Network-Realised Model Predictive Control Part II: Distributed Constraint Management
This work addresses scalability issues in model predictive control for networked systems, though it appears incremental as it builds on existing methods with set-based approaches.
The paper tackles the challenge of scalable model predictive control by proposing a two-layer architecture that enforces local constraints to achieve global theoretical guarantees, including recursive feasibility, while maintaining similarity to classical formulations for flexible implementation.
A two-layer control architecture is proposed, which promotes scalable implementations for model predictive controllers. The top layer acts as both a reference governor for the bottom layer and as a feedback controller for the regulated network. By employing set-based methods, global theoretical guarantees are obtained by enforcing local constraints upon the network's variables and upon those of the first layer's implementation. The proposed technique offers recursive feasibility guarantees as one of its central features, and the expressions of the resulting predictive strategies bear a striking resemblance to classical formulations from model predictive control literature, allowing for flexible and easily customisable implementations.