Proposes a two-layer MPC architecture with set-based methods for distributed constraint management that delivers recursive feasibility and resembles classical MPC formulations.
Network-Realised Model Predictive Control Part I: NRF-Enabled Closed-loop Decomposition
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abstract
A two-layer control architecture is proposed to enable scalable implementations for constraint-based decision strategies, such as model predictive controllers. The bottom layer is based upon a distributed feedback-feedforward scheme that directs the controlled network's information flow according to a pre-specified communication infrastructure. Explicit expressions for the resulting closed-loop maps are obtained, and an offline model-matching procedure is proposed for designing the first layer. The obtained control laws are deployed via distributed state-space-based implementations, and the resulting closed-loop models enable predictive control design for the constraint management procedure described in our companion paper.
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2025 1verdicts
UNVERDICTED 1representative citing papers
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Network-Realised Model Predictive Control Part II: Distributed Constraint Management
Proposes a two-layer MPC architecture with set-based methods for distributed constraint management that delivers recursive feasibility and resembles classical MPC formulations.