Fuzzy Broad Learning Adaptive Control for Voice Coil Motor Drivers

Hsu, Chun‐Fei; Chen, Bo‐Rui; Wu, Bing-Fei

doi:10.1007/s40815-021-01227-2

Cited by 6 publications

(1 citation statement)

References 32 publications

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“…Scholars have further improved the performance of micro linear motors by using highly magnetic materials, designing Halbach arrays instead of conventional pole structures, as well as applying superconducting materials to make windings [6][7][8]. The electromagnetic linear actuators, as an important development direction of linear motors, have become a research hotspot for high-performance linear servo control due to the advantages of cogging-free torque, high linearity, simple structure, and convenient control compared to conventional permanent magnet synchronous linear motors [9,10].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Non-Singular Terminal Sliding Mode Control Method for Electromagnetic Linear Actuator

Tan

et al. 2022

Micromachines

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In this study, to further improve the dynamic performance and steady-state accuracy of the electromagnetic linear actuator (EMLA) for the direct-drive system, the adaptive non-singular terminal sliding mode controller (ANTSMC) based on an improved disturbance observer (IDOB) is proposed. Accurate modeling of the EMLA based on the LuGre friction model was obtained by identification. Aiming at the continuous disturbance of the system, a non-singular terminal sliding mode control based on improved exponential reaching law (IERL) is designed, which is combined with the advantages of fuzzy control. Adaptive adjustment of the IERL gain is achieved to ensure finite time convergence of the tracking error while suppressing system jitter. Based on the IDOB to achieve effective observation compensation under noise disturbance and further improve the system robustness. The stability of the control is analyzed based on the Lyapunov function. The results show that the proposed method suppresses the jitter and improves the tracking performance and robustness of the servo system for different desired trajectories effectively.

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Section: Introductionmentioning

confidence: 99%

Adaptive Non-Singular Terminal Sliding Mode Control Method for Electromagnetic Linear Actuator

Tan

et al. 2022

Micromachines

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Hermite broad-learning recurrent neural control with adaptive learning rate for nonlinear systems

Hsu,

Chen

2023

Soft Comput

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Although conventional control systems are simple and widely used, they may not be effective for complex and uncertain systems. This study proposes a Hermite broad-learning recurrent neural network (HBRNN) to address such challenges. The HBRNN has a wide network structure and incorporates an internal feedback loop that enables fast learning and dynamic mapping. Furthermore, a Hermite broad-learning recurrent neural control (HBRNC) with the HBRNN as the main controller is proposed. All the network parameters of the HBRNN are updated online according to parameter learning laws through the gradient descent approach. To prevent network parameter overtraining of the HBRNN, an adaptive learning rate (ALR) is established using a discrete-type Lyapunov function to determine the least upper bound for the learning rate. The ALR can dynamically adjust the learning rates within specified ranges during the training process, thus achieving an appropriate balance between convergence speed and system stability. Finally, the HBRNC system with ALR is applied to a chaotic circuit and a reaction wheel pendulum, and its effectiveness is validated through simulation and experimentation.

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Intelligent Hermite Neural Control for Reaction Wheel Inverted Pendulums

Hsu

Chen

2023

2023 8th International Conference on Control and Robotics Engineering (ICCRE)

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Fuzzy Broad Learning Adaptive Control for Voice Coil Motor Drivers

Cited by 6 publications

References 32 publications

Adaptive Non-Singular Terminal Sliding Mode Control Method for Electromagnetic Linear Actuator

Adaptive Non-Singular Terminal Sliding Mode Control Method for Electromagnetic Linear Actuator

Hermite broad-learning recurrent neural control with adaptive learning rate for nonlinear systems

Intelligent Hermite Neural Control for Reaction Wheel Inverted Pendulums

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