VP21-02 Bagging-GA-Driven Multi-Objective Optimization of MR Fluid Brake-Integrated PMSM Considering Brake Torque Constraint
Creators: Hang Lu
Publisher: name: IEEE
Year: 2025

1. Introduction Magnetorheological (MR) fluid is a smart fluid capable of reversibly converting between liquid and semi-solid states within milliseconds, controlled by a magnetic field[1].This effect makes MR fluid medium-based brakes have adjustable torque, fast response, and compact volume[2]. MR fluid brake-integrated PMSM (MRFB-I-PMSM) is a novel machine topology[3] used to improve the compactness and lightweight of robot joints. However, the integrated topology augments dimensionality and structural complexity, posing challenges for optimization including brake torque, electromagnetic (EM) torque, and EM torque ripple. Thus, this paper proposes a multi-objective optimization method for MRFB-I-PMSM driven by Bagging-GA considering brake torque constraint. 2. Methodology Fig. 1(a) to (c) show the machine topology and the proposed optimization methodology, respectively. This article opts not to directly optimize brake torque Tb but rather to constrain it as a passive variable within a certain range related to average EM torque Tavg. It is reflected by introducing the constraint ratio RT=Tb/Tavg. Then, this paper sets expected RT at the rated braking current as the range of [0.9, 1.0]. Thus, the objective function in genetic algorithm (GA) can be simplified as shown in Fig1. (d). 3. Optimization design Bagging is an ensemble machine learning method, which can effectively reduce the risk of overfitting and improve the efficiency of surrogate models[4]. Fig. 2(a) shows that trained Bagging-based surrogate models exhibit almost the same accuracy as finite element analysis (FEA). In the brake constrained GA optimization process, RT began to converge within [0.9, 1.0]. Finally, It can be observed in Fig. 2(b) that the points within the expected range of 0.9 ≤ RT ≤ 1.0 have formed a Pareto front. To verify the validity of the proposed method, the torque performances of MRFB-I-PMSM in two modes with and without optimization are compared based on FEA. Fig. 2(c) shows torque performances of the initial and optimal models of the MRFB-I-PMSM. It can be observed after optimization, Tavg has increased by 9.89 %, Trip has decreased by 60.34 %, and Tb has increased by 18.46 %.References: [1] C. Rossa, A. Jaegy, J. Lozada, et al., “Design considerations for magnetorheological brakes,” IEEE/ASME Trans. Mechatronics., vol. 19, no. 5, pp. 1669-1680, Oct. 2014. [2] I. Bahiuddin, et al., “A new constitutive model of a magneto-rheological fluid actuator using an extreme learning machine method,” Sens. Actuators A. Phys., vol. 281, pp. 209-221, Oct. 2018. [3] Y. Hu and W. Xu, “A Built-in Integrated Magnetorheological Fluid Brake Permanent Magnet Synchronous Motor”, IEEE Trans. Magn., vol. 59, no. 11, pp. 1-6, Nov. 2023. [4] K. Chen, Y. Peng, S. Lu, B. Lin and X. Li, “Bagging based ensemble learning approaches for modeling the emission of PCDD/Fs from municipal solid waste incinerators,” Chemosphere, vol. 287, no. 4, pp. 1-11, Jan. 2022.
1. Introduction Magnetorheological (MR) fluid is a smart fluid capable of reversibly converting between liquid and semi-solid states within milliseconds, controlled by a magnetic field[1].This effect makes MR fluid medium-based brakes have adjustable torque, fast response, and compact volume[2]. MR fluid brake-integrated PMSM (MRFB-I-PMSM) is a novel machine topology[3] used to improve the compactness and lightweight of robot joints. However, the integrated topology augments dimensionality and structural complexity, posing challenges for optimization including brake torque, electromagnetic (EM) torque, and EM torque ripple. Thus, this paper proposes a multi-objective optimization method for MRFB-I-PMSM driven by Bagging-GA considering brake torque constraint. 2. Methodology Fig. 1(a) to (c) show the machine topology and the proposed optimization methodology, respectively. This article opts not to directly optimize brake torque Tb but rather to constrain it as a passive variable within a certain range related to average EM torque Tavg. It is reflected by introducing the constraint ratio RT=Tb/Tavg. Then, this paper sets expected RT at the rated braking current as the range of [0.9, 1.0]. Thus, the objective function in genetic algorithm (GA) can be simplified as shown in Fig1. (d). 3. Optimization design Bagging is an ensemble machine learning method, which can effectively reduce the risk of overfitting and improve the efficiency of surrogate models[4]. Fig. 2(a) shows that trained Bagging-based surrogate models exhibit almost the same accuracy as finite element analysis (FEA). In the brake constrained GA optimization process, RT began to converge within [0.9, 1.0]. Finally, It can be observed in Fig. 2(b) that the points within the expected range of 0.9 ≤ RT ≤ 1.0 have formed a Pareto front. To verify the validity of the proposed method, the torque performances of MRFB-I-PMSM in two modes with and without optimization are compared based on FEA. Fig. 2(c) shows torque performances of the initial and optimal models of the MRFB-I-PMSM. It can be observed after optimization, Tavg has increased by 9.89 %, Trip has decreased by 60.34 %, and Tb has increased by 18.46 %.References: [1] C. Rossa, A. Jaegy, J. Lozada, et al., “Design considerations for magnetorheological brakes,” IEEE/ASME Trans. Mechatronics., vol. 19, no. 5, pp. 1669-1680, Oct. 2014. [2] I. Bahiuddin, et al., “A new constitutive model of a magneto-rheological fluid actuator using an extreme learning machine method,” Sens. Actuators A. Phys., vol. 281, pp. 209-221, Oct. 2018. [3] Y. Hu and W. Xu, “A Built-in Integrated Magnetorheological Fluid Brake Permanent Magnet Synchronous Motor”, IEEE Trans. Magn., vol. 59, no. 11, pp. 1-6, Nov. 2023. [4] K. Chen, Y. Peng, S. Lu, B. Lin and X. Li, “Bagging based ensemble learning approaches for modeling the emission of PCDD/Fs from municipal solid waste incinerators,” Chemosphere, vol. 287, no. 4, pp. 1-11, Jan. 2022.