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<p>A dual duty cycle optimal model predictive control (DDCO-MPC) strategy is proposed to decouple torque and flux linkage in cost function, which achieve flux linkage and torque optimization without weighting factors. In this strategy, the change rate models of torque and flux linkage are constructed, and the corresponding cost functions are set to obtain the voltage vectors <i>V</i><sub>T</sub>, <i>V</i><sub>f</sub> and their duty cycles <i>d</i><sub>T</sub> and <i>d</i><sub>f</sub>, respectively. Duty cycle is dynamically assigned through a predictive cross-correction framework between torque and flux linkage, balancing the optimization of multi-objective operations and significantly improving control performance. Meanwhile, an adaptive compensation mechanism is integrated with the vector state evaluation algorithm to dynamically adjust duty cycles, achieving optimal trade-offs between transient response and steady-state error suppression. This synergistic approach enhances both rapid torque response and high-fidelity flux linkage tracking, thereby elevating the dynamic performance of hub motors under transient and steady-state conditions. In addition, the dynamic constraint d<i>i</i><sub>d</sub>/dt = 0 is introduced on the basis of the traditional <i>i</i><sub>d</sub> = 0 strategy, which can not only suppress the <i>d</i>-axis perturbation, stabilize the flux linkage phase, and reduce the torque ripple, but also improve the <i>q</i>-axis current utilization and optimize the torque output efficiency. Simulations and experiments verify the correctness and effectiveness of the control method.</p>

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Dual Duty Cycle Optimal Model Predictive Control for Permanent Magnet Hub Motors with Torque-Flux Linkage Decoupling

  • 期刊论文

  • 发表日期：

    2026年7月9日

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  • Li Quan,
  • Xiongwei Xia,
  • Xiaoyong Zhu,
  • Chao Zhang

摘要

A dual duty cycle optimal model predictive control (DDCO-MPC) strategy is proposed to decouple torque and flux linkage in cost function, which achieve flux linkage and torque optimization without weighting factors. In this strategy, the change rate models of torque and flux linkage are constructed, and the corresponding cost functions are set to obtain the voltage vectors VT, Vf and their duty cycles dT and df, respectively. Duty cycle is dynamically assigned through a predictive cross-correction framework between torque and flux linkage, balancing the optimization of multi-objective operations and significantly improving control performance. Meanwhile, an adaptive compensation mechanism is integrated with the vector state evaluation algorithm to dynamically adjust duty cycles, achieving optimal trade-offs between transient response and steady-state error suppression. This synergistic approach enhances both rapid torque response and high-fidelity flux linkage tracking, thereby elevating the dynamic performance of hub motors under transient and steady-state conditions. In addition, the dynamic constraint did/dt = 0 is introduced on the basis of the traditional id = 0 strategy, which can not only suppress the d-axis perturbation, stabilize the flux linkage phase, and reduce the torque ripple, but also improve the q-axis current utilization and optimize the torque output efficiency. Simulations and experiments verify the correctness and effectiveness of the control method.