Reference
P. Mc Namara,
R. R. Negenborn, B. De
Schutter, and G. Lightbody, "Weight optimisation for iterative distributed
model predictive control applied to power networks,"
Engineering Applications of Artificial Intelligence, vol. 26,
no. 1, pp. 532-543, Jan. 2013.
Abstract
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 the weights
associated with achieving consensus between control agents (while this paper
focuses on disturbance rejection, the same techniques could also be used for
set-point tracking based weight optimisation). 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. This paper examines the effects of weight optimisation
on both the disturbance rejection and the communication overhead. Two PSO
fitness functions are employed; the first function evaluates fitness based
solely on disturbance rejection ability, and the second is based on achieving a
trade off between good disturbance rejection ability and the maximum number of
distributed MPC iterations per control step. Simulation experiments illustrate
the potential of the proposed approach for weight tuning in two different power
system scenarios.
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BibTeX
@article{McNNeg:12-017,
author = {Mc Namara, Paul and Negenborn, Rudi R. and De Schutter, Bart and
Lightbody, Gordon},
title = {Weight Optimisation for Iterative Distributed Model Predictive
Control Applied to Power Networks},
journal = {Engineering Applications of Artificial Intelligence},
volume = {26},
number = {1},
pages = {532--543},
month = jan,
year = {2013}
}