Distributed model predictive control based on adaptive sampling mechanism

doi:10.1016/j.cjche.2020.10.048

Chinese Journal of Chemical Engineering ›› 2021, Vol. 39 ›› Issue (11): 193-204.DOI: 10.1016/j.cjche.2020.10.048

• Process Systems Engineering and Process Safety • Previous Articles Next Articles

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:2021-12-27 Published:2021-11-28
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).

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

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).

Abstract

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

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

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.

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.

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Distributed model predictive control based on adaptive sampling mechanism

Distributed model predictive control based on adaptive sampling mechanism

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