1 research outputs found
ADMM for Exploiting Structure in MPC Problems
We consider a model predictive control (MPC) setting, where we use the
alternating direction method of multipliers (ADMM) to exploit problem
structure. We take advantage of interacting components in the controlled system
by decomposing its dynamics with virtual subsystems and virtual inputs. We
introduce subsystem-individual penalty parameters together with optimal
selection techniques. Further, we propose a novel measure of system structure,
which we call separation tendency. For a sufficiently structured system, the
resulting structure-exploiting method has the following characteristics: (i)
its computational complexity scales favorably with the problem size; (ii) it is
highly parallelizable; (iii) it is highly adaptable to the problem at hand; and
(iv), even for a single-thread implementation, it improves the overall
performance. We show a simulation study for cascade systems and compare the new
method to conventional ADMM.Comment: Extended proofs, simulation details, and problem data is provided in
ancillary file