Reference:
D. Sun,
A. Jamshidnejad, and
B. De Schutter,
"Optimal sub-references for setpoint tracking: A multi-level MPC
approach," Proceedings of the 22nd IFAC World Congress,
Yokohama, Japan, pp. 9411-9416, July 2023.
Abstract:
We propose a novel method to improve the convergence performance of
model predictive control (MPC) for setpoint tracking, by introducing
sub-references within a multi-level MPC structure. In some cases, MPC
is implemented with a short prediction horizon due to limited on-line
computation capacity, which could lead to deteriorated dynamic
performance. The introduced multi-level optimization method can
generate proper sub-references for the MPC setpoint tracking problem,
and efficiently improve the dynamic performance. In the higher level a
specific performance criterion is taken as the objective, while
explicit MPC is utilized in the lower level to represent the control
input. The generated sub-references are then used in MPC for the real
system with prediction horizon restrictions. Setpoint-tracking MPC for
linear systems is used to illustrate the approach throughout the
paper. Numerical simulations show that MPC with sub-references
significantly improves the convergence performance compared with
regular MPC with the same prediction horizon. Thus, it can be
concluded that MPC with sub-references has a high potential to tackle
more complicated control problems with limited computation capacity.
Bibtex entry:
@inproceedings{SunJam:23-022,