Rethinking Cooperation in Distributed MPC: The Anticipation, the Reduction, and the Scaling of Consensus Constraints

• Applicant: Professor Dr.-Ing. Timm Faulwasser
• Subject Area: Automation, Mechatronics, Control Systems, Intelligent Technical Systems, Robotics
• Term: since 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 527447339

Model Predictive Control (MPC) has had substantial impact on industrial control practice in several industrial domains. However, when it comes to distributed cooperative settings, where large-scale networks comprised of dynamic subsystems are to be controlled in multi-agent fashion, its application still faces substantial challenges. Indeed, distributed predictive control requires the reconciliation of performance, communication, and coordination in different dimensions: problem formulation, system-theoretic analysis, and tailored decentralized optimization algorithms. In distributed MPC the cooperation among subsystems is achieved and fostered through consensus about the taken control actions, which is expressed through specific constraints. However, the convergence of the underlying numerical optimization is often slowed down by the need for consensus, i.e., the consensus constraints induce the need for the communication of optimization iterates between the controllers of the individual subsystems. On this canvas, this project rethinks cooperation in distributed MPC from the consensus point of view. That is, we explore new avenues to leverage the anticipation, the reduction, and the scaling of consensus constraints in the design, the system-theoretic analysis, and the algorithmic implementation of distributed cooperative predictive control. Specifically, we develop new methods for the scalable distributed anticipation of the dual variables of the consensus constraints and for the efficient use of these dual predictions in tailored numerical algorithms. Moreover, we investigate the effect of reducing the consensus constraint in time, i.e., instead of requiring consensus to hold over the entire prediction horizon we develop a new framework for closed-loop analysis with a consensus horizon shorter than the prediction horizon. Finally, we also research novel numerical methods for the scaling and the reduction of the consensus constraints, i.e., we create schemes for the distributed computation of pre-conditioners of the consensus constraints and for the dimensionality reduction of the consensus constraints in the dual space. Our findings are validated on case studies from mechatronics and energy systems.

• DFG Programme: Research Grants
• International Connection: China, Switzerland
• Co-Investigator: Dr.-Ing. Alexander Engelmann
• Cooperation Partners: Professor Dr. Boris Houska; Professor Colin Jones, Ph.D.