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Model-Free Adaptive Disturbance Rejection Control of An RSV With Hardware-in-The-Loop Experiments | IEEE Journals & Magazine | IEEE Xplore

Model-Free Adaptive Disturbance Rejection Control of An RSV With Hardware-in-The-Loop Experiments


Abstract:

This letter reports a model-free adaptive disturbance rejection control method for the surge velocity tracking of a robotic surface vehicle (RSV) in the presence of unkno...Show More

Abstract:

This letter reports a model-free adaptive disturbance rejection control method for the surge velocity tracking of a robotic surface vehicle (RSV) in the presence of unknown external disturbances, internal dynamics, and control coefficient. Specifically, reduced-order and full-order filtering adaptive extended state observers (FAESOs) are proposed at first, for estimating the unknown control coefficient, and the lumped disturbance consisting of the unknown external disturbances and internal dynamics, simultaneously. Based on the FAESO, a model-free adaptive disturbance rejection surge velocity tracking control law is derived. A key advantage of the proposed method is that it results in uniform control performance within large variation range of control coefficient regardless of the fully unknown model. The efficacy of the developed model-free adaptive disturbance rejection control method for the surge velocity tracking of RSV is substantiated by comparing simulations and hardware-in-the-loop experiments.
Published in: IEEE Transactions on Industrial Electronics ( Volume: 70, Issue: 7, July 2023)
Page(s): 7507 - 7510
Date of Publication: 30 August 2022

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I. Introduction

Robotic surface vehicle (RSV) is a marine robot capable of accomplishing various tasks without human operations with available sensors onboard [1], [2], [3]. In order to accomplish various tasks autonomously, a reliable and high-performance motion control system is vital. The motion stability is vulnerable to unknown internal dynamics and external ocean disturbances due to wind, waves, ocean currents, and operational equipments [4]. In order to achieve high-performance control in the presence of the external disturbances and internal dynamics, a variety of effective methods are proposed, ranging from robust control [5], [6], adaptive control [7], intelligent control, disturbance-observer-based control [8], to active disturbance rejection control (ADRC) [9].

References

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