOI 10.1109/tcst.2019.2939248
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Abstract

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.

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Crossref · 10.1109/tcst.2019.2939248

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44 of 44 references verified0 citation errors · 0 not checkable

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  • The cumulative error bound in this article does not increase as N increases.

    “Different from the cumulative error bound ( Ls ( L f N -1 -1 / L f -1 ) + Lt L f N -1 ) d (where L f , Ls , and Lt are Lipschitz constants and d is the upper bound of the disturbance) derived in [30] and [31], the cumulative error bound in this article N := ( K ( 1 + K 0 P )/ 1 - √ ρ) √ (λ max ( P )λ max ( Q )/λ min ( Q )) ¯ ∗”

    From Incremental Model Predictive Control Exploiting Time-Delay Estimation for a Robot Manipulator (2022) · cites this paper as [31]

  • In [30] and [31], ISS of MPC with terminal ingredients was analyzed using a feasible control sequence, but the derived cumulative error bound increases with increasing prediction horizon, resulting in an overconservative cumulative error bound.

    “In [30] and [31], ISS of MPC with terminal ingredients was analyzed where a feasible control sequence, which guarantees that input and state constraints are not violated, was employed. However, the derived cumulative error bound increases with increasing the prediction horizon. It results in an overconservative cumulative error bound”

    From Incremental Model Predictive Control Exploiting Time-Delay Estimation for a Robot Manipulator (2022) · cites this paper as [31]

  • In contrast to ISS analyses in [30] and [31], feasible control sequences are not required in this article.

    “In contrast to ISS analyses in [30] and [31], feasible control sequences are not required in this article.”

    From Incremental Model Predictive Control Exploiting Time-Delay Estimation for a Robot Manipulator (2022) · cites this paper as [31]

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Authors
Heshmati-Alamdari, S., Karras, G. C., Marantos, P., & Kyriakopoulos, K. J.
Affiliations
  • KTH Royal Institute of Technology (SE)
  • University of Thessaly (GR)
  • National Technical University of Athens (GR)
ORCID
Venue
IEEE Transactions on Control Systems Technology
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2020-11
Volume
28
Issue
6
Pages
2352-2363
ISSN
  • 1063-6536 (ISSN-L)
  • 1558-0865
  • 2374-0159
DOI
10.1109/tcst.2019.2939248
OpenAlex
W2978951642
Cited by
117
References
44
Citations by year
2026: 13 · 2025: 24 · 2024: 20 · 2023: 18 · 2022: 14 · 2021: 15 · 2020: 10 · 2019: 2 · 2018: 1
FWCI
7.04
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • IEEE Transactions on Control Systems Technology (publishedVersion)
  • University of Thessaly Institutional Repository (University of Thessaly) (submittedVersion)
Funders
Knut och Alice Wallenberg Academy (KAW) Fellow (2015.0216)
Topics
  • Physical Sciences › Engineering › Ocean Engineering › Underwater Vehicles and Communication Systems
  • Physical Sciences › Engineering › Control and Systems Engineering › Advanced Control Systems Optimization
  • Physical Sciences › Engineering › Control and Systems Engineering › Control and Dynamics of Mobile Robots
Keywords
underwater robotic vehicles, nonlinear model predictive control, robust control, autonomous navigation, obstacle avoidance, state and input constraints, ocean currents, energy consumption reduction, parameter uncertainty, constrained workspace

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