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.