High-Precision Position Control of PMLSM Using Fast Recursive Terminal Sliding Mode With Disturbance Rejection Ability

Wang, Lijun; Zhao, Jiwen; Yu, Zixiang; Pan, Zhenbao; Zheng, Zhilei

doi:10.1109/tii.2023.3295570

Cited by 5 publications

References 46 publications

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Design of Soft Microjoint to Improve Robotic Cell Micromanipulation Flexibility

Zhang,

Wang,

Chen

et al. 2024

Advanced Intelligent Systems

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Dexterity micromanipulation is a significant topic in the field of robotics. Herein, a novel robotic rotating microjoint controlled by an exogenous magnetic field based on magnetic programmable soft materials, which brings more dexterity to the micromanipulation task is proposed. First, the magnetic soft material is synthesized and the microjoint is manufactured. The maximum rotation angle and response time are characterized. Then the force analysis of the microjoint in a uniform magnetic field is performed and the relationship between the deformation bending angle of a microjoint and the magnetic field magnitude is first proposed. A sliding mode controller based on the deformation mechanism of microjoints and radial basis function neural network (DMRSMC) is proposed to achieve robust deformation control of the microjoint. The experiment that depicts the microjoint is able to achieve a maximum rotation angle of 22.16° and a response time of 16 ms. The DMRSMC controller performs angle control with higher accuracy than conventional SMC and PID controllers. Finally, a dexterous suction mechanism is developed by combining the microjoint with a soft micropipette needle to achieve dexterous grasping of zebrafish embryos with a 97% success rate and an orientation adjustment error is 1.36°.

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Design of Soft Microjoint to Improve Robotic Cell Micromanipulation Flexibility

Zhang,

Wang,

Chen

et al. 2024

Advanced Intelligent Systems

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Adaptive neural network-based sliding mode control for trajectory tracking control of cable-driven continuum robots with uncertainties

Chen,

Ming,

Qin

2024

Robotica

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In this paper, a novel fast nonsingular integral terminal sliding mode controller based on an adaptive neural network (ANN-FNITSMC) is proposed for the trajectory tracking control of cable-driven continuum robots (CDCRs) in complex underwater environments with uncertainties. First, a novel fast nonsingular integral terminal sliding mode control (FNITSMC) is designed to solve the chattering and singularity problems of the conventional terminal sliding mode control (TSMC). Second, an adaptive neural network (ANN) based on a radial basis function (RBF) is established to derive the uncertainties and compensate for the control input of CDCRs, enabling high-stable accuracy and strong robustness trajectory tracking in complex underwater environments. Simulation results are presented to demonstrate the high accuracy and strong robustness of the ANN-FNITSMC. Finally, the high accuracy, high stability, and strong robustness of the proposed trajectory tracking strategy are verified through an underwater experiment platform.

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Robust Control of Permanent Magnet Synchronous Linear Motor Using a General Order Model and Time Delay Estimation

Wang,

Zhao,

et al. 2023

2023 26th International Conference on Electrical Machines and Systems (ICEMS)

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High-Precision Position Control of PMLSM Using Fast Recursive Terminal Sliding Mode With Disturbance Rejection Ability

Cited by 5 publications

References 46 publications

Design of Soft Microjoint to Improve Robotic Cell Micromanipulation Flexibility

Design of Soft Microjoint to Improve Robotic Cell Micromanipulation Flexibility

Adaptive neural network-based sliding mode control for trajectory tracking control of cable-driven continuum robots with uncertainties

Robust Control of Permanent Magnet Synchronous Linear Motor Using a General Order Model and Time Delay Estimation

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