Decentralized Model Predictive Control of Nonlinear System Using Piesewise LTI Model
Main Article Content
Abstract
This paper proposes a Decentralized Model Predictive Control (DMPC) schema for nonlinear systems consisting of a number of interconnected nonlinear subsystems. Each subsystem is controlled by a model predictive controller using piecewise Linear Time Invariant (LTI) predictive model and the predictive information from other model predictive controllers to predict disturbance and to calculate the optimal control signals with considering the effect of interconnections as perturbation term. Besides, the input-to-state stability (ISS) property of the local subsystems and the global system is guaranteed by some predetermined assumptions. The simulation results on the boiler-turbine system have demonstrated the performance of the proposed approach.
Keywords
Boiler-turbine system, Decentralized model predictive control, Input-to-state stability
Article Details
References
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[14] Balko P. & Rosinová D.. Nonlinear Boiler-Turbine Unit: Modelling and Robust Decentralized Control. IFAC-PapersOnLine 49, 4(2016), pp. 49-54.
[2] Grne L. & Pannek J., Nonlinear Model Predictive Control: Theory and Algorithms, Springer, (2013).
[3] Rawlings J. B. & Mayne D. Q.. Model predictive control: Theory and design, Nob Hill Pub., (2015).
[4] Alessio A. & Bemporad A.; Stability conditions for decentralized model predictive control under packet drop communication; American Control Conference, 2008; IEEE(2008); pp. 3577-3582.
[5] Christofides P. D., et al., Distributed model predictive control: A tutorial review and future research directions. Computers & Chemical Engineering 51, 2013), pp. 21-41.
[6] Richards A. & How J.; Decentralized model predictive control of cooperating UAVs; Decision and Control, 2004. CDC. 43rd IEEE Conference on: IEEE(2004); pp. 4286-4291.
[7] Kayacan E. Peschel J. M. & Kayacan E.; Centralized, decentralized and distributed nonlinear model predictive control of a tractor-trailer system: A comparative study American Control Conference (ACC), 2016; IEEE(2016); pp. 4403-4408.
[8] Farina M.; Giulioni L. & Scattolini R.; Distributed Predictive Control of stochastic linear systems with chance constraints; American Control Conference (ACC), 2016; IEEE(2016); pp. 20-25.
[9] Phạm Văn Hùng: Cao Thành Trung & Hoàng Minh Sơn, Điều khiển dự bảo phi tập trung dựa trên mô hình tuyền tỉnh quá trình phản ứng tách. Tạp chí KHCN các trường đại học, 110(2016), pp. 7-11.
[10] Raimondo D. Magni L. & Scattolini R. Decentralized MPC of nonlinear systems: An input-to-state stability approach. International Journal of Robust and Nonlinear Control 17, 17(2007), pp. 1651-1667.
[11] Naghavi S. V. Safavi A. & Kazerooni M. Decentralized fault tolerant model predictive control of discrete-time interconnected nonlinear systems. Journal of the Franklin Institute 351, 3(2014), pp. 1644-1656.
[12] Phạm Văn Hùng: Nguyễn Đức Anh & Vũ Tiến Thành, Điều khiển bền vững hệ lò hơi - tuabin phi tuyền nhờ bộ điều khiển phản hồi trạng thái với mô hình dự báo tuyển tính và bộ quan sát UKF. Tạp chí Nghiên cứu KH&CN quân sự, 44(2016), pp. 33-43.
[13] Åström K. J. & Bell R., Dynamic models for boiler-turbine alternator units: Data logs and parameter estimation for a 160 MW unit. Technical Reports(1987).
[14] Balko P. & Rosinová D.. Nonlinear Boiler-Turbine Unit: Modelling and Robust Decentralized Control. IFAC-PapersOnLine 49, 4(2016), pp. 49-54.