Speed tracking of Brushless DC motor based on deep reinforcement learning and PID

Lu, Puwei; Huang, Wenmei; Xiao, Junlong

doi:10.1109/cmmno53328.2021.9467649

Cited by 14 publications

(1 citation statement)

References 8 publications

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“…After an adaptive PID controller is trained, its parameters change according to the changing state, achieving impressive performance. [29][30][31][32] In a previous study, 33) we combined DRL with a PID controller by integrating the A2C algorithm into the PID framework.The DRL-PID controller has been tested in the simulation environment and the actual machine with simple stepwise responses. The present study measures and analyzes the USLM's dynamic characteristics and extends the application of the DRL-PID controller for both position control and speed control, showing dynamic, versatile, and self-adjusting capabilities.…”

Section: Introductionmentioning

confidence: 99%

Dynamic characteristics and deep reinforcement learning of proportional-integral-differential controller for quadruped stator-based ultrasonic linear motor

Jiang,

Wang,

Sasamura

et al. 2024

Jpn. J. Appl. Phys.

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Ultrasonic linear motors have many advantages, such as small size, light weight, and a lack of electro-magnetic interference. In our previous study, a quadruped stator-based ultrasonic linear motor that mimics the motion of an inchworm based on longitudinal and bending modes was proposed. However, our previous studies focused on voltage control, overlooking the influence of phase, which made accurate and sustainable operation difficult. This study measures the dynamic characteristics of this motor using polymer-based rods and combines deep reinforcement learning (DRL) with proportional-integral-differential (PID) algorithms for both position and speed control through phase manipulation. The proposed DRL-PID controller outperforms voltage control in both simulation and real-world experiments.

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Section: Introductionmentioning

confidence: 99%