Distributed model predictive control based on adaptive sampling mechanism

doi:10.1016/j.cjche.2020.10.048

中国化学工程学报 ›› 2021, Vol. 39 ›› Issue (11): 193-204.DOI: 10.1016/j.cjche.2020.10.048

• Process Systems Engineering and Process Safety • 上一篇下一篇

Distributed model predictive control based on adaptive sampling mechanism

Zhen Wang¹, Aimin An^1,2,3, Qianrong Li³

1 College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China;
2 Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou 730050, China;
3 National Experimental Teaching Center of Electrical and Control Engineering, Lanzhou 730050, China

收稿日期:2020-05-12 修回日期:2020-09-14 出版日期:2021-11-28 发布日期:2021-12-27
通讯作者: Aimin An
基金资助:
This work was supported by the National Natural Science Foundation of China (61563032, 61963025), The Open Foundation of the Key Laboratory of Gansu Advanced Control for Industrial Processes (2019KX01), and The Project of Industrial support and guidance of Colleges and Universities in Gansu Province (2019C05).

Distributed model predictive control based on adaptive sampling mechanism

Zhen Wang¹, Aimin An^1,2,3, Qianrong Li³

1 College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China;
2 Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou 730050, China;
3 National Experimental Teaching Center of Electrical and Control Engineering, Lanzhou 730050, China

Received:2020-05-12 Revised:2020-09-14 Online:2021-11-28 Published:2021-12-27
Contact: Aimin An
Supported by:
This work was supported by the National Natural Science Foundation of China (61563032, 61963025), The Open Foundation of the Key Laboratory of Gansu Advanced Control for Industrial Processes (2019KX01), and The Project of Industrial support and guidance of Colleges and Universities in Gansu Province (2019C05).

摘要/Abstract

摘要： In this work, an adaptive sampling control strategy for distributed predictive control is proposed. According to the proposed method, the sampling rate of each subsystem of the accused object is determined based on the periodic detection of its dynamic behavior and calculations made using a correlation function. Then, the optimal sampling interval within the period is obtained and sent to the corresponding sub-prediction controller, and the sampling interval of the controller is changed accordingly before the next sampling period begins.In the nextcontrolperiod, the adaptive sampling mechanism recalculates the sampling rate of each subsystem's measurable output variable according to both the abovementioned method and the change in the dynamic behavior of the entire system, and this process is repeated. Such an adaptive sampling interval selection based on an autocorrelation function that measures dynamic behavior can dynamically optimize the selection of sampling rate according to the real-time change in the dynamic behavior of the controlled object. It can also accurately capture dynamic changes, meaning that each sub-prediction controller can more accurately calculate the optimal control quantity at the next moment, significantly improving the performance of distributed model predictive control (DMPC). A comparison demonstrates that the proposed adaptive sampling DMPC algorithm has better tracking performance than the traditional DMPC algorithm.

关键词: Chemical process, Distributed model predictive control, Adaptive sampling mechanism, Optimal sampling interval, System dynamic behavior

Abstract: In this work, an adaptive sampling control strategy for distributed predictive control is proposed. According to the proposed method, the sampling rate of each subsystem of the accused object is determined based on the periodic detection of its dynamic behavior and calculations made using a correlation function. Then, the optimal sampling interval within the period is obtained and sent to the corresponding sub-prediction controller, and the sampling interval of the controller is changed accordingly before the next sampling period begins.In the nextcontrolperiod, the adaptive sampling mechanism recalculates the sampling rate of each subsystem's measurable output variable according to both the abovementioned method and the change in the dynamic behavior of the entire system, and this process is repeated. Such an adaptive sampling interval selection based on an autocorrelation function that measures dynamic behavior can dynamically optimize the selection of sampling rate according to the real-time change in the dynamic behavior of the controlled object. It can also accurately capture dynamic changes, meaning that each sub-prediction controller can more accurately calculate the optimal control quantity at the next moment, significantly improving the performance of distributed model predictive control (DMPC). A comparison demonstrates that the proposed adaptive sampling DMPC algorithm has better tracking performance than the traditional DMPC algorithm.

Key words: Chemical process, Distributed model predictive control, Adaptive sampling mechanism, Optimal sampling interval, System dynamic behavior

Zhen Wang, Aimin An, Qianrong Li. Distributed model predictive control based on adaptive sampling mechanism[J]. 中国化学工程学报, 2021, 39(11): 193-204.

Zhen Wang, Aimin An, Qianrong Li. Distributed model predictive control based on adaptive sampling mechanism[J]. Chinese Journal of Chemical Engineering, 2021, 39(11): 193-204.

参考文献

[1] B.T. Stewart, A.N. Venkat, J.B. Rawlings, S.J. Wright, G. Pannocchia, Cooperative distributed model predictive control, Syst. Control. Lett. 59(8) (2010) 460-469.
[2] X.M. Zhu, Study on the influence of proportion coefficient and sampling period on the stability of digital system, J. Nanjing Normal Univ. (Eng. Technol. Ed.) (3) (2011) 8-11.
[3] A.L. Ma, L.F. Zhou, et al., Study on the optimal sampling period of multi rate predictive control system, Comput. Appl. Chemi. 27(1) (2010) 77-81.
[4] S. Roshany-Yamchi, R.R. Negenborn, M. Cychowski, B. de Schutter, J. Connell, K. Delaney, Distributed model predictive control and estimation of large-scale multi-rate systems, IFAC Proc. 44(1) (2011) 416-422.
[5] T. Wang, H. Gao, J. Qiu, A combined adaptive neural network and nonlinear model predictive control for multirate networked industrial process control, IEEE Trans. Neural Networks Learn. Syst. 27(2) (2016) 416-425.
[6] J.H. Lee, M.S. Gelormino, M. Morarih, Model predictive control of multi-rate sampled-data systems:A state-space approach, Int. J. Control 55(1) (1992) 153-191.
[7] M. Ohshima, I. Hashimoto, H. Ohno, M. Takeda, T. Yoneyama, F. Gotoh, Multirate multivariable model predictive control and its application to a polymerization reactor, Int. J. Control. 59(3) (1994) 731-742.
[8] M. Heidarinejad, J. Liu, D. Muñoz de la Peña, J.F. Davis, P.D. Christofides, Multirate Lyapunov-based distributed model predictive control of nonlinear uncertain systems, J. Process. Control. 21(9) (2011) 1231-1242.
[9] X. Liu, R. Gao, A direct robust predictive control algorithm for multi-rate systems, In:Proceedings of 2013 Chinese Intelligent Automation Conference (2013).
[10] Y.L. Jiang, Y.Y. Zou, Y.G. Niu, Explicit model predictive control for multi-rate piecewise linear systems, Acta Autom. Sin. 39(5) (2014) 631-636. (in Chinese)
[11] R.A. Wright, C. Kravaris, Two-degree-of-freedom multirate controllers for nonlinear processes, IFAC Proc. 37(9) (2004) 95-100.
[12] F. Kennel, S. Liu, Iterative learning control for periodic systems using model predictive methods with adaptive sampling rates, IFAC Proc. 47(3) (2014) 158-163.
[13] X. Yin, J. Liu, Distributed moving horizon state estimation of two-time-scale nonlinear systems, Automatica 79(2017) 152-161.
[14] I. Mizumoto, N. Watanabe, Z. Iwai, Fast rate adaptive output feedback control of multirate sampled systems with an adaptive output estimator, 2009 Fourth International Conference on Innovative Computing, Information and Control (ICICIC). Kaohsiung, Taiwan, China. IEEE, (2009) 165-168.
[15] I. Mizumoto, Y. Fujimoto, Adaptive predictive control system design with an adaptive output estimator, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC). Maui, HI, USA, IEEE, (2012) 5434-5441
[16] I. Mizumoto, M. Ikejiri, T. Takagi, Stable adaptive predictive control system design via adaptive output predictor for multi-rate sampled systems, IFACPapersOnLine 48(8) (2015) 1039-1044.
[17] K. Hashimoto, S. Adachi, D.V. Dimarogonas, Event-triggered intermittent sampling for nonlinear model predictive control, Automatica 81(2017) 148- 155.
[18] L. Dai, Research on distributed stochastic model predictive control method PhD Thesis, Beijing University of Technology, China, 2016, pp. 31-45.
[19] X.Z. Kong, Design and implementation of adaptive variable sampling rate and resolution data acquisition system, PhD Thesis, Fudan University, China, 2009.
[20] S.A. Billings, L.A. Aguirre, Effects of the sampling time on the dynamics and identification of nonlinear models, Int. J. 05(06) (1995) 1541-1556.
[21] H. Zhang, X.H. Yang, Statistical control chart of variable sampling interval in fixed time domain, Math. Statistics Manage. 1(2003) 45-48.
[22] Q.M. Shao, A. Cinar, System identification and distributed control for multirate sampled systems, J. Process. Control. 34(2015) 1-12.
[23] Roger, Liu Xiaohua, Networked multi rate sampling predictive control and its stability analysis, In:2008 proceedings of China Control and Decision Conference, IEEE, (2008) 5229-5233
[24] S.B. Wang, P.H. Hu, L. Lin, State feedback predictive control based on T-S fuzzy model, Control. Theory Appl. 24(5) (2007) 819-824. (in Chinese)
[25] M.A. Henson, D.E. Seborg, Input-output linearization of general nonlinear processes, AIChE J. 36(11) (1990) 1753-1757.
[26] Y. Cao, Z.J. Yang, Multiobjective process controllability analysis, Comput. Chem. Eng. 28(1-2) (2004) 83-90.

Distributed model predictive control based on adaptive sampling mechanism

Distributed model predictive control based on adaptive sampling mechanism

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