A New Cooperative Distributed MPC Method Based on Reduction and Classification

WU Lan; WANG Lei

doi:10.1049/cje.2017.03.007

Volume 26 Issue 3

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WU Lan and WANG Lei, “A New Cooperative Distributed MPC Method Based on Reduction and Classification,” Chinese Journal of Electronics, vol. 26, no. 3, pp. 564-572, 2017, doi: 10.1049/cje.2017.03.007

Citation:

WU Lan and WANG Lei, “A New Cooperative Distributed MPC Method Based on Reduction and Classification,” Chinese Journal of Electronics, vol. 26, no. 3, pp. 564-572, 2017, doi: 10.1049/cje.2017.03.007

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A New Cooperative Distributed MPC Method Based on Reduction and Classification

doi: 10.1049/cje.2017.03.007

WU Lan¹,
WANG Lei²

1.
College of Electrical Engineering, Henan University of Technology, Zhengzhou 450000, China;
2.
Department of Electrical Engineering, Hangzhou First Technician College, Hangzhou 310023, China

Funds: This work is supported by the National Natural Science Foundation of China (No.61503123, No.U1504616), Program for Science & Technology Innovation Talents in Universities of Henan Province (No.17HASTIT021), and Basic and Frontier Science Research Project of Henan Province (No.152300410200).

Received Date: 2016-10-18
Rev Recd Date: 2017-01-09
Publish Date: 2017-05-10

Abstract

Abstract

To decrease the overlarge calculation induced by the centralized processing, a new cooperative distributed Model predictive control (MPC) method is proposed for large-scale systems with coupled dynamics. Reduction and classification are investigated by defining the influence degree to reduce the whole system and then to classify the reduced system into several subsystem groups. These groups are mutually decoupled, while there is relativity between these subsystems comprised in the same group. Centralized/cooperative and distributed MPC algorithms for each group are implemented to ensure the feasibility and the stability of the whole system. Meanwhile, for practical applications, the finite times interactive control strategy between different groups is adopted to compensate information loss brought by the reduced subsystem and realize the global cooperative distributed MPC. This algorithm significantly decreases the computational load, has better control performance. Simulations are given to illustrate the effectiveness of these developed algorithms.
- Large-scale complex system,
- Reduction and classification,
- Influence degree,
- Cooperative distributed Model predictive control (MPC)

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References(18)

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