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.


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Bibtex entry:

@inproceedings{SunJam:23-022,
        author={D. Sun and A. Jamshidnejad and B. {D}e Schutter},
        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},
        doi={10.1016/j.ifacol.2023.10.233}
        }


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