Data-driven trajectory tracking control for autonomous underwater vehicle based on iterative extended state observer

Chengxi Wu; Yuewei Dai; Liang Shan; Zhiyu Zhu; Zhengtian Wu; Chengxi Wu; Yuewei Dai; Liang Shan; Zhiyu Zhu; Zhengtian Wu

doi:10.3934/mbe.2022140

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 3: 3036-3055. doi: 10.3934/mbe.2022140

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Data-driven trajectory tracking control for autonomous underwater vehicle based on iterative extended state observer

1.
School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China
2.
School of Electronic and Information, Jiangsu University of Science and Technology, Zhenjiang 212100, China
3.
School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China

Academic Editor: Hamid Reza Karimi

Received: 11 October 2021 Revised: 22 December 2021 Accepted: 09 January 2022 Published: 18 January 2022

In this study, we explore the precise trajectory tracking control problem of autonomous underwater vehicle (AUV) under the disturbance of the underwater environment. First, a model-free adaptive control (MFAC) is designed based on data-driven ideology and a full-form dynamic linearization (FFDL) method is utilized to online estimate time-varying parameter pseudo gradient (PG) to establish an equivalent data model of AUV motion. Second, the iterative extended state observer (IESO) scheme is designed to combine with FFDL-MFAC. Because the proposed novel controller is able to learn from repeated iterations, the proposed novel controller can estimate and compensate the model approximation error produced by external environmental unknown disturbance. Third, three-dimensional motion is decoupled into horizontal and vertical and a multi closed-loop control structure is designed that exhibits faster convergence rate and reduces sensitivity to parameter jumps than single closed-loop system. Finally, two simulation scenarios are designed featuring non external disturbance and Gaussian noise of signal-to-noise ratio of 90 dB. The simulation results reveal the superiority of FFDL. Furthermore, we adpot the technical parameters data of T-SEA I AUV to conduct numerical simulation, aunderwater trajectory as the tracking scenario and set waves to 0.5 m and current to 0.2 m/s to simulate Lv.2 ocean conditions of "International Ocean State Standard". The simulation results demonstrate the effectiveness and robustness of the proposed tracking control algorithm.
- autonomous underwater vehicle,
- model-free adaptive control,
- full-form dynamic linearization,
- pseudo gradient,
- iterative extended state observer,
- trajectory tracking
Citation: Chengxi Wu, Yuewei Dai, Liang Shan, Zhiyu Zhu, Zhengtian Wu. Data-driven trajectory tracking control for autonomous underwater vehicle based on iterative extended state observer[J]. Mathematical Biosciences and Engineering, 2022, 19(3): 3036-3055. doi: 10.3934/mbe.2022140

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Abstract

In this study, we explore the precise trajectory tracking control problem of autonomous underwater vehicle (AUV) under the disturbance of the underwater environment. First, a model-free adaptive control (MFAC) is designed based on data-driven ideology and a full-form dynamic linearization (FFDL) method is utilized to online estimate time-varying parameter pseudo gradient (PG) to establish an equivalent data model of AUV motion. Second, the iterative extended state observer (IESO) scheme is designed to combine with FFDL-MFAC. Because the proposed novel controller is able to learn from repeated iterations, the proposed novel controller can estimate and compensate the model approximation error produced by external environmental unknown disturbance. Third, three-dimensional motion is decoupled into horizontal and vertical and a multi closed-loop control structure is designed that exhibits faster convergence rate and reduces sensitivity to parameter jumps than single closed-loop system. Finally, two simulation scenarios are designed featuring non external disturbance and Gaussian noise of signal-to-noise ratio of 90 dB. The simulation results reveal the superiority of FFDL. Furthermore, we adpot the technical parameters data of T-SEA I AUV to conduct numerical simulation, aunderwater trajectory as the tracking scenario and set waves to 0.5 m and current to 0.2 m/s to simulate Lv.2 ocean conditions of "International Ocean State Standard". The simulation results demonstrate the effectiveness and robustness of the proposed tracking control algorithm.

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