| Home > Approximate Model Predictive Control of Switched Affine Systems Using Multitask Learning with Safety and Stability Guarantees |
| Article | |
| Title | Approximate Model Predictive Control of Switched Affine Systems Using Multitask Learning with Safety and Stability Guarantees |
| Author(s) | Ghawash, Faiq (Norwegian U. Sci. Tech.) ; Hovd, Morten (Norwegian U. Sci. Tech.) ; Schofield, Brad (CERN) |
| Publication | 2023 |
| Number of pages | 8 |
| In: | 62nd IEEE Conference on Decision and Control (CDC 2023), Singapore, Singapore, 13 - 15 Dec 2023, pp.4903-4910 |
| DOI | 10.1109/CDC49753.2023.10383894 |
| Abstract | We study the problem of designing an approximate model predictive control (MPC) for discrete time switched affine systems. The MPC design for the switched affine system requires an online solution of a mixed integer program. However, the combinatorial nature of the mixed integer problems might require a large computational time limiting its applicability in real time scenarios. To this end, we propose a framework based on the multitask learning paradigm to approximate the solution of mixed integer MPC for switched affine systems. We also provide a computational method to overapproximate the reachable sets of the closed-loop system that helps to analyze the safety and stability of the system under the influence of the learned controller. Once trained offline, the resulting controller results in a solver free approach especially suited for implementation on resource constrained embedded hardware. We demonstrate the efficacy of the approach on a real world example of an induced draft cooling tower. |
| Copyright/License | © 2023-2025 IEEE |