Constraint-adaptive MPC for large-scale systems: Satisfying state constraints without imposing them
| Autor: | Nouwens, S. A. N., de Jager, B., Paulides, M. M., Heemels, W. P. M. H. |
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| Rok vydání: | 2024 |
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| Druh dokumentu: | Working Paper |
| DOI: | 10.1016/j.ifacol.2021.08.550 |
| Popis: | Model Predictive Control (MPC) is a successful control methodology, which is applied to increasingly complex systems. However, real-time feasibility of MPC can be challenging for complex systems, certainly when an (extremely) large number of constraints have to be adhered to. For such scenarios with a large number of state constraints, this paper proposes two novel MPC schemes for general nonlinear systems, which we call constraint-adaptive MPC. These novel schemes dynamically select at each time step a (varying) set of constraints that are included in the on-line optimization problem. Carefully selecting the included constraints can significantly reduce, as we will demonstrate, the computational complexity with often only a slight impact on the closed-loop performance. Although not all (state) constraints are imposed in the on-line optimization, the schemes still guarantee recursive feasibility and constraint satisfaction. A numerical case study illustrates the proposed MPC schemes and demonstrates the achieved computation time improvements exceeding two orders of magnitude without loss of performance. Comment: 6 pages, 4 figures, IFAC NMPC 2021 conference |
| Databáze: | arXiv |
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