Robust State/Output-Feedback Control of Robotic Manipulators: An Adaptive Fuzzy-Logic-Based Approach With Self-Organized Membership Functions

Yilmaz, Bayram Melih; Tatlıcıoğlu, Enver; Savran, Aydoğan; Alcı, Musa

doi:10.1109/tsmc.2022.3224255

Cited by 2 publications

(2 citation statements)

References 34 publications

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“…Takagi-Sugeno-Kang adaptive fuzzy system control is applied to the equivalent part, whereas adaptive PI control is used for the robust segment. Yilmaz et al (2022) addressed the modeling uncertainty of the manipulator by using a self-organized adaptive fuzzy-logic (AFL)-based controller. They designed a high-gain joint velocity observer to derive a self-organizing AFL-based robust output feedback controller.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy logic system-based force tracking control of robot in highly dynamic environments

Liu,

Li,

et al. 2024

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Purpose The study aims to equip robots with the ability to precisely maintain interaction forces, which is crucial for tasks such as polishing in highly dynamic environments with unknown and varying stiffness and geometry, including those found in airplane wings or thin, soft materials. The purpose of this study is to develop a novel adaptive force-tracking admittance control scheme aimed at achieving a faster response rate with higher tracking accuracy for robot force control. Design/methodology/approach In the proposed method, the traditional admittance model is improved by introducing a pre-proportional-derivative controller to accelerate parameter convergence. Subsequently, the authors design an adaptive law based on fuzzy logic systems (FLS) to compensate for uncertainties in the unknown environment. Stability conditions are established for the proposed method through Lyapunov analysis, which ensures the force tracking accuracy and the stability of the coupled system consisting of the robot and the interaction environment. Furthermore, the effectiveness and robustness of the proposed control algorithm are demonstrated by simulation and experiment. Findings A variety of unstructured simulations and experimental scenarios are designed to validate the effectiveness of the proposed algorithm in force control. The outcomes demonstrate that this control strategy excels in providing fast response, precise tracking accuracy and robust performance. Practical implications In real-world applications spanning industrial, service and medical fields where accurate force control by robots is essential, the proposed method stands out as both practical and straightforward, delivering consistently satisfactory performance across various scenarios. Originality/value This research introduces a novel adaptive force-tracking admittance controller based on FLS and validated through both simulations and experiments. The proposed controller demonstrates exceptional performance in force control within environments characterized by unknown and varying.

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Section: Introductionmentioning

confidence: 99%

Fuzzy logic system-based force tracking control of robot in highly dynamic environments

Liu,

Li,

et al. 2024

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“…Afterward, aiming at the tracking control of the end-effector for manipulators, an FIS-based controller is designed by Yilmaz et al ( 2022 ), in which the centers and widths of the membership functions are adjusted adaptively, thus promoting the learning power of the controller. Recently, Yilmaz et al ( 2023 ) devised an FIS-based output-feedback controller for the joint space tracking of manipulators, in which the demands for joint velocity and knowledge of manipulators are eliminated.…”

Section: Introductionmentioning

confidence: 99%

Online learning fuzzy echo state network with applications on redundant manipulators

Li,

Liu,

Gao

2024

Front. Neurorobot.

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Redundant manipulators are universally employed to save manpower and improve work efficiency in numerous areas. Nevertheless, the redundancy makes the inverse kinematics of manipulators hard to address, thus increasing the difficulty in instructing manipulators to perform a given task. To deal with this problem, an online learning fuzzy echo state network (OLFESN) is proposed in the first place, which is based upon an online learning echo state network and the Takagi–Sugeno–Kang fuzzy inference system (FIS). Then, an OLFESN-based control scheme is devised to implement the efficient control of redundant manipulators. Furthermore, simulations and experiments on redundant manipulators, covering UR5 and Franka Emika Panda manipulators, are carried out to verify the effectiveness of the proposed control scheme.

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Robust State/Output-Feedback Control of Robotic Manipulators: An Adaptive Fuzzy-Logic-Based Approach With Self-Organized Membership Functions

Cited by 2 publications

References 34 publications

Fuzzy logic system-based force tracking control of robot in highly dynamic environments

Fuzzy logic system-based force tracking control of robot in highly dynamic environments

Online learning fuzzy echo state network with applications on redundant manipulators

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