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Synchronization and tracking control for dual-motor driving servo systems with friction compensation. (English) Zbl 1422.93149

Summary: A nonsingular fast terminal sliding mode (NFTSM) controller is designed by incorporating the variable gain neural network (NN) observer, which is utilized to guarantee motor speed synchronization and load position tracking of dual-motor driving servo systems. By designing the variable gain NN observer, the states and uncertain nonlinearities of servo systems are estimated with fast convergence rate and small steady-state error, where the effects from external disturbance are suppressed as well. Based on the estimated states, the cross-coupling synchronization strategy and NFTSM tracking scheme are designed to achieve the rapid speed synchronization and precise load tracking, where the NNs are introduced to approximate and compensate friction nonlinearities. In particular, a novel nonlinear synchronization factor characterizing the degree of speed synchronization is proposed to achieve switching between synchronization control and tracking control, which is proven to deal with the coupling problem of synchronization and tracking. Finally, the comparative simulations and experiments are included to verify the reliability and effectiveness.

MSC:

93D05 Lyapunov and other classical stabilities (Lagrange, Poisson, \(L^p, l^p\), etc.) in control theory
93B40 Computational methods in systems theory (MSC2010)
93B12 Variable structure systems
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[1] Li, Z., J.Chen, G.Zhang, et al., “Adaptive robust control of servo mechanisms with compensation for nonlinearly parameterized dynamic friction,” IEEE Trans. Control Syst. Technol., Vol. 21, No. 1, pp. 193-202 (2013).
[2] Chen, Z., B.Yao, and Q.Wang, “Adaptive robust precision motion control of linear motors with integrated compensation of nonlinearities and bearing flexible modes,” IEEE Trans. Ind. Inform., Vol. 9, No. 2, pp. 965-973 (2013).
[3] Wu, J., J.Huang, Y.Wang, et al., “Nonlinear disturbance observer‐based dynamic surface control for trajectory tracking of pneumatic muscle system,” IEEE Trans. Control Syst. Technol., Vol. 22, No. 2, pp. 440-455 (2014).
[4] Chu, M. Q. and H. H.Jia, “Backstepping control for flexible joint with friction using wavelet neural networks and L2‐gain approach,” Asian J. Control, Vol. 20, No. 4, pp. 1-11 (2018).
[5] Xia, D., L.Wang, and T.Chai, “Neural‐network‐friction compensation‐based energy swing‐up control of pendubot,” IEEE Trans. Ind. Electron., Vol. 61, No. 3, pp. 1411-1423 (2014).
[6] Wang, Y. F., D. H.Wang, and T. Y.Chai, “Modeling and control compensation of nonlinear friction using adaptive fuzzy systems,” Mech. Syst. Signal Process., Vol. 23, No. 8, pp. 2445-2457 (2009).
[7] Khalil, H. K. and L.Praly, “High‐gain observers in nonlinear feedback control,” Int. J. Robust Nonlinear Control, Vol. 24, No. 6, pp. 993-1015 (2014). · Zbl 1291.93054
[8] Naifar, O., A.Ben Makhlouf, M. A.Hammami, et al., “Global practical Mittag Leffler stabilization by output feedback for a class of nonlinear fractional‐order systems,” Asian J. Control, Vol. 20, No. 1, pp. 599-607 (2018). · Zbl 1391.93176
[9] Na, J., X.Ren, and D.Zheng, “Adaptive control for nonlinear pure‐feedback systems with high‐order sliding mode observer,” IEEE Trans. Neural Netw. Learn. Syst., Vol. 24, No. 3, pp. 370-382 (2013).
[10] Lin, F., Y.Hung, J.Chen, et al., “Sensorless inverter‐fed compressor drive system using back‐EMF estimator with PIDNN torque observer,” Asian J. Control, Vol. 16, No. 4, pp. 1042-1056 (2014). · Zbl 1300.93161
[11] Dinh, H. T., R.Kamalapurkar, S.Bhasin, et al., “Dynamic neural network‐based robust observers for uncertain nonlinear systems,” Neural Netw., Vol. 60, pp. 44-52 (2014). · Zbl 1323.93022
[12] Lin, F. J., S. G.Chen, and I. F.Sun, “Adaptive backstepping control of six‐phase PMSM using functional link radial basis function network uncertainty observer,” Asian J. Control, Vol. 19, No. 6, pp. 2255-Ű2269 (2017). · Zbl 1386.93164
[13] Sun, D., X.Shao, and G.Feng, “A model‐free cross‐coupled control for position synchronization of multi‐axis motions: theory and experiments,” IEEE Trans. Control Syst. Technol., Vol. 15, No. 2, pp. 306-314 (2007).
[14] Xiao, Y. and K. Y.Zhu, “Optimal synchronization control of high‐precision motion systems,” IEEE Trans. Ind. Electron., Vol. 53, No. 4, pp. 1160-1169 (2006).
[15] Sun, G., X.Ren, and D.Li, “Neural active disturbance rejection output control of multimotor servomechanism,” IEEE Trans. Control Syst. Technol., Vol. 23, No. 2, pp. 746-753 (2015).
[16] Na, J., Q.Chen, X.Ren, et al., “Adaptive prescribed performance motion control of servo mechanisms with friction compensation,” IEEE Trans. Ind. Electron., Vol. 61, No. 1, pp. 486-494 (2014).
[17] Zhao, H. and X.Zhou, “Backstepping adaptive control of dual‐motor driving servo system,” Control Theory Applicat., Vol. 28, No. 5, pp. 745-751 (2011).
[18] Tao, G., X.Ma, and Y.Ling, “Optimal and nonlinear decoupling control of systems with sandwiched backlash,” Automatica, Vol. 37, No. 2, pp. 165-176 (2001). · Zbl 0959.93500
[19] Mezouki, R., J. A.Davila, and L.Fridman, “Backlash phenomenon observation and identification in electromechanical systems,” Control Eng. Practice, Vol. 15, No. 4, pp. 447-457 (2007).
[20] Lu, L., B.Yao, Q.Wang, et al., “Adaptive robust control of linear motors with dynamic friction compensation using modified LuGre model,” Automatica, Vol. 45, No. 12, pp. 2890-2896 (2009). · Zbl 1192.93060
[21] Hua, C., C.Yu, and X.Guan, “Neural network observer‐based networked control for a class of nonlinear systems,” Neurocomputing, Vol. 133, pp. 103-110 (2014).
[22] Zou, A. M., K. D.Kumar, Z. G.Hou, et al., “Finite‐time attitude tracking control for spacecraft using terminal sliding mode and Chebyshev neural network,” IEEE Trans. Syst. Man Cybern. B: Cybern., Vol. 41, No. 4, pp. 950-963 (2011).
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