Optimal Sub-References for Setpoint Tracking: A Multi-level MPC Approach

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.

Publisher page

• Access full text  

Downloads

• PDF of corresponding technical report (904 KB)

BibTeX

@inproceedings{SunJam:23-022,
   author    = {Sun, Dingshan and Jamshidnejad, Anahita and De Schutter, Bart},
   title     = {Optimal Sub-References for Setpoint Tracking: {A} Multi-level
                {MPC} Approach},
   booktitle = {Proceedings of the 22nd IFAC World Congress},
   address   = {Yokohama, Japan},
   pages     = {9411--9416},
   month     = jul,
   year      = {2023}
   }

• Download BibTeX entry