Fuzzy PID Motion Control Based on Extended State Observer for AUV

Yin, Qingqing; Shen, Yue; Li, Hongjia; Wan, Junhe; Wang, Dianrui; Liu, Fengxu; Kong, Xiangrui; Yan, Tianhong

doi:10.1109/ut.2019.8734374

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…The merit of an automated control method for AUVs is directly related to whether the AUVs can successfully complete their tasks, which affects the safety performance of AUVs. The development of science and technology has led to more and more control methods being applied to the field of AUVs, such as PID [25], S-plane [26], adaptive [27], sliding mode [28], and other controllers. The design of controllers for marine vessels or robots usually needs to consider their multiple constraint terms [29], suffered from uncertainty disturbances [30,31], as well as control stability, robustness, etc., to design a suitable controller by combining various factors.…”

Section: Related Workmentioning

confidence: 99%

End-to-End AUV Local Motion Planning Method Based on Deep Reinforcement Learning

Lyu,

Sun,

Wang

et al. 2023

JMSE

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This study aims to solve the problems of sparse reward, single policy, and poor environmental adaptability in the local motion planning task of autonomous underwater vehicles (AUVs). We propose a two-layer deep deterministic policy gradient algorithm-based end-to-end perception–planning–execution method to overcome the challenges associated with training and learning in end-to-end approaches that directly output control forces. In this approach, the state set is established based on the environment information, the action set is established based on the motion characteristics of the AUV, and the control execution force set is established based on the control constraints. The mapping relations between each set are trained using deep reinforcement learning, enabling the AUV to perform the corresponding action in the current state, thereby accomplishing tasks in an end-to-end manner. Furthermore, we introduce the hindsight experience replay (HER) method in the perception planning mapping process to enhance stability and sample efficiency during training. Finally, we conduct simulation experiments encompassing planning, execution, and end-to-end performance evaluation. Simulation training demonstrates that our proposed method exhibits improved decision-making capabilities and real-time obstacle avoidance during planning. Compared to global planning, the end-to-end algorithm comprehensively considers constraints in the AUV planning process, resulting in more realistic AUV actions that are gentler and more stable, leading to controlled tracking errors.

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Section: Related Workmentioning

confidence: 99%

End-to-End AUV Local Motion Planning Method Based on Deep Reinforcement Learning

Lyu,

Sun,

Wang

et al. 2023

JMSE

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“…The realization of autonomous collision avoidance is crucial for ensuring AUV safety and completing autonomous control in unknown underwater environments, determining the quality and efficiency of task completion. The AUV autonomous control algorithms commonly used in the past include some traditional model-based control methods, such as proportional integral derivative (PID) [3]. However, with the rise of machine learning in recent years, some intelligent algorithms do not need to know the complex model of the AUV, nor do they need to have complete prior knowledge to achieve good control effects, such as reinforcement learning (RL).…”

Section: Introductionmentioning

confidence: 99%

AUV Obstacle Avoidance Framework Based on Event-Triggered Reinforcement Learning

Liu,

Ma,

Juan

2024

Electronics

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Autonomous Underwater Vehicles (AUVs), as a member of the unmanned intelligent ocean vehicle group, can replace human beings to complete dangerous tasks in the ocean. It is of great significance to apply reinforcement learning (RL) to AUVs to realize intelligent control. This paper proposes an AUV obstacle avoidance framework based on event-triggered reinforcement learning. Firstly, an environment perception model is designed to judge the relative position relationship between the AUV and all unknown obstacles and known targets. Secondly, considering that the detection range of AUVs is limited, and the proposed method needs to deal with unknown static obstacles and unknown dynamic obstacles at the same time, two different event-triggered mechanisms are designed. Soft actor–critic (SAC) with a non-policy sampling method is used. Then, improved reinforcement learning and the event-triggered mechanism are combined in this paper. Finally, a simulation experiment of the obstacle avoidance task is carried out on the Gazebo simulation platform. Results show that the proposed method can obtain higher rewards and complete tasks successfully. At the same time, the trajectory and the distance between each obstacle confirm that the AUV can reach the target well while maintaining a safe distance from static and dynamic obstacles.

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“…In [ 33 ], a linear-extended-state-observer (LESO-based backstepping controller is introduced for depth tracking of the underactuated AUV. In [ 34 ], a fuzzy PID (FPID) control system based on the extended state observer (ESO) is proposed for AUV. However, only fixed observer bandwidth is considered in the above-mentioned research studies.…”

Section: Introductionmentioning

confidence: 99%

Antidisturbance Control for AUV Trajectory Tracking Based on Fuzzy Adaptive Extended State Observer

Kang

Rong

Chou

2020

Sensors

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In this paper, an output-feedback fuzzy adaptive dynamic surface controller (FADSC) based on fuzzy adaptive extended state observer (FAESO) is proposed for autonomous underwater vehicle (AUV) systems in the presence of external disturbances, parameter uncertainties, measurement noises and actuator faults. The fuzzy logic system is incorporated into both the observers and controllers to improve the adaptability of the entire system. The dynamics of the AUV system is established first, considering the external disturbances and parameter uncertainties. Based on the dynamic models, the ESO, combined with a fuzzy logic system tuning the observer bandwidth, is developed to not only adaptively estimate both system states and the lumped disturbances for the controller, but also reduce the impact of measurement noises. Then, the DSC, together with fuzzy logic system tuning the time constant of the low-pass filter, is designed using estimations from the FAESO for the AUV system. The asymptotic stability of the entire system is analyzed through Lyapunov’s direct method in the time domain. Comparative simulations are implemented to verify the effectiveness and advantages of the proposed method compared with other observers and controllers considering external disturbances, parameter uncertainties and measurement noises and even the actuator faults that are not considered in the design process. The results show that the proposed method outperforms others in terms of tracking accuracy, robustness and energy consumption.

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Fuzzy PID Motion Control Based on Extended State Observer for AUV

Cited by 5 publications

References 6 publications

End-to-End AUV Local Motion Planning Method Based on Deep Reinforcement Learning

End-to-End AUV Local Motion Planning Method Based on Deep Reinforcement Learning

AUV Obstacle Avoidance Framework Based on Event-Triggered Reinforcement Learning

Antidisturbance Control for AUV Trajectory Tracking Based on Fuzzy Adaptive Extended State Observer

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