Abstract
In this article, an adaptive fuzzy finite time command filter control scheme is first proposed for a single machine infinite power system with static VAR compensator (SVC). The unknown external interference is taken into account in the controller design, and the incomplete single machine infinite SVC power system is formulated in terms of fuzzy logic system. The devised adaptive backstepping control scheme employs command filter technology and finite time control theory. Further more, it is proven that the designed controller ensures the rotor power angle in the power system converge to the expected value and all variables of the control systems are semi-global practical finite-time stable (SGPFS). Finally, simulation results are presented to show the effectiveness of the proposed control scheme.
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References
Y. Wang, Y. Tan, and G. Guo, “Robust nonlinear coordinated generator excitation and SVC control for power systems,” International Journal of Electrical Power & Energy Systems, vol. 22, no. 3, pp. 187–195, 2000.
C. Yan, Y. Sun, and Q. Lu, “Nonlinear controller design of SVC by exact linearization method,” Tsinghua Univ, vol. 33, no. 1, pp. 18–24, 1993.
Y. Ruan and J. Wang, “The coordinated control of SVC and excitation of generators in power systems with nonlinear loads,” International Journal of Electrical Power and Energy Systems, vol. 22, no. 3, pp. 187–195, 2000.
F. Shi, J. Wang, and G. Xue, “Coordinated excitation and SVC control based on Hamilton theory for improving transient stability of multi-machine power system,” Electric Power Automation Equipment, vol. 32, no. 10, pp. 48–52, 2012.
R. Yan, Z. Dong and T. K. Saha, “Nonlinear robust adaptive SVC controller design for power systems,” Proc. of 2008 IEEE Power and Energy Society General Meeting-Conversion and Delivery of Electrical Energy in the 21st Century. IEEE, pp. 1–7, 2008.
M. Khaleghi and M.-M. Farsangi, “Voltage stability improvement by multi-objective placement of SVC using modified artificial immune network algorithm,” Proc. of 2009 IEEE Power & Energy Society General Meeting, IEEE, pp. 1–7, 2009.
Y. Xu, P. Cui, J. Wang, and Y. Lin, “Adaptive dynamic surface control for generator excitation control system,” Mathematical Problems in Engineering, vol. 2014, Article ID 481936, 2014.
Y. Chang, “Robust neural network-based control of static VAR compensator,” IET Power Electronics, vol. 7, no. 8, pp. 1964–1977, 2014.
G. Zhu, L. Nie, Z. Lv, L. Sun, X. Zhang, and C. Wang, “Adaptive fuzzy dynamic surface sliding mode control of large-scale power systems with prescribe output tracking performance,” ISA transactions, vol. 99, pp. 305–321, 2020.
X. Zhang, Y. Wang, X. Chen, C. Su, Z. Li, C. Wang, and Y. Peng, “Decentralized adaptive neural approximated inverse control for a class of large-scale nonlinear hysteretic systems with time delays,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 49, no. 12, pp. 2424–2437, 2018.
G. Zhu, L. Nie, M. Zhou, X. Zhang, L. Sun and C. Zhong, “Adaptive fuzzy dynamic surface control for multi-machine power system based on composite learning method and disturbance observer,” IEEE Access, vol. 8, pp. 163163–163175, 2020.
X. Zhang, S. Wang, G. Zhu, J. Ma, X. Li, and X. Chen, “Decentralized robust adaptive neural dynamic surface control for multi-machine excitation systems with static VAR compensator,” International Journal of Adaptive Control and Signal Processing, vol. 33, no. 1, pp. 92–113, 2019.
G. Zhu, S. Ji, Z. Li, and Y. Zhang, “Adaptive dynamic surface integral sliding mode fault-tolerant control for multi-machine excitation systems with SVC,” Complexity, vol. 2020, Article ID 610794, 2020.
L. Sun and Y. Liu, “Nonlinear adaptive backstepping controller design for static VAR compensator,” Proc. of Chinese Control and Decision Conference, pp. 3013–3018, 2010.
B. Lei, S. Fei, J. Zhai, J. Zhai, and W. Xiang, “Nonlinear adaptive dynamic surface control of static VAR compensator for improving power system transient stability,” Proc. of Proceedings of the 32nd Chinese Control Conference, IEEE, pp. 343–347, 2013.
L. Sun, S. Tong, and Y. Liu, “Adaptive backstepping sliding mode H∞ control of static VAR compensator,” IEEE Transactions on Control Systems Technology, vol. 19, no. 5, pp. 1178–1185, 2010.
Y. Xu, S. Tong, and Y. Li, “Adaptive fuzzy backstepping control of static VAR compensator based on state observer,” Nonlinear Dynamics, vol. 73, no. 3, pp. 133–142, 2013.
Y. Li, S. Tong, and T. Li, “Robust nonlinear coordinated generator excitation and SVC control for power systems,” Nonlinear Dynamics, vol. 73, no. 1–2, pp. 133–142, 2013.
P. Du, K. Sun, S. Zhao, and H. Liang, “Observer-based adaptive fuzzy control for time-varying state constrained strict-feedback nonlinear systems with dead-zone,” International Journal of Fuzzy Systems, vol. 21, no. 3, pp. 733–744, 2019.
W. He and S. Yang, “Cooperative control of a nonuniform gantry crane with constrained tension,” Automatica, vol. 18, no. 2, pp. 363–373, 2020.
W. He, T. Meng, X. He, and S. Ge, “Unified iterative learning control for flexible structures with input constraints,” Automatica, vol. 18, no. 2, pp. 363–373, 2020.
H. Shen, S. Jiao, J.-H. Park, and V. Sreeram, “An improved result on H∞ load frequency control for power systems with time delays,” IEEE Systems Journal, 2020. DOI: https://doi.org/10.1109/JSYST.2020.3014936
J. Wang, J. Xia, H. Shen, M. Xing, and J.-H. Park, “H∞ synchronization for fuzzy Markov jump chaotic systems with piecewise-constant transition probabilities subject to PDT switching rule,” IEEE Transactions on Fuzzy Systems, 2020. DOI: https://doi.org/10.1109/TFUZZ.2020.3012761
Z. Chen, F. Huang, C. Yang, and B. Yao, “Adaptive fuzzy backstepping control for stable nonlinear bilateral teleoperation manipulators with enhanced transparency performance,” IEEE Transactions on Industrial Electronics, vol. 67, no. 1, pp. 746–756, 2019.
Y. Gao, J. Liu, Z. Wang, and L. Wu, “Interval type-2 FNN-based quantized tracking control for hypersonic flight vehicles with prescribed performance,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 51, no. 3, pp. 1981–1993, 2021.
G. Xia and T. Luan, “Study of ship heading control using RBF neural network,” International Journal of Control and Automation, vol. 8, no. 10, pp. 227–236, 2015.
Y. Luo, S. Zhao, D. Yang, and H. Zhang, “A new robust adaptive neural network backstepping control for single machine infinite power system with TCSC,” IEEE/CAA Journal of Automatica Sinica, vol. 7, no. 1, pp. 48–56, 2020.
S.-P. Bhat and D.-S. Bernstein, “Continuous finite-time stabilization of the translational and rotational double integrators,” IEEE Transactions on Automatic Control, vol. 43, no. 5, pp. 678–682, 1998.
S.-P. Bhat and D.-S. Bernstein, “Finite-time stability of continuous autonomous systems,” SIAM Journal on Control and Optimization, vol. 38, no. 3, pp. 751–766, 2000.
X. Lu and Y. Xia, “Adaptive attitude tracking control for rigid spacecraft with finite-time convergence,” Automatica, vol. 49, no. 12, pp. 3591–3599, 2013.
Y. Shen and Y. Huang, “Global finite-time stabilisation for a class of nonlinear systems,” International Journal of Systems Science, vol. 43, no. 1, pp. 73–78, 2012.
M. Shahvali, M. B. Naghibi-Sistani, and J. Askari, “Adaptive output feedback bipartite consensus for nonstrict-feedback nonlinear multi-agent systems: A finite-time approach,” Neurocomputing, vol. 318, pp. 7–17, 2018.
S. Sui, C. Chen, and S. Tong, “Fuzzy adaptive finite-time control design for nontriangular stochastic nonlinear systems,” IEEE Transactions on Fuzzy Systems, vol. 27, no. 1, pp. 172–184, 2019.
S. Sui, C. Chen, and S. Tong, “Finite-time filter decentralized control for nonstrict-feedback nonlinear large-scale systems,” IEEE Transactions on Fuzzy Systems, vol. 26, no. 6, pp. 3289–3300, 2018.
H. Wang, B. Chen, C. Lin, Y. Sun, and F. Wang, “Adaptive finite-time control for a class of uncertain high-order nonlinear systems based on fuzzy approximation,” IET Control Theory and Applications, vol. 11, no. 5, pp. 677–684, 2017.
W. Lv and F. Wang, “Adaptive fuzzy finite-time control for uncertain nonlinear systems with asymmetric actuator backlash,” International Journal of Fuzzy Systems, vol. 21, no. 1, pp. 50–59, 2019.
C. Chang, C.-F. H. Su, and T.-T. Lee, “Backstepping-based finitetime adaptive fuzzy control of unknown nonlinear systems,” International Journal of Fuzzy Systems, vol. 20, no. 8, pp. 2545–2555, 2018.
X. Zhang, G. Feng, and Y. Sun, “Finite-time stabilization by state feedback control for a class of time-varying nonlinear systems,” Automatica, vol. 48, no. 3, pp. 499–504, 2012.
B. Xiao, Q. Hu, and Y. Zhang, “Finite-time attitude tracking of spacecraft with fault-tolerant capability,” IEEE Transactions on Control Systems and Technology, vol. 23, no. 4, pp. 1338–1350, 2015.
M. Cai and Z. Xiang, “Adaptive fuzzy finite-time control for a class of switched nonlinear systems with unknown control coefficients,” Neurocomputing, pp. 105–115, 2015.
W. Lv and F. Wang, “Finite-time adaptive fuzzy tracking control for a class of nonlinear systems with unknown hysteresis,” International Journal of Fuzzy Systems, vol. 20, no. 3, pp. 782–790, 2018.
W. Lv, F. Wang, and L. Zhang, “Adaptive fuzzy finite-time control for uncertain nonlinear systems with dead-zone input,” International Journal of Control Automation and Systems, vol. 16, no. 5, pp. 2549–2558, 2018.
Y. Li, K. Li, and S. Tong, “Finite-time adaptive fuzzy output feedback dynamic surface control for MIMO nonstrict feedback systems,” IEEE Transactions on Fuzzy Systems, vol. 27, no. 1, pp. 96–110, 2018.
J. Yu, L. Zhao, and H. Yu, “Fuzzy finite-time command filtered control of nonlinear systems with input saturation,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 48, no. 8, pp. 2378–2387, 2018.
J. Yu, P. Shi, and L. Zhao, “Finite-time command filtered backstepping control for a class of nonlinear systems,” Automatica, vol. 92, no. 92 pp. 173–180, 2018.
H. Wang, S. Kang, and Z. Feng, “Finite-time adaptive fuzzy command filtered backstepping control for a class of nonlinear systems,” International Journal of Fuzzy Systems, vol. 21, no. 8, pp. 2575–2587, 2019.
L. Sun, S. Tong, and Y. Liu, “Adaptive backstepping sliding mode control of static VAR compensator,” IEEE Transactions on Control Systems and Technology, vol. 19, pp. 178–1185, 2011.
F. Wang, B. Chen, C. Lin, J. Zhang and X. Meng, “Adaptive neural network finite-time output feedback control of quantized nolinear systems,” IEEE Transactions on Cybernetics, vol. 48, no. 6, pp. 1839–1848, 2018.
A. Levant, “Higher-order sliding modes, differentiation and output feedback control,” Int. J. Control, vol. 76, pp. 924–941, 2003.
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Recommended by Associate Editor Zhijia Zhao under the direction of Editor Euntai Kim
This work was supported in part by the National Natural Science Foundation of China under Grants 61703059, 61873144, 61876121, by the China Post-Doctoral Science Foundation under Grant 2018 632621, by the Natural Science Foundation of Jiangsu Province under Grant BK20170291, by the Science and Technology Development Plan Project of Suzhou City under Grant SS202024, and by the key Reaearch and development plan of Jiangsu Provinceunder Grant BE2017663.
Wangyao Xu received his Bachelor’s degree in electric and information engineering from Suzhou University of Science and Technology in 2019. His research interests include adaptive fuzzy control and nonlinear systems.
Ze Li received her Bachelor’s degree and Ph.D. degree in the School of Automation, Nanjing University of Science and Technology, in 2005 and 2010, respectively. She is now an Associate Professor in the School of Electronic and Information Engineering, Suzhou University of Science and Technology, associated with the Intelligent Control and Information Processing Group. Her research interests lie in the fields of control and filtering problem for fuzzy system, stochastic system and unmanned surface vehicle.
Guozeng Cui received his B.Sc. degree in applied mathematics from the Shandong University of Technology, Zibo, China, in 2009, and an M.Sc. degree in applied mathematics from Qufu Normal University, Qufu, China, in 2012, and a Ph.D. degree in control science and engineering from the Nanjing University of Science and Technology, Nanjing, China, in 2016. He is currently a Lecturer with the School of Electronic and Information Engineering, Suzhou University of Science and Technology. His current research interests include adaptive control, intelligent control for nonlinear systems, and multi-agent systems.
Chengxi Wang received his Bachelor degree in electric and information engineering from Suzhou University of Science and Technology in 2019. His research interests include model predictive control and nonlinear systems.
Fuyuan Hu received his Ph.D. degree in Northwest Polytechnic University in 2007. He is now a Professor in the School of Electronic and Information Engineering, Suzhou University of Science and Technology. His research interests are machine learning and computer vision.
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Xu, W., Li, Z., Cui, G. et al. Fuzzy Adaptive Finite Time Command Filter Backstepping Control of Power System. Int. J. Control Autom. Syst. 19, 3812–3822 (2021). https://doi.org/10.1007/s12555-020-0466-x
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DOI: https://doi.org/10.1007/s12555-020-0466-x