(Industrial University of Ho Chi Minh City, Viet Nam)(2) Thanh Hai Tran
(Industrial University of Ho Chi Minh City, Viet Nam)(3) Phan Minh Than
(Industrial University of Ho Chi Minh City, Viet Nam)(4) * Thanh Quyen Ngo
(Industrial University of Ho Chi Minh City, Viet Nam)(5) Van Sy Nguyen
(Industrial University of Ho Chi Minh City, Viet Nam)(6) Tong Tan Hoa Le
(Industrial University of Ho Chi Minh City, Viet Nam)*corresponding author
AbstractThis paper addresses the problem of improving speed control accuracy and disturbance rejection capability for Permanent Magnet Synchronous Motors (PMSMs), which are widely used in industrial applications requiring high-performance drives. Conventional controllers such as PID often exhibit limited performance under nonlinear and time-varying conditions. The sliding mode control combined with a Radial Basis Function Neural Network (RBFNN) is proposed to enhance robustness and adaptability to overcome these limitations. The main contribution of this study is the integration of an adaptive RBFNN to estimate and compensate for unknown disturbances in real time, ensuring precise and stable motor operation. The theoretical stability of the system is guaranteed based on Lyapunov’s theory. The proposed method is implemented in a MATLAB/Simulink environment and tested on the OPAL-RT OP5707XG real-time hardware platform. The control system includes a speed loop using the RBFNN and a current loop for field-oriented control. The motor is subjected to varying speed commands in three stages to evaluate performance under dynamic conditions. Simulation results show that the RBFNN controller significantly improves speed tracking accuracy, reduces overshoot, and adapts better to sudden changes compared to conventional PID control. Real-time experimental results further confirm the effectiveness of the controller, despite the presence of noise and hardware delays. Current control performance also demonstrates better torque production and phase symmetry under dynamic loading with the RBFNN. A comparative analysis between simulation and experimental data highlights the practical applicability of the proposed approach.
KeywordsPMSM; Adaptive Control; RBFNN; Real-Time; OPAL-RT
|
DOIhttps://doi.org/10.31763/ijrcs.v5i3.2048 |
Article metrics10.31763/ijrcs.v5i3.2048 Abstract views : 36 | PDF views : 21 |
Cite |
Full Text Download
|
References
[1] F. Chen, J. Fan, W. Li, J. Fang, S. Ding, “Mitigation of high-resistance connection in surface-mounted PMSM drive system based on model predictive current control,” Results in Engineering, vol. 15, p. 100590, 2022, https://doi.org/10.1016/j.rineng.2022.100590.
[2] S. Gao, Y. Wei, D. Zhang, H. Qi, Y. Wei and Z. Yang, “Model-Free Hybrid Parallel Predictive Speed Control Based On Ultralocal Model of PMSM for Electric Vehicles,” IEEE Transactions on Industrial Electronics, vol. 69, no. 10, pp. 9739-9748, 2022, https://doi.org/10.1109/TIE.2022.3159951.
[3] R. Yan, D. Wang, C. Wang, W. Miao and X. Wang, “Analytical Approach and Experimental Validation of Sideband Electromagnetic Vibration and Noise in PMSM Drive With Voltage-Source Inverter by SVPWM Technique,” IEEE Transactions on Magnetics, vol. 61, no. 1, pp. 1-6, 2025, https://doi.org/10.1109/TMAG.2024.3498051.
[4] H. Cao et al., “Improved Deadbeat Predictive Current Control of PMSM Drives With Repetitive Control-Based Disturbance Correction Observer,” IEEE Transactions on Power Electronics, vol. 40, no. 1, pp. 801-812, 2025, https://doi.org/10.1109/TPEL.2024.3482315.
[5] T. Yazdan, M. Humza and H. -W. Cho, “Three-Phase Dual-Winding Multitasked PMSM Machine Using Double Layer Concentrated Winding for HEV Application,” IEEE Access, vol. 11, pp. 36682-36691, 2023, https://doi.org/10.1109/ACCESS.2023.3264568.
[6] S. Thangavel, D. Mohanraj, T. Girijaprasanna, S. Raju, C. Dhanamjayulu and S. M. Muyeen, “A Comprehensive Review on Electric Vehicle: Battery Management System, Charging Station, Traction Motors,” IEEE Access, vol. 11, pp. 20994-21019, 2023, https://doi.org/10.1109/ACCESS.2023.3250221.
[7] M. R. Khowja, K. Singh, A. la Rocca, G. Vakil, R. Ramnathan and C. Gerada, “Fault-Tolerant Dual Channels Three-Phase PMSM for Aerospace Applications,” IEEE Access, vol. 12, pp. 126845-126857, 2024, https://doi.org/10.1109/ACCESS.2024.3451705.
[8] W. Deng, X. Zhang and B. Yan, “An Enhanced Discrete Virtual Vector-Based Direct Torque Control of PMSM Drives,” IEEE Transactions on Energy Conversion, vol. 39, no. 1, pp. 277-286, 2024, https://doi.org/10.1109/TEC.2023.3314521.
[9] H. Zhou, C. Chen, X. Xiang and G. Liu, “Switching-Table-Based Diagnosis-Free Fault-Tolerant DTC for Five-Phase PMSM With Any Phase Open-Circuit Fault,” IEEE Transactions on Industrial Electronics, vol. 71, no. 11, pp. 13790-13800, 2024, https://doi.org/10.1109/TIE.2024.3379662.
[10] Basil and H. M. Marhoon, “Correction to: selection and evaluation of FOPID criteria for the X-15 adaptive flight control system (AFCS) via Lyapunov candidates: Optimizing trade-offs and critical values using optimization algorithms,” e-Prime - Advances in Electrical Engineering, Electronics and Energy, vol. 8, p. 100589, 2024, https://doi.org/10.1016/j.prime.2024.100589.
[11] A. A. Al-Jazaeri, M. Z. Al-Faiz, “The Optimum Design of Interval Type-2 Fuzzy Controller for 5 DOF Robotic Manipulator,” Iraqi Journal of Information and Communications Technology, vol. 1, no. 1, pp. 36-51, 2018, https://doi.org/10.31987/ijict.1.1.10.
[12] N. Basil, H. M. Marhoon, D. F. Sahib, A. F. Mohammed, H. M. Ridha & A. Ma’arif, “Accelerated black hole optimization algorithm with enhanced FOPID controller for omni-wheel drive mobile robot system,” Neural Computing and Applications, vol. 37, pp. 16983-17014, 2025, https://doi.org/10.1007/s00521-025-11310-6.
[13] T.-H. Liu, Y.-H. Zhuang, “Maximum efficiency control and predictive-speed controller design for interior permanent magnet synchronous motor drive systems,” Frontiers in Electronics, vol. 3, pp. 1-13, 2022, https://doi.org/10.3389/felec.2022.904976.
[14] J. Feng, “Parameter fuzzy rectification for sliding mode control of five-phase permanent magnet synchronous motor speed control system,” Frontiers in Mechanical Engineering, vol. 10, 2024, https://doi.org/10.3389/fmech.2024.1391593.
[15] Z. Liu, X. Huang, Q. Hu, G. Yang, Y. Wang and J. Shen, “Model-Free Predictive Current Control of PMSM Using Modified Extended State Observer,” IEEE Transactions on Power Electronics, vol. 40, no. 1, pp. 679-690, 2025, https://doi.org/10.1109/TPEL.2024.3476318.
[16] J. Mao et al., “Non-Cascaded Model-Free Predictive Speed Control of SMPMSM Drive System,” IEEE Transactions on Energy Conversion, vol. 37, no. 1, pp. 153-162, 2022, https://doi.org/10.1109/TEC.2021.3090427.
[17] K. Zhao, X. Chen, J. Liu and J. Yu, “Discrete-Time Adaptive Fuzzy Event-Triggered Control for PMSMs With Voltage Faults via Command Filter Approximator,” IEEE Transactions on Power Electronics, vol. 39, no. 6, pp. 7343-7350, 2024, https://doi.org/10.1109/TPEL.2024.3369055.
[18] I. A. Hasan and O. A. Awad, “An optimized fuzzy logic controller for wireless network control system using PSO,” Iraqi Journal of Information and Communication Technology, vol. 5, no. 1, pp. 1-15, 2022, https://doi.org/10.31987/ijict.5.1.180.
[19] H. S. Abdulkareem and O. A. Awad, “Fuzzy Set-Point Weight for PID Controller Based on Antlion Optimizer to Congestion Avoidance in TCP/AQM Routers,” Iraqi Journal of Information and Communication Technology, vol. 2, no. 4, pp. 1-10, 2019, https://doi.org/10.31987/ijict.2.4.73.
[20] Q. Hou, Y. Zuo, J. Sun, C. H. T. Lee, Y. Wang and S. Ding, “Modified Nonlinear Active Disturbance Rejection Control for PMSM Speed Regulation With Frequency Domain Analysis,” IEEE Transactions on Power Electronics, vol. 38, no. 7, pp. 8126-8134, 2023, https://doi.org/10.1109/TPEL.2023.3262519.
[21] Z. Hao et al., “Linear/Nonlinear Active Disturbance Rejection Switching Control for Permanent Magnet Synchronous Motors,” IEEE Transactions on Power Electronics, vol. 36, no. 8, pp. 9334-9347, 2021, https://doi.org/10.1109/TPEL.2021.3055143.
[22] V. Djordjevic, H. Tao, X. Song, S. He, W. Gao, V. Stojanovic, “Data-driven control of hydraulic servo actuator: an event-triggered adaptive dynamic programming approach,” Mathematical Biosciences an Engineering, vol. 20, no. 5, pp. 8561-8582, 2022, https://doi.org/10.3934/mbe.2023376.
[23] J. Zhao, “Data-Driven Adaptive Dynamic Programming for Optimal Control of Continuous-Time Multicontroller Systems With Unknown Dynamics,” IEEE Access, vol. 10, pp. 41503-41511, 2022, https://doi.org/10.1109/ACCESS.2022.3168032.
[24] T. Sun, L. Cheng, W. Wang, and Y. Pan, “Semiglobal exponential control of Euler–Lagrange systems using a sliding-mode disturbance observer,” Automatica, vol. 112, p. 108677, 2020, https://doi.org/10.1016/j.automatica.2019.108677.
[25] M. Chen, G. Tao and B. Jiang, “Dynamic Surface Control Using Neural Networks for a Class of Uncertain Nonlinear Systems With Input Saturation,” IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no. 9, pp. 2086-2097, 2015, https://doi.org/10.1109/TNNLS.2014.2360933.
[26] T. Yang, N. Sun and Y. Fang, “Adaptive Fuzzy Control for Uncertain Mechatronic Systems With State Estimation and Input Nonlinearities,” IEEE Transactions on Industrial Informatics, vol. 18, no. 3, pp. 1770-1780, 2022, https://doi.org/10.1109/TII.2021.3089143.
[27] S. Roy, S. Baldi, and L. Fridman, “On adaptive sliding mode control without a priori bounded uncertainty,” Automatica, vol. 111, p. 108650, 2020, https://doi.org/10.1016/j.automatica.2019.108650.
[28] Q. Deng, Y. Peng, T. Han and D. Qu, “Event-Triggered Bipartite Consensus in Networked Euler–Lagrange Systems With External Disturbance,” IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 68, no. 8, pp. 2870-2874, 2021, https://doi.org/10.1109/TCSII.2021.3057859.
[29] J. Han, “From PID to Active Disturbance Rejection Control,” IEEE Transactions on Industrial Electronics, vol. 56, no. 3, pp. 900-906, 2009, https://doi.org/10.1109/TIE.2008.2011621.
[30] W. -H. Chen, J. Yang, L. Guo and S. Li, “Disturbance-Observer-Based Control and Related Methods—An Overview,” IEEE Transactions on Industrial Electronics, vol. 63, no. 2, pp. 1083-1095, 2016, https://doi.org/10.1109/TIE.2015.2478397.
[31] W. He, Y. Sun, Z. Yan, C. Yang, Z. Li and O. Kaynak, “Disturbance Observer-Based Neural Network Control of Cooperative Multiple Manipulators With Input Saturation,” IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 5, pp. 1735-1746, 2020, https://doi.org/10.1109/TNNLS.2019.2923241.
[32] M. Basin and P. Ramirez, “Sliding mode controller design for linear systems with unmeasured states,” Journal of the Franklin Institute, vol. 349, no. 4, pp. 1337-1349, 2012, https://doi.org/10.1016/j.jfranklin.2011.06.019.
[33] H. Wu and P. Shi, “Adaptive variable structure state estimation for uncertain systems with persistently bounded disturbances,” International Journal of Robust and Nonlinear Control, vol. 20, no. 17, pp. 2003-2015, 2010, https://doi.org/10.1002/rnc.1567.
[34] L. Wu, P. Shi and H. Gao, “State Estimation and Sliding-Mode Control of Markovian Jump Singular Systems,” IEEE Transactions on Automatic Control, vol. 55, no. 5, pp. 1213-1219, 2010, https://doi.org/10.1109/TAC.2010.2042234.
[35] Y. Xia, H. Yang, M. Fu, and P. Shi, “Sliding mode control for linear systems with time-varying input and state delays,” Circuits, Systems, and Signal Processing, vol. 30, no. 3, pp. 629-641, 2011, https://doi.org/10.1007/s00034-010-9237-x.
[36] ?. Eker, “Second-order sliding mode control with experimental application,” ISA Transactions, vol. 49, no. 3, pp. 394-405, 2010, https://doi.org/10.1016/j.isatra.2010.03.010.
[37] H. F. Ho, Y. K. Wong, and A. B. Rad, “Adaptive fuzzy sliding mode control with chattering elimination for nonlinear SISO systems,” Simulation Modelling Practice and Theory, vol. 17, no. 7, pp. 1199-1210, 2009, https://doi.org/10.1016/j.simpat.2009.04.004.
[38] J. Hu, Z. Wang, H. Gao, and L. K. Stergioulas, “Robust H? sliding mode control for discrete time-delay systems with stochastic nonlinearities,” Journal of the Franklin Institute, vol. 349, no. 4, pp. 1459-1479, 2012, https://doi.org/10.1016/j.jfranklin.2011.05.018.
[39] T. Sun, H. Pei, Y. Pan, H. Zhou, and C. Zhang, “Neural network-based sliding mode adaptive control for robot manipulators,” Neurocomputing, vol. 74, no. 14, pp. 2377-2384, 2011, https://doi.org/10.1016/j.neucom.2011.03.015.
[40] M. S. Kahkeshi, F. Sheikholeslam, and M. Zekri, “Design of adaptive fuzzy wavelet neural sliding mode controller for uncertain nonlinear systems,” ISA Transactions, vol. 52, no. 3, pp. 342-350, 2013, https://doi.org/10.1016/j.isatra.2013.01.004.
[41] L. Zhang, Z. Chen, X. Yu, J. Yang and S. Li, “Sliding-Mode-Based Robust Output Regulation and Its Application in PMSM Servo Systems,” IEEE Transactions on Industrial Electronics, vol. 70, no. 2, pp. 1852-1860, 2023, https://doi.org/10.1109/TIE.2022.3163536.
[42] X. Miao, W. Yao, H. Ouyang, Z. Zhu, “Novel composite speed control of permanent magnet synchronous motor using integral sliding mode approach,” Mathematics, vol. 11, no. 22, p. 4666, 2023, https://doi.org/10.3390/math11224666.
[43] S. Kuppusamy and Y. H. Joo, “Memory-Based Integral Sliding-Mode Control for T–S Fuzzy Systems With PMSM via Disturbance Observer,” IEEE Transactions on Cybernetics, vol. 51, no. 5, pp. 2457-2465, 2021, https://doi.org/10.1109/TCYB.2019.2953567.
[44] C. Zhang, R. Qi, B. Li, S. Riaz, “Experimental validation and analysis of hybrid adaptive iterative learning sliding mode control for PMSM seeker coordinator,” Engineering Science and Technology, an International Journal, vol. 58, p. 101826, 2024, https://doi.org/10.1016/j.jestch.2024.101826.
[45] S. H. Hosseini, M. Tabatabaei, “IPMSM velocity and current control using MTPA based adaptive fractional order sliding mode controller,” Engineering Science and Technology, an International Journal, vol. 20, no. 3, pp. 896-908, 2017, https://doi.org/10.1016/j.jestch.2017.03.008.
[46] P. Chen and Y. Luo, “Analytical Fractional-Order PID Controller Design With Bode’s Ideal Cutoff Filter for PMSM Speed Servo System,” IEEE Transactions on Industrial Electronics, vol. 70, no. 2, pp. 1783-1793, 2023, https://doi.org/10.1109/TIE.2022.3158009.
[47] L. Zhang, H. Li, L. Shan, L. Zhang, L. Zhang, “Double-hierarchical fuzzy exponential convergence law fractional-order sliding mode montrol for PMSM drive control in EV,” Engineering Science and Technology, an International Journal, vol. 47, p. 101536, 2023, https://doi.org/10.1016/j.jestch.2023.101536.
[48] D. Nicolis, F. Allevi and P. Rocco, “Operational Space Model Predictive Sliding Mode Control for Redundant Manipulators,” IEEE Transactions on Robotics, vol. 36, no. 4, pp. 1348-1355, 2020, https://doi.org/10.1109/TRO.2020.2974092.
[49] L. Wu, J. Liu, S. Vazquez and S. K. Mazumder, “Sliding Mode Control in Power Converters and Drives: A Review,” IEEE/CAA Journal of Automatica Sinica, vol. 9, no. 3, pp. 392-406, 2022, https://doi.org/10.1109/JAS.2021.1004380.
[50] S. Wang, J. Na and Q. Chen, “Adaptive Predefined Performance Sliding Mode Control of Motor Driving Systems With Disturbances,” IEEE Transactions on Energy Conversion, vol. 36, no. 3, pp. 1931-1939, 2021, https://doi.org/10.1109/TEC.2020.3038010.
[51] J. Qiu, W. Ji and M. Chadli, “A Novel Fuzzy Output Feedback Dynamic Sliding Mode Controller Design for Two-Dimensional Nonlinear Systems,” IEEE Transactions on Fuzzy Systems, vol. 29, no. 10, pp. 2869-2877, 2021, https://doi.org/10.1109/TFUZZ.2020.3008271.
[52] T. Orlowska-Kowalska et al., “Fault Diagnosis and Fault-Tolerant Control of PMSM Drives–State of the Art and Future Challenges,” IEEE Access, vol. 10, pp. 59979-60024, 2022, https://doi.org/10.1109/ACCESS.2022.3180153.
[53] A. Najem, A. Moutabir, A. Ouchatti, M. Haissouf, “Experimental Validation of the Generation of Direct and Quadratic Reference Currents by Combining the Ant Colony Optimization Algorithm and Sliding Mode Control in PMSM using the Process PIL,” International Journal of Robotics and Control Systems, vol. 4, no. 1, pp. 188-216, 2024, https://doi.org/10.31763/ijrcs.v4i1.1286.
[54] L. Wang, J. Mishra, Y. Zhu and X. Yu, “An Improved Sliding-Mode Current Control of Induction Machine in Presence of Voltage Constraints,” IEEE Transactions on Industrial Informatics, vol. 16, no. 2, pp. 1182-1191, 2020, https://doi.org/10.1109/TII.2019.2944228.
[55] Z. Wang, Y. Z. Zhu, H. Xue, and H. J. Liang, “Neural networks-based adaptive event-triggered consensus control for a class of multi-agent systems with communication faults,” Neurocomputing, vol. 470, pp. 99-108, 2022, https://doi.org/10.1016/j.neucom.2021.10.059.
[56] X. Zhao, P. Shi, X. Zheng and J. Zhang, “Intelligent Tracking Control for a Class of Uncertain High-Order Nonlinear Systems,” IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 9, pp. 1976-1982, 2016, https://doi.org/10.1109/TNNLS.2015.2460236.
[57] W. He, Y. Dong and C. Sun, “Adaptive Neural Impedance Control of a Robotic Manipulator With Input Saturation,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 46, no. 3, pp. 334-344, 2016, https://doi.org/10.1109/TSMC.2015.2429555.
[58] T. Q. Ngo, M. K. Duong, D. C. Pham, and D. -N. Nguyen, “Adaptive Wavelet CMAC Tracking Control for Induction Servomotor Drive System,” Journal of Electrical Engineering & Technology, vol. 14, no. 1, pp. 209-218, 2019, https://doi.org/10.1007/s42835-018-00029-1.
[59] R. Yang, C. Yang, M. Chen, and A. S. Annamalai, “Discrete-time optimal adaptive RBFNN control for robot manipulators with uncertain dynamics,” Neurocomputing, vol. 234, pp. 107-115, 2017, https://doi.org/10.1016/j.neucom.2016.12.048.
[60] Z. Wang, J. Yuan, Y. Pan, and J. Wei, “Neural network-based adaptive fault tolerant consensus control for a class of high order multi-agent systems with input quantization and time-varying parameters,” Neurocomputing, vol. 266, pp. 315-324, 2017, https://doi.org/10.1016/j.neucom.2017.05.043.
[61] T. H. Tran, T. Q. Ngo, H. T. T. Uyen, V. T. Nguyen, and T. ?oan Duong, “Adaptive Task-Space Control of Five-Bar Parallel Robot Dynamic Model with Fully Unknown Using Radial Basis Function Neural Networks for High-Precision Applications,” Journal of Robotics and Control (JRC), vol. 6, no. 4, pp. 1624-1635, 2025, https://doi.org/10.18196/jrc.v6i4.26537.
[62] M. Daachi, T. Madani, B. Daachi, and K. Djouani, “A radial basis function neural network adaptive controller to drive a powered lower limb knee joint orthosis,” Applied Soft Computing, vol. 34, pp. 324- 336, 2015, https://doi.org/10.1016/j.asoc.2015.04.034.
[63] H. R. Nohooji, “Constrained neural adaptive PID control for robot manipulators,” Journal of the Franklin Institute, vol. 357, no. 7, pp. 3907-3923, 2020, https://doi.org/10.1016/j.jfranklin.2019.12.042.
[64] S. Seshagiri and H. K. Khalil, “Output feedback control of nonlinear systems using RBF neural networks,” IEEE Transactions on Neural Networks, vol. 11, no. 1, pp. 69-79, 2000, https://doi.org/10.1109/72.822511.
[65] P. Pillay and R. Krishnan, “Modeling of permanent magnet motor drives,” IEEE Transactions on Industrial Electronics, vol. 35, no. 4, pp. 537-541, 1988, https://doi.org/10.1109/41.9176.
[66] F. Mohd Zaihidee, S. Mekhilef, and M. Mubin, “Robust speed control of PMSM using sliding mode control (SMC)—A review,” Energies, vol. 12, no. 9, p. 1669, 2019, https://doi.org/10.3390/en12091669.
Refbacks
- There are currently no refbacks.
Copyright (c) 2025 Xuan Hung Hoang, Thanh Hai Tran, Minh Than Phan, Thanh Quyen Ngo, Van Sy Nguyen, Tong Tan Hoa Le
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
| About the Journal | Journal Policies | Author | Information |
International Journal of Robotics and Control Systems
e-ISSN: 2775-2658
Website: https://pubs2.ascee.org/index.php/IJRCS
Email: ijrcs@ascee.org
Organized by: Association for Scientific Computing Electronics and Engineering (ASCEE), Peneliti Teknologi Teknik Indonesia, Department of Electrical Engineering, Universitas Ahmad Dahlan and Kuliah Teknik Elektro
Published by: Association for Scientific Computing Electronics and Engineering (ASCEE)
Office: Jalan Janti, Karangjambe 130B, Banguntapan, Bantul, Daerah Istimewa Yogyakarta, Indonesia