Adaptive reinforcement learning fault-tolerant control for AUVs With thruster faults based on the integral extended state observer

Li, Zhifu; Wang, Ming; Ma, Ge; Zou, Tao

doi:10.1016/j.oceaneng.2023.113722

Cited by 15 publications

(3 citation statements)

References 35 publications

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“…The proof of Lemma 1 using the assumptions in Equation ( 21) and the two Lyapunov functions in Equation ( 23) is similar to that in [32]. This work will directly use this conclusion.…”

Section: Actor-critic Networkmentioning

confidence: 63%

Reinforcement-Learning-Based Visual Servoing of Underwater Vehicle Dual-Manipulator System

Wang,

Gao

2024

JMSE

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As a substitute for human arms, underwater vehicle dual-manipulator systems (UVDMSs) have attracted the interest of global researchers. Visual servoing is an important tool for the positioning and tracking control of UVDMSs. In this paper, a reinforcement-learning-based adaptive control strategy for the UVDMS visual servo, considering the model uncertainties, is proposed. Initially, the kinematic control is designed by developing a hybrid visual servo approach using the information from multi-cameras. The command velocity of the whole system is produced through a task priority method. Then, the reinforcement-learning-based velocity tracking control is developed with a dynamic inversion approach. The hybrid visual servoing uses sensors equipped with UVDMSs while requiring fewer image features. Model uncertainties of the coupled nonlinear system are compensated by the actor–critic neural network for better control performances. Moreover, the stability analysis using the Lyapunov theory proves that the system error is ultimately uniformly bounded (UUB). At last, the simulation shows that the proposed control strategy performs well in the task of dynamical positioning.

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“…The proof of Lemma 1 using the assumptions in Equation ( 21) and the two Lyapunov functions in Equation ( 23) is similar to that in [32]. This work will directly use this conclusion.…”

Section: Actor-critic Networkmentioning

confidence: 63%

Reinforcement-Learning-Based Visual Servoing of Underwater Vehicle Dual-Manipulator System

Wang,

Gao

2024

JMSE

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“…To validate the effectiveness and benefits of our proposed approach, we conduct a comparative results of PMSM. Two other robust control schemes are taken to simulate for comparisons with our proposed method, that is, the FOSMCC (10) and NFOSMCC (15) control schemes. The primary PMSM parameters utilized in the comparatives are presented in Table 2.…”

Section: Comparative Results and Discussionmentioning

confidence: 99%

“…As a robust feedforward control method, the nonlinear extended state observer (ESO) was proposed by Han. 6,7 Because of its ease of selecting parameters and high estimation accuracy, the nonlinear ESO has been designed and implemented in various applications such as active hydro-pneumatic suspension, 8 agricultural quadrotor UAV, 9 autonomous underwater vehicles, 10 airships, 11 and so on. After years of progress, the sliding mode control (SMC) strategy has emerged as a leading variable structure control method within the control field.…”

Section: The Development Of the Current Technologymentioning

confidence: 99%

One new composite control based smooth nonlinear fractional order sliding mode algorithm and disturbance compensation for PMSM with parameter uncertainties

Gao,

Pan,

Zhu

2023

Advances in Mechanical Engineering

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To achieve high-quality control of PMSM with parameter uncertainties, a nonlinear smooth fractional order sliding mode composite control (NSFOSMCC) strategy is designed in this paper. This approach incorporates a novel nonlinear smooth fractional order sliding mode controller, a novel adaptive super-twisting (AST) algorithm, and a modified nonlinear extended state observer (ESO) to achieve enhanced motor performance. To effectively overcome the disadvantages of simplicity and toughness associated with the conventional fractional order sliding mode controller, a novel nonlinear smooth fractional order sliding mode controller is designed. A novel AST technique is introduced to effectively weaken chattering issues. The modified nonlinear ESO is used to estimate the uncertainty parameters of the PMSM. By combining the modified nonlinear ESO with the latest sliding mode controller, a new composite controller called NSFOSMCC strategy is developed for PMSM. The proposed NSFOSMCC strategy, utilizing a combination of “feedforward plus feedback,” is implemented for PMSM with parameter uncertainties. The reliability and stability of the controller are thoroughly established through the utilization of the Lyapunov stability theorem. Furthermore, comparative results show strong evidence supporting the effectiveness of the novel composite control strategy.

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Low-complexity formation control of marine vehicle system based on prescribed performance

Xie,

Wu,

Huang

2024

Nonlinear Dyn

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Adaptive reinforcement learning fault-tolerant control for AUVs With thruster faults based on the integral extended state observer

Cited by 15 publications

References 35 publications

Reinforcement-Learning-Based Visual Servoing of Underwater Vehicle Dual-Manipulator System

Reinforcement-Learning-Based Visual Servoing of Underwater Vehicle Dual-Manipulator System

One new composite control based smooth nonlinear fractional order sliding mode algorithm and disturbance compensation for PMSM with parameter uncertainties

Low-complexity formation control of marine vehicle system based on prescribed performance

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