Network-Realised Model Predictive Control Part I: NRF-Enabled Closed-loop Decomposition
This work addresses scalability issues in control systems for applications requiring constraint management, though it appears incremental as it builds on existing model predictive control frameworks.
The paper tackles the challenge of scalable implementation for constraint-based decision strategies like model predictive control by proposing a two-layer control architecture with a distributed feedback-feedforward scheme, resulting in explicit closed-loop maps and enabling predictive control design for constraint management.
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.