Observability, controllability and decentralized control of interconnected power systems
Computers and Electrical Engineering
Predictive control design for large-scale systems
Automatica (Journal of IFAC)
Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence
Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence
Parallel and Distributed Computation: Numerical Methods
Parallel and Distributed Computation: Numerical Methods
Model Predictive Control in the Process Industry
Model Predictive Control in the Process Industry
2001 IEEE International Conference on Acoustics, Speech, and Signal Processing
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 200. on IEEE International Conference - Volume 02
Optimal coordination of variable speed limits to suppress shock waves
IEEE Transactions on Intelligent Transportation Systems
Survey Constrained model predictive control: Stability and optimality
Automatica (Journal of IFAC)
Distributed receding horizon control for multi-vehicle formation stabilization
Automatica (Journal of IFAC)
Distributed optimization for model predictive control of linear-dynamic networks
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
An MPC approach to the design of two-layer hierarchical control systems
Automatica (Journal of IFAC)
Weight optimisation for iterative distributed model predictive control applied to power networks
Engineering Applications of Artificial Intelligence
Accelerated gradient methods and dual decomposition in distributed model predictive control
Automatica (Journal of IFAC)
Cooperative distributed MPC for tracking
Automatica (Journal of IFAC)
Engineering Applications of Artificial Intelligence
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We consider the control of large-scale transportation networks, like road traffic networks, power distribution networks, water distribution networks, etc. Control of these networks is often not possible from a single point by a single intelligent control agent; instead control has to be performed using multiple intelligent agents. We consider multi-agent control schemes in which each agent employs a model-based predictive control approach. Coordination between the agents is used to improve decision making. This coordination can be in the form of parallel or serial schemes. We propose a novel serial coordination scheme based on Lagrange theory and compare this with an existing parallel scheme. Experiments by means of simulations on a particular type of transportation network, viz., an electric power network, illustrate the performance of both schemes. It is shown that the serial scheme has preferable properties compared to the parallel scheme in terms of the convergence speed and the quality of the solution.