Multi motor neural PID relative coupling speed synchronous control

• Autorzy: Zhang Yonglong , An Yuejun , Wang Guangyu , Kong Xiangling

Identyfikatory

DOI

10.24425/aee.2020.131759

• Języki publikacji: EN

Abstrakty

EN

When the traditional multi-motor speed synchronous control strategy is applied to the vacuum pump system, it is prone to the drawbacks of large synchronization error. In this paper, a simplified mathematical model of the motor for a vacuum pump is established and the transfer function is introduced, which weakens the multivariable, strong coupling and nonlinear characteristics of the motor system. According to the basic principle of the relative coupling control strategy, the neural network Proportion Integration Differentiation (PID) is introduced as a speed compensator in this system. It effectively improves the synchronization and anti-interference ability of the multi motor

Słowa kluczowe

EN

fuzzy control; multi motor synchronous control; neural PID; relative coupling control; vacuum pump

• Wydawca: Polish Academy of Sciences, Committee on Electrical Engineering
• Czasopismo: Archives of Electrical Engineering
• Rocznik: 2020
• Tom: Vol. 69, nr 1
• Strony: 69--88
• Opis fizyczny: Bibliogr. 30 poz., rys., tab., wz.

Twórcy

autor

Zhang Yonglong

• School of Electrical Engineering Shenyang University of Technology Shenyang, 110870, China

autor

An Yuejun

• School of Electrical Engineering Shenyang University of Technology Shenyang, 110870, China

autor

Wang Guangyu

• Vacuum Dry Pump Business Division SKY Technology Development Co. Ltd Chinese Academy of Sciences Shenyang, 110179, China

autor

Kong Xiangling

• Vacuum Dry Pump Business Division SKY Technology Development Co. Ltd Chinese Academy of Sciences Shenyang, 110179, China

Bibliografia

• [1] Valenzuela M.A., Lorenz R.D., Startup and commissioning procedures for electronically line-shafted paper machine drives, IEEE Transactions on Industry Applications, vol. 38, no. 4, pp. 966−973 (2002).
• [2] Lorenz R.D., Schmidt P.B., Synchronized motion control for process automation, Industry Applications Society Meeting (2002).
• [3] Koren Y., Cross-Coupled Biaxial Computer Controls for Manufacturing Systems [J], Journal of Dynamic Systems Measurement and Control, vol. 102, no. 4, pp. 265–272 (1980).
• [4] Xiao Y., Zhu K.Y., Optimal synchronization control of high-precision motion systems, IEEE Transactions on Industrial Electronics, vol. 53, no. 4, pp. 1160–1169 (2006).
• [5] Anderson R.G., Meyer A.J., Valenzuela M.A., Lorenz R.D., Web machine coordinated motion control via electronic line-shafting, IEEE Transactions on Industry Applications, vol. 37, no. 1, pp. 247–254 (2001).
• [6] Perez-Pinal, Nunez C., Alvarez R., Cervantes I., Comparison of multi-motor synchronization techniques, Conference of the IEEE Industrial Electronics Society (2004).
• [7] Cong C., Liu X., Liu G., Liang Z., Li C., Zhao B., Multi-motor Synchronous System Based on Neural Network Control, Chinese Control Conference (2008).
• [8] Liu X., Second-order active disturbance rejection controller applied in three-motor synchronous system, Transactions of China Electrotechnical Society, vol. 27, no. 2, pp. 179–184 (2012).
• [9] Liu G., Liu P., Yue S., Wang F., Experimental Research on Decoupling Control of Multi-motor Variable Frequency System Based on Neural Network Generalized Inverse, IEEE International Conference on Networking (2008).
• [10] Liu H., Geng Q., Xia C., Shi T., Improved relative coupling control structure for multi-motor speed synchronous driving system, IET Electric Power Applications, vol. 10, no. 6, pp. 451–457 (2016).
• [11] Sun G., Ren X., Li D., Neural active disturbance rejection output control of multimotor servomechanism, IEEE Transactions on Control Systems Technology, vol. 23, no. 2, pp. 746–753 (2015).
• [12] Inamura S., Sakai T., Sawa K.A, Temperature rise analysis of switched reluctance motor due to the core and copper loss by fem, IEEE Transactions on Magnetics, vol. 39, no. 3, pp. 1554–1557 (2003).
• [13] Ergene L.T., Salon S.J., Determining the equivalent circuit parameters of canned solid-rotor induction motors, IEEE Transactions on Magnetics, vol. 41, no. 7, pp. 2281–2286 (2005).
• [14] Tarchala G., Sliding modes application to the control and state variables estimation of the drive system with induction motor, Ph.D. Thesis (in Polish), Wroclaw University of Technology, Wroclaw (2013).
• [15] Orlowska-Kowalska T., Tarchala G., Sliding mode speed and torque control of the induction motor, Electrical Engineering Review (in Polish), vol. 87, pp. 245–248 (2011).
• [16] Casadei D., Serra G., Tani A., Zarri L., Assessment of direct torque control for induction motor drives, Bulletin of Polish Academy of Science: Technology, vol. 54, no. 3, pp. 237–254 (2006).
• [17] Werner Leonhard, Control of Electrical Drives (2001).
• [18] Zhang Y., Feng C., Li B., Pid control of nonlinear motor-mechanism coupling system using artificial neural network, Lecture Notes in Computer Science, vol. 3972, pp. 1096–1103 (2006).
• [19] Zhongda Tian, Shujiang Li, Yanhong Wang, T-S fuzzy neural network predictive control for burning zone temperature in rotary kiln with improved hierarchical genetic algorithm, International Journal of Modelling, Identification and Control, vol. 25, no. 4, pp. 323–334 (2016).
• [20] Zhongda Tian, Main steam temperature control based on GA-BP optimised fuzzy neural network, International Journal of Engineering Systems Modelling and Simulation, vol. 9, no. 3, pp. 150–160 (2017).
• [21] Zhongda Tian, Gang Wang, Yi Ren, Shujiang Li, An Adaptive Online Sequential Extreme Learning Machine for Short-term Wind Speed Prediction Based on Improved Artificial Bee Colony Algorithm, Neural Network World, vol. 28, no. 3, pp. 191–212 (2018).
• [22] Tian Z., Li S., Wang Y., Zhang Q., Multi permanent magnet synchronous motor synchronization control based on variable universe fuzzy PI method, Engineering Letters, vol. 23, no. 3, pp. 180−188 (2015).
• [23] Zhongda Tian, Xianwen Gao, Peiqin Guo, Network Teleoperation Robot System Control based on Fuzzy Sliding Mode, Journal of Advanced Computational Intelligence and Intelligent Informatics, vol. 20, no. 5, pp. 828–835 (2016).
• [24] Zhongda Tian, Yi Ren, Gang Wang, Fuzzy-PID controller based on variable universe for main steam temperature system, Australian Journal of Electrical and Electronics Engineering, vol. 15, no. 1–2, pp. 21–28 (2018).
• [25] Ahmad Kalaie, Li Shujiang, Wang Yanhong, Wang Xiangdong, Scheduling method for networked control system with resource constraints based on fuzzy feedback priority and variable sampling period, Transactions of the Institute of Measurement and Control, vol. 40, no. 4, pp. 1136−1149 (2018).
• [26] Zhongda Tian, Xianwen Gao, Dehua Wang, The Application Research of Fuzzy Control with Selftuning of Scaling Factor in the Energy Saving Control System of Pumping Unit, Engineering Letters, vol. 24, no. 2, pp. 187–194 (2016).
• [27] Ibrahim Z., Levi E., A comparative analysis of fuzzy logic and pi speed control in high-performance ac drives using experimental approach, IEEE Transactions on Industry Applications, vol. 38, no. 5, pp. 1210–1218 (2002).
• [28] Ma X.J., Sun Z.Q., He Y.Y., Analysis and design of fuzzy controller and fuzzy observer, vol. 6, no. 1, pp. 41–51 (2002).
• [29] Mirzaei A., Moallem M., Dehkordi B.M., Fahimi B., Design of an optimal fuzzy controller for antilock braking systems, IEEE Transactions on Vehicular Technology, vol. 55, no. 6, pp. 1725-1730 (2006).
• [30] Rubaai A., Castro-Sitiriche J., Ofoli A.R., Design and implementation of parallel fuzzy pid controller for high-performance brushless motor drives: an integrated environment for rapid control prototyping, IEEE Transactions on Industry Applications, vol. 44, no. 4, pp. 1090–1098 (2008).

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).