Agent-Based Decentralized Model Predictive Control for Plants With Multiple Identical Actuators
This article proposes a decentralized model predictive control (DMPC) algorithm without communication for systems consisting of multiple identical, independent actuators acting on a single central plant. The particular system design is relevant for applications where modularity is paramount and for...
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| Veröffentlicht in: | IEEE transactions on control systems technology 2023-03, Vol.31 (2), p.841-855 |
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| container_title | IEEE transactions on control systems technology |
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| creator | Kofler, Sandro Luchini, Elisabeth Schirrer, Alexander Fallmann, Markus Konig, Oliver Kozek, Martin Hametner, Christoph Jakubek, Stefan |
| description | This article proposes a decentralized model predictive control (DMPC) algorithm without communication for systems consisting of multiple identical, independent actuators acting on a single central plant. The particular system design is relevant for applications where modularity is paramount and for highly dynamic systems, e.g., battery emulator systems controlled by multiple dc-dc converter modules. Each agent, consisting of an actuator and a DMPC, controls a virtually scaled version of the plant to implicitly consider the effects of other agents. The set of DMPCs achieves the same plant performance as a corresponding centralized model predictive controller (CMPC) in unconstrained operation. Also, the states of the independent agents converge toward the globally optimal CMPC solution. This is obtained by dividing the CMPC's objective function into local objective functions related to the subsystems. The optimality and stability of the DMPC in unconstrained operation are shown analytically. The stability of control input-constrained operation is analyzed by computing the region of attraction. Numerical studies of a battery emulator system compare the performance of the DMPC with the global optimum in detail. |
| doi_str_mv | 10.1109/TCST.2022.3207354 |
| format | Article |
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The particular system design is relevant for applications where modularity is paramount and for highly dynamic systems, e.g., battery emulator systems controlled by multiple dc-dc converter modules. Each agent, consisting of an actuator and a DMPC, controls a virtually scaled version of the plant to implicitly consider the effects of other agents. The set of DMPCs achieves the same plant performance as a corresponding centralized model predictive controller (CMPC) in unconstrained operation. Also, the states of the independent agents converge toward the globally optimal CMPC solution. This is obtained by dividing the CMPC's objective function into local objective functions related to the subsystems. The optimality and stability of the DMPC in unconstrained operation are shown analytically. The stability of control input-constrained operation is analyzed by computing the region of attraction. 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The particular system design is relevant for applications where modularity is paramount and for highly dynamic systems, e.g., battery emulator systems controlled by multiple dc-dc converter modules. Each agent, consisting of an actuator and a DMPC, controls a virtually scaled version of the plant to implicitly consider the effects of other agents. The set of DMPCs achieves the same plant performance as a corresponding centralized model predictive controller (CMPC) in unconstrained operation. Also, the states of the independent agents converge toward the globally optimal CMPC solution. This is obtained by dividing the CMPC's objective function into local objective functions related to the subsystems. The optimality and stability of the DMPC in unconstrained operation are shown analytically. The stability of control input-constrained operation is analyzed by computing the region of attraction. 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| subjects | Actuators Algorithms Control stability Control systems Decentralized control Dynamical systems Emulators Linear programming Manganese Modularity Optimization power electronics Predictive control predictive control for linear systems Predictive models Stability analysis Stability criteria Subsystems Systems design |
| title | Agent-Based Decentralized Model Predictive Control for Plants With Multiple Identical Actuators |
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