A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 8 Issue 3
Mar.  2021

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Seok-Kyoon Kim and Choon Ki Ahn, "DC Motor Speed Regulator via Active Damping Injection and Angular Acceleration Estimation Techniques," IEEE/CAA J. Autom. Sinica, vol. 8, no. 3, pp. 641-647, Mar. 2021. doi: 10.1109/JAS.2020.1003548
Citation: Seok-Kyoon Kim and Choon Ki Ahn, "DC Motor Speed Regulator via Active Damping Injection and Angular Acceleration Estimation Techniques," IEEE/CAA J. Autom. Sinica, vol. 8, no. 3, pp. 641-647, Mar. 2021. doi: 10.1109/JAS.2020.1003548

DC Motor Speed Regulator via Active Damping Injection and Angular Acceleration Estimation Techniques

doi: 10.1109/JAS.2020.1003548
Funds:  This work was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2020M3H4A3106326), and was supported in part by the NRF grant funded by the Korea government (Ministry of Science and ICT) (NRF-2020R1A2C1005449)
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  • This paper suggests a novel model-based nonlinear DC motor speed regulator without the use of a current sensor. The current dynamics, machine parameters and mismatched load variations are considered. The proposed controller is designed to include an active damping term that regulates the motor speed in accordance with the first-order low-pass filter dynamics through the pole-zero cancellation. Meanwhile, the angular acceleration and its reference are obtained from simple first-order estimators using only the speed information. The effectiveness is experimentally verified using hardware comprising the QUBE-Servo2, myRIO-1900, and LabVIEW.

     

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  • [1]
    H. Yang and J. Liu, “An adaptive RBF neural network control method for a class of nonlinear systems,” IEEE/CAA J. Autom. Sinica, vol. 5, no. 2, pp. 457–462, 2018. doi: 10.1109/JAS.2017.7510820
    [2]
    J. Huang, Y. Chen, X. Peng, L. Hu, and D. Cao, “Study on the driving style adaptive vehicle longitudinal control strategy,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 1107–1115, Jul. 2020. doi: 10.1109/JAS.2020.1003261
    [3]
    S. Ling, H. Wang, and P. X. Liu, “Adaptive fuzzy dynamic surface control of flexible-joint robot systems with input saturation,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 1, pp. 97–107, 2019. doi: 10.1109/JAS.2019.1911330
    [4]
    Y. Li and F. Yang, “Robust adaptive attitude control for non-rigid spacecraft with quantized control input,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 2, pp. 472–481, 2020. doi: 10.1109/JAS.2020.1003000
    [5]
    J. Peng, B. Fan, J. Duan, Q. Yang, and W. Liu, “Adaptive decentralized output-constrained control of single-bus DC microgrids,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 2, pp. 424–432, 2019. doi: 10.1109/JAS.2019.1911387
    [6]
    Y. Xu, C. K. Ahn, Y. S. Shmaliy, X. Chen, and L. Bu, “Indoor INS / UWB-based human localization with missing data utilizing predictive UFIR filtering,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 4, pp. 952–960, 2019. doi: 10.1109/JAS.2019.1911570
    [7]
    S. K. Jha and S. Bhasin, “Adaptive linear quadratic regulator for continuous-time systems with uncertain dynamics,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 3, pp. 833–841, 2020. doi: 10.1109/JAS.2019.1911438
    [8]
    G.-D. Andeescu, C.Pitic, F. Blaabjerg, and I. Boldea, “Combined flux observer with signal injection enhancement for wide speed range sensorless direct torque control of IPMSM drives,” IEEE Trans. Energy Convers., vol. 23, pp. 393–402, 2008. doi: 10.1109/TEC.2007.914386
    [9]
    L. Tang, L. Zhong, M. Rahman, and Y. Hu, “A novel direct torque control for interior permanent-magnet synchronous machine drive with low ripple in torque and flux - A speed-senseroless approach,” IEEE Trans. Ind. Appl., vol. 39, pp. 1748–1756, 2003. doi: 10.1109/TIA.2003.818981
    [10]
    A. A. El-samahy and M. A.Shamseldin, “Brushless DC motor tracking control using self-tuning fuzzy PID control and model reference adaptive control,” Ain Shams Eng. J., vol. 9, pp. 341–352, 2018. doi: 10.1016/j.asej.2016.02.004
    [11]
    C. R. Lee, S.-K. Kim, and C. K. Ahn, “Auto-tuning proportional-type synchronization algorithm for DC motor speed control applications,” IEEE Trans. Circuits Syst. Ⅱ Express Briefs, vol. 67, pp. 521–525, 2020. doi: 10.1109/TCSII.2019.2915384
    [12]
    Y. S. Lee, D. S. Kim, and S.-K. Kim, “Disturbance observer-based proportional-type position tracking controller for dc motor,” Int. J. Control Autom. Syst., vol. 16, pp. 2169–2176, 2018. doi: 10.1007/s12555-017-0805-8
    [13]
    D. M. Dawson, J. J. Carroll, and M. Schneider, “Integrator backstepping control of a brush DC motor turning a robotic load,” IEEE Trans. Control Syst. Technol., vol. 2, pp. 233–244, 1994. doi: 10.1109/87.317980
    [14]
    L. Liu, Y.-J. Liu, and C. L. P. Chen, “Adaptive neural network control for a DC motor system with dead-zone,” Nonlinear Dyn., vol. 72, pp. 141–147, 2012.
    [15]
    J. Yao, Z. Jiao, and D. Ma, “Adaptive robust control of DC motors with extended state observer,” IEEE Trans. Ind. Electron., vol. 61, pp. 3630–3637, 2014. doi: 10.1109/TIE.2013.2281165
    [16]
    H. Ma, H. Y. Li, R. Lu, and T. W. Huang, “Adaptive event-triggered control for a class of nonlinear systems with periodic disturbances,” Sci. China Inf. Sci., vol. 63, no. 5, pp. 157–171, 2020. doi: 10.1007/s11432-019-2680-1
    [17]
    W. B. Xiao, L. Cao, H. Y. Li, and R. Q. Lu, “Observer-based adaptive consensus control for nonlinear multi-agent systems with time-delay,” Sci. China Inf. Sci., vol. 63, no. 3, pp. 185–201, 2020. doi: 10.1007/s11432-019-2678-2
    [18]
    H. Li R. Lu Q. Zhou, S. Zhao, and C. Wu, “Adaptive neural network tracking control for robotic manipulators with dead zone,” IEEE Trans. Neural Networks Learn. Syst., vol. 30, pp. 3611–3620, 2019. doi: 10.1109/TNNLS.2018.2869375
    [19]
    S.-K. Kim and C. K. Ahn, “Offset-free proportional-type self-tuning speed controller for permanent magnet synchronous motors,” IEEE Trans. Ind. Electron., vol. 66, pp. 7168–7176, 2019. doi: 10.1109/TIE.2018.2874616
    [20]
    S.-K. Kim, Y. Kim, and C. K. Ahn, “Energy-shaping speed controller with time-varying damping injection for permanent-magnet synchronous motors,” IEEE Trans. Circuits and Systems II:Express Briefs, pp. 1–1, May 2020. DOI: 10.1109/TCSII.2020.2992260
    [21]
    M. Tomita, T. Senjyu, S. Doki, and S. Okuma, “New sensorless control for brushless DC motors using disturbance observers and adaptive velocity estimations,” IEEE Trans. Ind. Electron., vol. 45, pp. 274–282, 1998. doi: 10.1109/41.681226
    [22]
    Z. Chen, M. Tomita, S. Doki, and S. Okuma, “New adaptive sliding observers for position- and velocity-sensorless controls of brushless DC motors,” IEEE Trans. Ind. Electron., vol. 47, pp. 582–591, 2000. doi: 10.1109/41.847899
    [23]
    S.-K. Sul. Control of Electric Machine Drive Systems, vol. 88. Hoboken, NJ, USA: Wiley, 2011.

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    Highlights

    • The elimination of the current feedback considering current dynamics with the use of parameter-independent angular acceleration observers.
    • The observer-based active damping injection control for the pole-zero cancellation resulting in the first-order closed-loop speed dynamics.
    • The observer-based disturbance observer reinforcing the closed-loop robustness with the guarantee of the offset-free property.

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