Peer-Reviewed Journal Details
Mandatory Fields
Mc Namara, P,Negenborn, RR,De Schutter, B,Lightbody, G
Engineering Applications of Artificial Intelligence
Weight optimisation for iterative distributed model predictive control applied to power networks
Optional Fields
Multi-agent Distributed model predictive control Particle swarm optimisation Smart grids Weight tuning Power networks NONLINEAR PROCESS SYSTEMS PID CONTROLLER ARCHITECTURES ALGORITHM DESIGN
This paper presents a weight tuning technique for iterative distributed Model Predictive Control (MPC). Particle Swarm Optimisation (PSO) is used to optimise both the weights associated with disturbance rejection and those associated with achieving consensus between control agents. Unlike centralised MPC, where tuning focuses solely on disturbance rejection performance, iterative distributed MPC practitioners must concern themselves with the trade off between disturbance rejection and the overall communication overhead when tuning weights. This is particularly the case in large scale systems, such as power networks, where typically there will be a large communication overhead associated with control. In this paper a method for simultaneously optimising both the closed loop performance and minimising the communications overhead of iterative distributed MPC systems is proposed. Simulation experiments illustrate the potential of the proposed approach in two different power system scenarios. (C) 2012 Elsevier Ltd. All rights reserved.
DOI 10.1016/j.engappai.2012.06.003
Grant Details