Abstract
In this paper, an adaptive PI controller based on deep Q network (DQN) is proposed, which improves the speed control performance of the permanent magnet synchronous motor (PMSM) drive system and solves the contradiction between the rapidity and overshoot of the traditional PI controller. The mathematical model of PMSM vector control system with series PI controller is established, and the parameters of PI controller are calculated by pole assignment method. The damping factor of the speed loop series PI controller is taken as the variable coefficient of the adaptive PI controller and adjusted dynamically. The effectiveness of the proposed method is verified by simulation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Sutton, R., Barto, A.: Reinforcement learning: an introduction. IEEE Trans. Neural Netw. 9(5), 1054 (1998). https://doi.org/10.1109/TNN.1998.712192
Shrestha, A., Mahmood, A.: Review of deep learning algorithms and architectures. IEEE Access (2019). https://doi.org/10.1109/ACCESS.2019.2912200
Samuel, A.L.: Some Studies in Machine Learning Using the Game of Checkers. Springer, New York (1988). https://doi.org/10.1007/978-1-4613-8716-9_15
Liu, Y., Lu, H., Cheng, S., Shi, Y.: An adaptive online parameter control algorithm for particle swarm optimization based on reinforcement learning. In: 2019 IEEE Congress on Evolutionary Computation (CEC). IEEE (2019). https://doi.org/10.1109/CEC.2019.8790035
Ruan, X., Ren, D., Zhu, X., Huang, J.: Mobile robot navigation based on deep reinforcement learning. In: 2019 Chinese Control And Decision Conference (CCDC) (2019). https://doi.org/10.1109/CCDC.2019.8832393
Qi, X., Zhang. Q.: Data-driven induction motor parameters offline identification method based on actor-critic framework. Trans. China Electrotech. Soc. (2019). https://doi.org/10.19595/j.cnki.1000-6753.tces.180273
Han, J.: From PID to active disturbance rejection control. IEEE Trans. Ind. Electron. 56(3), 900–906 (2009). https://doi.org/10.1109/TIE.2008.2011621
Carlucho, I., Paula, M.D., Acosta, G.G.: Double Q -PID algorithm for mobile robot control. Expert Syst. Appl (2019). https://doi.org/10.1016/j.eswa.2019.06.066
Carlucho, I., Paula, M.D., Acosta, G.G.: An adaptive deep reinforcement learning approach for MIMO PID control of mobile robots. ISA Trans. 102 (2020). https://doi.org/10.1016/j.isatra.2020.02.017
Chen, X., Yang, Y.: A novel adaptive PID controller based on actor-critic learning. Control Theory Appl. 28(8), 1187–1192 (2011). https://doi.org/10.1007/s11518-011-5154-1
Wang, X., Cheng, Y., Sun, W.: A proposal of adaptive PID controller based on reinforcement learning. J. China Univ. Min. Technol. 17(1), 40–44 (2007). https://doi.org/10.1016/S1006-1266(07)60009-1
Sun, Q., Du, C., Duan, Y., Ren, H., Li, H.: Design and application of adaptive PID controller based on asynchronous advantage actor–critic learning method. Wirel. Netw. 27(5), 3537–3547 (2019). https://doi.org/10.1007/s11276-019-02225-x
Zhang, Z., Zhang, Y., Chen, L., Zhang, D.: DDPG adaptive control of permanent magnet synchronous linear motor. Micromotors 53(04), 89–92+102 (2020). https://doi.org/10.15934/j.cnki.micromotors.2020.04.017
Fang, X., Pan, S.: Research on motor position servo system based on reinforcement learning. Autom. Instrum. 35(6) (2020). https://doi.org/10.19557/j.cnki.1001-9944.2020.06.005
Peng, W., Lan, Y., Chen, S., Lin, F., Chang, R.: Reinforcement learning control for six-phase permanent magnet synchronous motor position servo drive. In: 2020 3rd IEEE International Conference on Knowledge Innovation and Invention (ICKII). IEEE (2020). https://doi.org/10.1109/ICKII50300.2020.9318882
Yu, Y., Wang, S., Du, Y., Rong, S., Gupta, A.: Application of off-policy integral reinforcement learning for H\(\infty \), input constrained control of permanent magnet synchronous machine. In: 2019 IEEE Applied Power Electronics Conference and Exposition (APEC). IEEE (2019). https://doi.org/10.1109/APEC.2019.8722206
Bhattacharjee, S., Halder, S., Balamurali, A., Towhidi, M., Kar, N.C.: An advanced policy gradient based vector control of PMSM for EV application. In: 2020 10th International Electric Drives Production Conference (EDPC) (2020). https://doi.org/10.1109/EDPC51184.2020.9388187
Chen, P., He, Z., Chen, C., Xu, J.: Control strategy of speed servo systems based on deep reinforcement learning. Algorithms 11(5) (2018). https://doi.org/10.3390/a11050065
Mukhopadhyay, R., Bandyopadhyay, S., Sutradhar, A., Chattopadhyay, P.: Performance analysis of deep q networks and advantage actor critic algorithms in designing reinforcement learning-based self-tuning PID controllers. In: 2019 IEEE Bombay Section Signature Conference (IBSSC). IEEE (2019). https://doi.org/10.1109/IBSSC47189.2019.8973068
InstaSPIN-FOC and InstaSPIN-MOTION User’s Guide. https://www.ti.com.cn/cn/lit/pdf/spruhj1
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Xue, Z., Wang, Y., Li, L., Wang, X. (2022). An Adaptive Speed Control Method Based on Deep Reinforcement Learning for Permanent Magnet Synchronous Motor. In: Jia, Y., Zhang, W., Fu, Y., Yu, Z., Zheng, S. (eds) Proceedings of 2021 Chinese Intelligent Systems Conference. Lecture Notes in Electrical Engineering, vol 803. Springer, Singapore. https://doi.org/10.1007/978-981-16-6328-4_30
Download citation
DOI: https://doi.org/10.1007/978-981-16-6328-4_30
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-6327-7
Online ISBN: 978-981-16-6328-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)