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UPSI Digital Repository (UDRep)
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| Abstract : Universiti Pendidikan Sultan Idris |
| An improved fractional-order sliding mode control (FOSMC) for PMSM is presented in this study to set the unavoidable parameters and to improve permanent magnet synchronous motors (PMSMs) drive performance, such as current and speed tracking accuracy. To determine the optimal parameters of the FOSMC for control speed in a PMSM drive, a neural network algorithm with reinforcement learning (RLNNA) is proposed. The FOSMC parameters are set by the ANN algorithm and then adapted through reinforcement learning to enhance the results. The proposed controller using RLNNA based on fractional-order sliding mode control (RLNNA-FOSMC) can drive the motor speed to achieve the referred value in a finite period of time, leading to faster convergence and improved tracking accuracy. For a fair comparison and evaluation, the proposed RLNNA-FOSMC is compared with conventional FOSMC by applying the integral of time multiplied absolute error as an objective function. The most commonly used objective functions in the literature were also compared, including the integral time multiplied square error, integral square error, and integral absolute error. To validate the performance of the RLNNA-FOSMC speed controller, different scenarios with different speeds steps were carried out. The computational results are promising and demonstrate the effectiveness of the proposed controller. Overall, the proposed RLNNA-FOSMC controller for the PMSM speed control system performed better than conventional FOSMC in numerical simulations. 2023 by the authors. |
| References |
Xu, W.; Jiang, Y.; Mu, C. Novel Composite Sliding Mode Control for PMSM Drive System Based on Disturbance Observer. IEEE Trans. Appl. Supercond. 2016, 26, 0612905. [CrossRef] Kamel, T.; Abdelkader, D.; Said, B.; Padmanaban, S.; Iqbal, A. Extended Kalman filter based sliding mode control of parallelconnected two five-phase PMSM drive system. Electronics 2018, 7, 14. [CrossRef] Zahraoui, Y.; Zaihidee, F.M.; Kermadi, M.; Mekhilef, S.; Mubin, M.; Tang, J.R.; Zaihidee, E.M. Fractional Order Sliding Mode Controller Based on Supervised Machine Learning Techniques for Speed Control of PMSM. Mathematics 2023, 11, 1457. [CrossRef] Zhang, B.T.; Pi, Y.G.; Luo, Y. Fractional order sliding-mode control based on parameters auto-tuning for velocity control of permanent magnet synchronous motor. ISA Trans. 2012, 51, 649–656. [CrossRef] [PubMed] Wang, H.; Li, S.; Lan, Q.; Zhao, Z.; Zhou, X. Continuous terminal sliding mode control with extended state observer for PMSM speed regulation system. Trans. Inst. Meas. Control 2017, 39, 1195–1204. [CrossRef] Zaihidee, F.M.; Mekhilef, S.; Mubin, M. Robust speed control of pmsm using sliding mode control (smc)—A review. Energies 2019, 12, 1669. [CrossRef] Zhou, Z.; Zhang, B.; Mao, D. Robust sliding mode control of PMSM based on a rapid nonlinear tracking differentiator and disturbance observer. Sensors 2018, 18, 1031. [CrossRef] Saihi, L.; Boutera, A. Robust Sensorless Sliding Mode Control of PMSM with MRAS and Luenberger Extended Observer. In Proceedings of the 2016 8th International Conference on Modelling, Identification and Control ICMIC 2016, Algiers, Algeria, 15–17 November 2016; pp. 174–179. [CrossRef] Song, Q.; Li, Y.; Jia, C. A novel direct torque control method based on asymmetric boundary layer sliding mode control for PMSM. Energies 2018, 11, 657. [CrossRef] Liu, X.; Zhang, C.; Li, K.; Zhang, Q. Robust current control-based generalized predictive control with sliding mode disturbance compensation for PMSM drives. ISA Trans. 2017, 71, 542–552. [CrossRef] Jiang, Y.; Xu, W.; Mu, C.; Liu, Y. Improved deadbeat predictive current control combined sliding mode strategy for PMSM drive system. IEEE Trans. Veh. Technol. 2018, 67, 251–263. [CrossRef] Ma, Y.; Li, D.; Li, Y.; Yang, L. A Novel Discrete Compound Integral Terminal Sliding Mode Control with Disturbance Compensation for PMSM Speed System. IEEE/ASME Trans. Mechatron. 2022, 27, 549–560. [CrossRef] Xu, B.; Zhang, L.; Ji, W. Improved Non-Singular Fast Terminal Sliding Mode Control with Disturbance Observer for PMSM Drives. IEEE Trans. Transp. Electrif. 2021, 7, 2753–2762. [CrossRef] Gao, P.; Zhang, G.; Lv, X. Model-Free Control Using Improved Smoothing Extended State Observer and Super-Twisting Nonlinear Sliding Mode Control for PMSM Drives. Energies 2021, 14, 922. [CrossRef] Dai, Y.; Ni, S.; Xu, D.; Zhang, L.; Yan, X.-G. Disturbance-observer based prescribed-performance fuzzy sliding mode control for PMSM in electric vehicles. Eng. Appl. Artif. Intell. 2021, 104, 104361. [CrossRef] Wang, Y.; Zhu, Y.; Zhang, X.; Tian, B.;Wang, K.; Liang, J. Antidisturbance Sliding Mode-Based Deadbeat Direct Torque Control for PMSM Speed Regulation System. IEEE Trans. Transp. Electrif. 2021, 7, 2705–2714. [CrossRef] Hou, Q.; Ding, S.; Yu, X. Composite Super-Twisting Sliding Mode Control Design for PMSM Speed Regulation Problem Based on a Novel Disturbance Observer. IEEE Trans. Energy Convers. 2021, 36, 2591–2599. [CrossRef] Liu, X.; Yu, H.; Yu, J.; Zhao, L. Combined Speed and Current Terminal Sliding Mode Control with Nonlinear Disturbance Observer for PMSM Drive. IEEE Access 2018, 6, 29594–29601. [CrossRef] Zaihidee, F.M.; Mekhilef, S.; Mubin, M. Application of Fractional Order Sliding Mode Control for Speed Control of Permanent Magnet Synchronous Motor. IEEE Access 2019, 7, 101765–101774. [CrossRef] Mani, P.; Rajan, R.; Shanmugam, L.; Joo, Y.H. Adaptive fractional fuzzy integral sliding mode control for PMSM model. IEEE Trans. Fuzzy Syst. 2019, 27, 1674–1686. [CrossRef] Liu, Y.C.; Laghrouche, S.; N’Diaye, A.; Cirrincione, M. Hermite neural network-based second-order sliding-mode control of synchronous reluctance motor drive systems. J. Frankl. Inst. 2021, 358, 400–427. [CrossRef] Chen, W.H.; Yang, J.; Guo, L.; Li, S. Disturbance-Observer-Based Control and Related Methods—An Overview. IEEE Trans. Ind. Electron. 2016, 63, 1083–1095. [CrossRef] Yang, J.; Chen,W.H.; Li, S.; Guo, L.; Yan, Y. Disturbance/Uncertainty Estimation and Attenuation Techniques in PMSM Drives—A Survey. IEEE Trans. Ind. Electron. 2017, 64, 3273–3285. [CrossRef] Qi, Z.; Shi, Q.; Zhang, H. Tuning of digital PID controllers using particle swarm optimization algorithm for a CAN-Based DC motor subject to stochastic delays. IEEE Trans. Ind. Electron. 2020, 67, 5637–5646. [CrossRef] Zhang, Y. Learning for Parameters Extraction of Photovoltaic Models. IEEE Trans. Neural Netw. Learn. Syst. 2021, 1–11. Cook, D.F.; Ragsdale, C.T.; Major, R.L. Combining a neural network with a genetic algorithm for process parameter optimization. Eng. Appl. Artif. Intell. 2000, 13, 391–396. [CrossRef] Sadollah, A.; Sayyaadi, H.; Yadav, A. A dynamic metaheuristic optimization model inspired by biological nervous systems: Neural network algorithm. Appl. Soft Comput. J. 2018, 71, 747–782. [CrossRef] Arwa, E.O.; Folly, K.A. Reinforcement Learning Techniques for Optimal Power Control in Grid-Connected Microgrids: A Comprehensive Review. IEEE Access 2020, 8, 208992–209007. [CrossRef] Zhou, S.; Hu, Z.; Gu,W.; Jiang, M.; Chen, M.; Hong, Q.; Booth, C. Combined heat and power system intelligent economic dispatch: A deep reinforcement learning approach. Int. J. Electr. Power Energy Syst. 2020, 120, 106016. [CrossRef] |
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