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<p>A human-simulated intelligent control strategy, based on the hybrid Taguchi genetic algorithm, is proposed to address the distinct influence mechanisms of torsional stiffness, torsional damping, and moment of inertia on the torsional vibration behavior of magneto-rheological (MR) transmission systems with variable stiffness, damping, and inertia. First, an in-depth analysis is conducted on the effects of torsional stiffness, damping, and moment of inertia on the dynamics of the transmission system. A comprehensive dynamic model of the MR transmission system, incorporating adaptable stiffness, damping, and inertia parameters, is then developed based on the concept of an MR torsional vibration absorber. Control parameters governing torsional stiffness, damping, and moment of inertia are refined based on the dynamic optimization results obtained from the hybrid Taguchi genetic algorithm. This refinement process forms the basis for designing a human-simulated intelligent controller with partitioned and multimodal characteristics. The human-simulated intelligent controller can precisely determine the optimal control parameters, including torsional stiffness, damping, and load’s moment of inertia, for the MR torsional vibration damper. This is achieved through the dynamic optimization results from the hybrid Taguchi genetic algorithm, which are based on the detection of torsional vibration amplitude in the time domain and response frequency in the frequency domain of the MR transmission system. After the controller's design phase, rigorous simulation analyses are conducted to evaluate its effectiveness. Numerical simulation results confirm that the human-simulated intelligent control mechanism, supported by the hybrid Taguchi genetic algorithm, effectively reduces torsional vibrations across a wide range of frequency bands. When exposed to random excitation, the human-simulated intelligent control of the MR semi-active transmission system significantly reduces the root-mean-square values of torsional angle displacement, angular velocity, and angular acceleration by 21.03%, 43.06%, and 45.31%, respectively, highlighting the superiority of the proposed control strategy. This approach is observed to significantly improve the overall output characteristics of the MR transmission system.</p>

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Human-simulated intelligent control of torsional vibration in magneto-rheological variable stiffness–damping–moment of inertia transmission system based on hybrid Taguchi genetic algorithm

  • 期刊论文

  • 发表日期：

    2025年6月27日

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  • Wenfeng Li,
  • Le Liu,
  • Xinfeng Ge,
  • Manying Liang,
  • Xiong Deng,
  • Jianrong Gao

摘要

A human-simulated intelligent control strategy, based on the hybrid Taguchi genetic algorithm, is proposed to address the distinct influence mechanisms of torsional stiffness, torsional damping, and moment of inertia on the torsional vibration behavior of magneto-rheological (MR) transmission systems with variable stiffness, damping, and inertia. First, an in-depth analysis is conducted on the effects of torsional stiffness, damping, and moment of inertia on the dynamics of the transmission system. A comprehensive dynamic model of the MR transmission system, incorporating adaptable stiffness, damping, and inertia parameters, is then developed based on the concept of an MR torsional vibration absorber. Control parameters governing torsional stiffness, damping, and moment of inertia are refined based on the dynamic optimization results obtained from the hybrid Taguchi genetic algorithm. This refinement process forms the basis for designing a human-simulated intelligent controller with partitioned and multimodal characteristics. The human-simulated intelligent controller can precisely determine the optimal control parameters, including torsional stiffness, damping, and load’s moment of inertia, for the MR torsional vibration damper. This is achieved through the dynamic optimization results from the hybrid Taguchi genetic algorithm, which are based on the detection of torsional vibration amplitude in the time domain and response frequency in the frequency domain of the MR transmission system. After the controller's design phase, rigorous simulation analyses are conducted to evaluate its effectiveness. Numerical simulation results confirm that the human-simulated intelligent control mechanism, supported by the hybrid Taguchi genetic algorithm, effectively reduces torsional vibrations across a wide range of frequency bands. When exposed to random excitation, the human-simulated intelligent control of the MR semi-active transmission system significantly reduces the root-mean-square values of torsional angle displacement, angular velocity, and angular acceleration by 21.03%, 43.06%, and 45.31%, respectively, highlighting the superiority of the proposed control strategy. This approach is observed to significantly improve the overall output characteristics of the MR transmission system.