Journal of Electrical and Computer Engineering Innovations (JECEI)
مقاله 6 ، دوره 7، شماره 2 ، مهر 2019، صفحه 183-194 938.07 K )
نوع مقاله: Original Research Paper
شناسه دیجیتال (DOI): 10.22061/jecei.2020.6795.340 نویسندگان
A. Mirzaei ؛ A. Ramezani*
تاریخ دریافت : 04 مهر 1397 ،
تاریخ بازنگری : 09 اسفند 1397 ،
تاریخ پذیرش : 08 خرداد 1398
چکیده Background and Objectives: In this paper, a constrained cooperative distributed model predictive control (DMPC) is proposed. The proposed DMPC is based on linear adaptive generalized predictive control (AGPC) to control uncertain nonlinear large-scale systems.Methods: The proposed approach, has two main contributions. First, a novel cooperative optimization strategy is proposed to improve the centralized global cost function of each local controller. Second, using the proposed linear distributed AGPC (DAGPC), the mismatch between linearized and nonlinear models is compensated via online identification of the linearized model in each iteration of optimization.Results: The proposed novel cooperative optimization strategy decreases the computational burden of optimization process compared to conventional cooperative DMPC strategies. Moreover, the proposed linear DAGPC decreases the satisfaction time of the terminal condition compared to conventional DMPC methods. The paper establishes sufficient conditions for the closed-loop stability. The performance and effectiveness of proposed method is demonstrated through simulation of a quadruple-tank system for both certain and uncertain situations. The imposed uncertainty changes the system from minimum phase to nonminimum-phase situation. Closed-loop stability and proper convergences are concluded from simulation results of both situations.Conclusion: Most important advantages of proposed linear cooperative DAGPC are its less design complexity and consequently less convergence time compared to fully nonlinear DMPC methods, due to its online identification of the linearized model.کلیدواژهها
Uncertain nonlinear large-scale system ؛ constrained cooperative DMPC ؛ cooperative optimization ؛ linear distributed adaptive generalized predictive control ؛ online identification
مراجع
[1] P. D. Christofides, R. Scattolini, D. M. De la Pena, J. Liu, “Distributed model predictive control: A tutorial review and future research directions,” Journal of Computers and Chemical Engineering, 51: 21-41, 2013.
[2] I. Necoara, V. Nedelcu, I. Dumitrache, “Parallel and distributed optimization methods for estimation and control in networks,” Journal of Process Control, 21(5): 756-766, 2011.
[3] B. T. Stewart, S. J. Wright, J. B. Rawlings, “Cooperative distributed model predictive control for nonlinear systems,” Journal of Process Control, 21(5): 698-704, 2011.
[4] B. T. Stewart, A. N. Venkat, J. B. Rawlings, S. J. Wright, G. Pannocchia, “Cooperative distributed model predictive control,” Systems & Control Letters, 59(8): 460_469, 2010.
[5] T. Bai, S. Li, Y. Zheng, “Distributed Model Predictive Control for Networked Plant-wide Systems With Neighborhood Cooperation,” IEEE/CAA Journal of Automatica Sinica, 6(1): 108-117, 2019.
[6] A. Grancharova, T. A. Johansen, S. Olaru, “Dual-Mode distributed model predictive control of a quadruple-tank system,” Journal of Chemical Technology and Metallurgy, 53(4): 674-682, 2018.
[7] H. N. Soloklo, N. Bigdeli, “A PFC-based Hybrid Approach for Control of Industrial Heating Furnace,” Journal of Electrical and Computer Engineering Innovations, 7(1): 81-92, 2019.
[8] A. Moradmand, A. Ramezani, H. S. Nezhad, A. Sardashti, “Fault Tolerant Kalman Filter-Based Distributed Predictive Control in Power systems Under Governor Malfunction,” 6th International Conference on Control, Instrumentation and Automation (ICCIA): 1-6, 2019.
[9] H. S. Nezhad, A. Sardashti, A. Ramezani, A. Moradmand, “Sensor Fault Detection in a Distributed Power System via Decentralized Kalman Filters. 6th International Conference on Control,” Instrumentation and Automation (ICCIA): 1-6, 2019.
[10] M. Zhao, B. Ding, “Distributed model predictive control for constrained nonlinear systems with decoupled local dynamics,” ISA Transactions, 55: 1-12, 2015.
[11] T. Keviczky, F. Borrelli, K. Fregene, D. Godbole, G. J. Balas, “Decentralized Receding Horizon Control and Coordination of Autonomous Vehicle Formations,” IEEE Transactions on Control Systems Technology, 16(1): 19-33, 2008.
[12] X. Liu, Y. Shi, D. Constantinescu, “Distributed model predictive control of constrained weakly coupled nonlinear systems,” Systems & Control Letters, 74: 41-49, 2014.
[13] W. B. Dunbar, “Distributed Receding Horizon Control of Dynamically Coupled Nonlinear Systems,” IEEE Transactions on Automatic Control, 52(7): 1249-1263, 2007.
[14] M. Heidarinejad, J. Liu, D. M. De la Pena, J. F. Davis, P. D. Christofides, “Multirate Lyapunov-based distributed model predictive control of nonlinear uncertain systems,” Journal of Process Control, 21(9): 1231-1242, 2011.
[15] H. Li, Y. Shi, “Distributed model predictive control of constrained nonlinear systems with communication delays,” Systems & Control Letters, 62(10): 819–826, 2013.
[16] J. Liu, D. M. De la Pena, P. D. Christofides, “Distributed model predictive control of nonlinear systems subject to asynchronous and delayed measurements,” Automatica, 46(1): 52-61, 2010.
[17] X. Liu, Y. Shi, D. Constantinescu, “Robust Distributed Model Predictive Control of Constrained Continuous-Time Nonlinear Systems Using Two-Layer Invariant Sets,” Journal of Dynamic Systems, Measurement, and Control, 138: 1-7, 2016.
[18] A. Ma, K. Liu, Q. Zhang, Y. Xia, “Distributed MPC for linear discrete-time systems with disturbances and coupled states,” Systems & Control Letters, 135: 1-8, 2020.
[19] Z. Guo, S. Li, Y. Zheng, “Feedback Linearization Based Distributed Model Predictive Control for Secondary Control of Islanded Microgrid,” Asian Journal of Control, 22(1): 460–473, 2020.
[20] H. Li, Y. Shi, “Robust Distributed Model Predictive Control of Constrained Continuous-Time Nonlinear Systems: A Robustness Constraint Approach,” IEEE Transactions on Automatic Control, 59(6): 1673- 1678, 2014.
[21] M. Siavash, V. J. Majd, M. Tahmasebi, “Finite-Time Consensus Control of Euler-Lagrange Multi-agent Systems in the Presence of Stochastic Disturbances and Actuator Faults,” Journal of Electrical and Computer Engineering Innovations, 7(2): 163-170, 2019.
[22] A. Moradmand, M. Dorostian, B. Shafai, “Energy Scheduling for Residential Distributed Energy Resources with Uncertainties Using Model-based Predictive Control,” arXiv preprint arXiv:2007.11182, 2020.
[23] S. G. Abokhatwa, R. Katebi, “Sequential Distributed Model Predictive Control for State-Dependent Nonlinear Systems,” IEEE International Conference on Systems, Man, and Cybernetics: 565-570, 2013.
[24] Y. Long, S. Liu, L. Xie, K. H. Johansson, “Distributed nonlinear model predictive control based on contraction theory,” International Journal of Robust and Nonlinear Control, 28(2): 492–503, 2018.
[25] X. Liu, Y. Shi, D. Constantinescu, “Robust distributed model predictive control of constrained dynamically decoupled nonlinear systems: A contraction theory perspective,” Systems & Control Letters, 105: 84-91, 2017.
[26] N. R. Esfahani, K. Khorasani, “A Distributed Model Predictive Control (MPC) Fault Reconfiguration Strategy for Formation Flying Satellites,” International Journal of Control, 89(5): 960-983, 2016.
[27] X. Kong, X. Liu, L. Ma, K. Y. Lee, “Hierarchical Distributed Model Predictive Control of Standalone Wind/Solar/Battery Power System,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, 49(8): 1570-1581, 2019.
[28] E. F. Camacho, C. Bordons, Model Predictive Control, II. London: Springer,:47, 2007.
[29] K. H. Johansson, “The Quadruple-Tank Process: A Multivariable Laboratory Process with an Adjustable Zero,” IEEE Transactions on Control Systems Technology, 8(3), 2000.
آمار
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