Model Predictive Control in the Process Industry
Model Predictive Control in the Process Industry
Information Sciences—Informatics and Computer Science: An International Journal
Designing Communication Networks to Decompose Network Control Problems
INFORMS Journal on Computing
Multi-agent model predictive control for transportation networks: Serial versus parallel schemes
Engineering Applications of Artificial Intelligence
Decentralized receding horizon control for large scale dynamically decoupled systems
Automatica (Journal of IFAC)
Distributed Model Predictive Control: Synchronous and Asynchronous Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Distributed receding horizon control for multi-vehicle formation stabilization
Automatica (Journal of IFAC)
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A linear-dynamic network consists of a directed graph in which the nodes represent subsystems and the arcs model dynamic couplings. The local state of each subsystem evolves according to discrete linear dynamics that depend on the local state, local control signals, and control signals of upstream subsystems. Such networks appear in the model predictive control (MPC) of geographically distributed systems such as urban traffic networks and electric power grids. In this correspondence, we propose a decomposition of the quadratic MPC problem into a set of local subproblems that are solved iteratively by a network of agents. A distributed algorithm based on the method of feasible directions is developed for the agents to iterate toward a solution of the subproblems. The local iterations require relatively low effort to arrive at a solution but at the expense of high communication among neighboring agents and with a slower convergence rate.