Fuzzy active disturbance rejection control design for autonomous underwater vehicle manipulators system

Li, Xiaogang; Wang, JiaMeng

doi:10.1002/adc2.44

Cited by 6 publications

(7 citation statements)

References 43 publications

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“…Define the tracking error of the AUV's trajectory in the X ‐direction as design, the relationship between e x , ė x and parameters of LESO are described in fuzzy language and rules. To achieve efficient and precise control, the fuzzy rules are described in practical empirical‐models instead of simple linear and diagonal switching lines [25]. The fuzzy rules of ωc1 and ωo1 are tabulated in Tables 1 and 2, respectively.…”

Section: Methodsmentioning

confidence: 99%

Fuzzy linear extended states observer‐based iteration learning fault‐tolerant control for autonomous underwater vehicle trajectory‐tracking system

Hou

Wang

et al. 2022

IET Control Theory & Appl

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To deal with thrusters' faults of autonomous underwater vehicle (AUV), an iterative learning algorithm fault-tolerant control (FTC) based on the linear extended states observer (LESO) is proposed. In this control scheme, the non-linear feedback mechanism of the LESO is transplanted into iterative learning processes to estimate fault. Compared to our previous work, LESO is used to substitute classic non-linear extended state observer to make the establishment of the whole system more structured; moreover, the number of parameters need to be tuned can be reduced by the conception of observer bandwidth of LESO. To enhance the controllability and robustness of whole scheme, a new saturated sliding mode controller is proposed based on the Lyapunov theory. Then to achieve online parameter self-tuning for the control system, fuzzy logic controllers are introduced to find optimal relationship between LESO's parameter and tracking errors. The performance of the proposed controller is tested by some comparison experiments on Zhuhai A18D AUV; the results show that the proposed control scheme can ensure better stability than classical control and our previous control scheme when AUV suffers faults.

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Section: Methodsmentioning

confidence: 99%

Fuzzy linear extended states observer‐based iteration learning fault‐tolerant control for autonomous underwater vehicle trajectory‐tracking system

Hou

Wang

et al. 2022

IET Control Theory & Appl

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“…] T , which is parameterized for the observer gain, as defined in equation (36), which is obtained as follows:…”

Section: Analysis Of Error Dynamics the Tracking Error Vector Is Defi...mentioning

confidence: 99%

Designing, modeling and controlling the angular bending of a foldable soft actuator without using a curvature sensor

My Nu,

Viet,

Truyen

2024

Smart Mater. Struct.

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Soft actuators have recently attracted considerable attention owing to their inherent flexibility and adaptability. Nevertheless, for a soft robot to successfully engage with its surroundings and perform tasks with optimal effectiveness, it encounters a range of obstacles, including the need for precise and skillful movement, the capacity to perceive its own position and motion, and the ability to effectively regulate its flexible structures. Researchers have developed techniques to integrate curvature sensors onto flexible devices, enabling them to detect and react to their positions. However, the integration of curvature sensors into flexible structures presents a substantial challenge in the structural manufacturing process. To address these concerns, this article presents a technique for designing, dynamic modeling, and controlling the bending angle of foldable soft actuator without the need for curvature sensors. An optimal design for the geometric dimensions of the soft structure utilizing origami concepts to guarantee the requisite bending properties is suggested. A model-based control method that considers both the motion dynamic and the air dynamic is proposed for controlling the angular bending of the actuator. The motion dynamic was developed using the constant volume principle of the elastomer material and the neo-Hookean hyperelastic theory to establish the correlation between the applied pressure and bending angle. This dynamic model incorporates both the hyperelastic material characteristics of silicone rubber and the geometry of the actuator. Soft actuators have variations in the air chamber's volume during operation, and accurately measuring this variation is challenging. In order to tackle this problem, the fuzzy active disturbance rejection controller is used to predict these variations. The controller possesses exceptional position-tracking capability. This control strategy exhibited excellent responsiveness throughout the range of steady-state error values from approximately 1o to 2o. Removing the curvature sensor increases the longevity of this soft actuator and promotes the efficiency of the manufacturing process, hence enhancing the practical application possibilities for the soft actuator made from super elastic material.

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“… 15 In recent years, adaptive fuzzy control has been increasingly used in manipulator control and has achieved a series of results. 16 – 18 Hsu et al 19 combined fuzzy control and supervisory control to ensure the stability of the closed-loop system. Labiod 20 use fuzzy logic compensation system to adaptively compensate the inaccurate and external interference manipulator dynamic model, and use the two-degree-of-freedom or five-degree-of-freedom manipulator for simulation verification, the results show that the control strategy has good stability and robustness, and has good trajectory tracking position error convergence.…”

Section: Introductionmentioning

confidence: 99%

Research on trajectory tracking control of 7-DOF picking manipulator

Sun

Cao

2021

Science Progress

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In order to solve the problem of poor robustness of the traditional method of calculating torque in the mechanical model of 7-DOF picking manipulator, this paper proposes a control strategy of calculating torque plus fuzzy compensation by using adaptive fuzzy logic system to compensate the uncertain part of the mechanical model of 7-DOF picking manipulator. By using Lagrange method, the dynamic model of 7-DOF manipulator is established, and the relationship between joint motion and applied torque (force) is obtained. Using ADAMS and MATLAB to establish a co-simulation platform, the manipulator and trajectory tracking control system are simulated. The results show that the trajectory tracking error of each joint in the algorithm is obviously reduced and the convergence trend is obvious. The average trajectory tracking accuracy of joint 1 to joint 7 was improved by 70.22%, 94.78%, 0.62%, 74.23%, 89.78%, 86.45%, and 67.15%, respectively. In this control scheme, the control force (moment) of each joint changes regularly, and the output force (moment) does not appear chattering and mutation when the disturbance signal is added. The research results can provide support for the further study of picking manipulator trajectory tracking control system.

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Fuzzy active disturbance rejection control design for autonomous underwater vehicle manipulators system

Cited by 6 publications

References 43 publications

Fuzzy linear extended states observer‐based iteration learning fault‐tolerant control for autonomous underwater vehicle trajectory‐tracking system

Fuzzy linear extended states observer‐based iteration learning fault‐tolerant control for autonomous underwater vehicle trajectory‐tracking system

Designing, modeling and controlling the angular bending of a foldable soft actuator without using a curvature sensor

Research on trajectory tracking control of 7-DOF picking manipulator

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