Abstract
In order to keep the height of the table tennis ball machine from being stable, it is difficult to achieve fast and accurate speed regulation in the brushless DC motor control system for the traditional PID control. A method of controlling the RBF neural network PID is proposed. Based on the mathematical model of the brushless DC motor, the self-learning ability of the RBF neural network is used to adjust the parameters of the PID controller in real time to adjust the motor speed so that the table tennis ball-out mechanism is highly stable. The experimental results show that the motor control system based on the RBF neural network PID control strategy has stable start-up, good static and dynamic performance, and strong robustness, which can meet the requirements of high stability of the ball-out machine.
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References
夏长亮 (2009) 无刷直流电机控制系统. 科学出版社
Fan X, Meng F, Fu C et al (2009) Research of brushless DC motor simulation system based on RBF-PID algorithm. In: 2009 second international symposium on knowledge acquisition and modeling. IEEE Computer Society
裴玉兵, 朱学来 (2017) 基于 RBF-PID 控制的直流调速系统仿真. 盐城工学院学报(自然科学版) (4):21–25
王晓远, 傅涛 (2015) 基于模糊 RBF 神经网络的无刷直流电机控制. 微电机 48(11)
Cheng J, Zhang G, Lu C, et al (2017) Research of brushless DC motor control system based on RBF neural network. In: Automation. IEEE
Cao SY, Tang WJ (2018) 2018 37th Chinese control conference (CCC) - speed control system based on fuzzy neural network of BLDCM, Wuhan, China, 25–27 July 2018, pp 3295–3297
Cao SY, Tang WJ. Speed control system based on fuzzy neural network of BLDCM. In: 第 37 届中国控制会议
马晓爽, 石征锦 (2016) 基于 Simulink 的无刷直流电机双闭环调速系统仿真研究. 制造业自动化 38(7):82–88
Rank E (2003) Application of Bayesian trained RBF networks to nonlinear time-series modeling. Signal Process 83(7):1393–1410
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Xu, M., Wang, C. (2020). Design of Speed Regulation of Ping-Pong Ball Ejector Based on RBF Neural Network PID Control. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2019 Chinese Intelligent Systems Conference. CISC 2019. Lecture Notes in Electrical Engineering, vol 593. Springer, Singapore. https://doi.org/10.1007/978-981-32-9686-2_46
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DOI: https://doi.org/10.1007/978-981-32-9686-2_46
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