Adaptive workload adjustment for cyber-physical systems using deep reinforcement learning

Xu, Shikang; Koren, Israel; Krishna, C.M.

doi:10.1016/j.suscom.2021.100525

Cited by 4 publications

(1 citation statement)

References 9 publications

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“…The cyber subsystem is located in the feedback loop of the electric vehicle drive motor. The cyber subsystem drives actuators and the controlled drive motor based on the current states and inputs of the sensors [1,2]. The permanent magnet synchronous motor, especially interior permanent magnet synchronous machines (IPMSM), has become the ideal driving motor for EVs due to its advantages such as high efficiency, high power factor, wide operating speed range, high torque to the current ratio, and robustness.…”

Section: Introductionmentioning

confidence: 99%

A Control Method for IPMSM Based on Active Disturbance Rejection Control and Model Predictive Control

Liu

et al. 2021

Mathematics

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In this paper, the speed tracking problem of the interior permanent magnet synchronous motor (IPMSM) of an electric vehicle is studied. A cascade speed control strategy based on active disturbance rejection control (ADRC) and a current control strategy based on improved duty cycle finite control set model predictive control (FCSMPC) are proposed, both of which can reduce torque ripple and current ripple as well as the computational burden. First of all, in the linearization process, some nonlinear terms are added into the control signal for voltage compensation, which can reduce the order of the prediction model. Then, the dq-axis currents are selected by maximum torque per ampere (MTPA). Six virtual vectors are employed to FCSMPC, and a novel way to calculate the duty cycle is adopted. Finally, the simulation results show the validity and superiority of the proposed method.

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Section: Introductionmentioning

confidence: 99%