In order to achieve the best possible performance
of a model predictive controller (MPC) for a given set of
resources, the software algorithm and computational platform
have to be designed simultaneously. Moreover, in practical
applications the controller design problem has a multi-objective
nature: performance is traded off against computational hardware
resource usage, namely time, energy and space. This
paper proposes formulating an MPC design problem as a multiobjective
optimization (MOO) problem in order to explore the
design trade-offs in a systematic way.
Since the design objectives in the resulting MOO problem are
expensive to evaluate, i.e. evaluation requires time consuming
simulations, most of the classical and evolutionary MOO
algorithms cannot be employed for this class of design problems.
For this reason a practical MOO algorithm that can deal with
expensive-to-evaluate functions is presented. The algorithm is
based on Kriging and the hypervolume criterion that was
recently proposed in the expensive optimization literature. A
numerical example for a fast gradient-based controller design
shows that the proposed approach can efficiently explore
optimal performance-resource trade-offs.