Hierarchical cooperative distributed model predictive control

Stewart, Brett T.; Rawlings, James B.; Wright, Stephen J.

doi:10.1109/acc.2010.5530634

Cited by 22 publications

(11 citation statements)

References 6 publications

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“…Thus the information exchange among systems is approaching to the true interaction of subsystems. From the perspective of communication burden, Stewart proposed an information exchanging strategy through the lowered communication frequency among subsystems [14]. In this method, the subsystems with the same time delays will be clustered into the same group and each group selects a representative to interact with the representatives from other groups.…”

Section: Introductionmentioning

confidence: 99%

Global Coordinated Distributed Predictive Control Algorithm Based on System Dynamic Decomposition Index

Tang

Zhang

et al. 2019

IEEE Access

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Distributed model predictive control algorithms have been widely used in the field of process control of large system. However, there is no effective way to deal with subsystem decomposition and subsystem coordination strategies. This paper proposes a global coordinated distributed predictive control algorithm based on system dynamic decomposition index to solve this problem. Firstly, the dynamic partial least squares algorithm is adopted to decompose the large system into individual subsystems. In the decomposition process a correlation index matrix is constructed, which is simultaneously applied to weight the interaction of subsystems in the control strategy design of each subsystem. By this way the control coordination among subsystems is based on the true interaction rather than blindness. Finally, the proposed algorithm was applied to the chemical process of Shell Heavy Oil Fractionator to verify its effectiveness and feasibility.

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Section: Introductionmentioning

confidence: 99%

Global Coordinated Distributed Predictive Control Algorithm Based on System Dynamic Decomposition Index

Tang

Zhang

et al. 2019

IEEE Access

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“…In some works, a dedicated assumptions regarding the nature of the coupling signals are introduced in order to avoid strong destabilizing coupling effects. For instance, the framework of [17,10] assumes only coupling through control input actions while in [11] a two layer hierarchical structure is studied where the the higher layer system is slow with control input given by the lower level. Such assumptions circumvent the major difficulties in strongly coupled systems where a typical fixed-point iterations lies beneath with a potential risk of non convergence during the iterations between the coordinator and the subsystems.…”

Section: Introductionmentioning

confidence: 99%

Fixed-point based hierarchical MPC control design for a cryogenic refrigerator

Alamir

Bonnay

Bonne

et al. 2017

Journal of Process Control

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In this paper, a simple and general hierarchical control framework is proposed and validated through the interconnection of the Joule-Thomson and the Brayton cycle stages of a cryogenic refrigerator. The proposed framework enables to handle the case of destabilizing interconnections through state and/or control signals (which is the case of the cryogenic refrigerator example). Moreover, it offers the possibility to simply change the behavior of the overall system (depending on the context) by only changing the coordinator problem's parameters without changing the set of local controllers used by subsystems which is a common industrial requirement regarding industrial control architectures. Finally, the proposed scheme enables a smooth operator handover on a specific subsystem and/or actuator.

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“…To provide scope for managing the communication overheads, motivated by the initial idea presented in [3], in [4] a distributed model predictive control method with hierarchical (two-level) architecture for communication and a three-step algorithm including an extra outer iterate step are presented for solving constrained linear quadratic optimal control problems. Hence, this method seems to be more suitable to overcome the above computational scalability problem.…”

Section: Introductionmentioning

confidence: 99%

Distributed model predictive control methods for improving transient response of automated irrigation channels

Khodabandehlou

Farhadi

Parsa

2015

2015 23rd Iranian Conference on Electrical Engineering

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This paper presents distributed model predictive control methods for improving the transient response of feedback-controlled irrigation channels by managing the water-level reference set points across the irrigation season. The implemented distributed model predictive control methods exploit a two-level and single-level architectures for communication. For an automated irrigation channel, the satisfactory performance of the method with two-level architecture for communication in improving the transient response of automated irrigation channel is illustrated using computer simulation and compared with the performance of the distributed model predictive control method that exploits a single-level architecture for communication. It is illustrated that the distributed model predictive control method that exploits a two-level architecture for communication has a better performance in improving the transient response by better managing communication overhead.

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Hierarchical cooperative distributed model predictive control

Cited by 22 publications

References 6 publications

Global Coordinated Distributed Predictive Control Algorithm Based on System Dynamic Decomposition Index

Global Coordinated Distributed Predictive Control Algorithm Based on System Dynamic Decomposition Index

Fixed-point based hierarchical MPC control design for a cryogenic refrigerator

Distributed model predictive control methods for improving transient response of automated irrigation channels

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