Model Predictive Super-twisting sliding mode control for an autonomous surface vehicle

• Autorzy: Esfahani Hossein Nejatbakhsh , Szlapczynski Rafal

Identyfikatory

DOI

10.2478/pomr-2019-0057

• Języki publikacji: EN

Abstrakty

EN

This paper presents a new robust Model Predictive Control (MPC) algorithm for trajectory tracking of an Autonomous Surface Vehicle (ASV) in presence of the time-varying external disturbances including winds, waves and ocean currents as well as dynamical uncertainties. For fulfilling the robustness property, a sliding mode control-based procedure for designing of MPC and a super-twisting term are adopted. The MPC algorithm has been known as an effective approach for the implementation simplicity and its fast dynamic response. The proposed hybrid controller has been implemented in MATLAB / Simulink environment. The results for the combined Model Predictive Super-Twisting Sliding Mode Control (MP-STSMC) algorithm have shown that it significantly outperforms conventional MPC algorithm in terms of the transient response, robustness and steady state response and presents an effective chattering attenuation in comparison with the Super-Twisting Sliding Mode Control (STSMC) algorithm.

Słowa kluczowe

EN

utonomous Surface Vehicle; Model Predictive Control; Sliding Mode Control; Super-Twisting Algorithm; Chattering Attenuation

• Wydawca: Gdańsk University of Technology, Faculty of Ocean Engineering & Ship Technology
• Czasopismo: Polish Maritime Research
• Rocznik: 2019
• Tom: nr 3
• Strony: 163--171
• Opis fizyczny: Bibliogr. 23 poz., rys.

Twórcy

autor

Esfahani Hossein Nejatbakhsh

• Gdansk University of Technology Faculty of Ocean Engineering and Ship Technology, Department of Marine Mechatronics G. Narutowicza 11/12 80-233 Gdansk Poland

autor

Szlapczynski Rafal

• Gdansk University of Technology Faculty of Ocean Engineering and Ship Technology, Department of Marine Mechatronics G. Narutowicza 11/12 80-233 Gdansk Poland

Bibliografia

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Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).