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| Title: | Model predictive control with memory-based discrete search for switched linear systems | | | Authors: | R.B. Larsen, B. Atasoy, R.R. Negenborn |
| | Conference: | 21st IFAC World Congress (IFAC2020) | | Address: | Berlin, Germany | | Date: | July 2020 |
| | Abstract: | Controlling systems with both continuous and discrete actuators using model
predictive control is often impractical, since mixed integer optimization problems are too complex to solve sufficiently fast. This paper proposes a parallelizable method to control both the continuous input and the discrete switching signal for linear switched systems. The method uses ideas from Bayesian optimization to limit the computation to a predefined number of convex optimization problems. The recursive feasibility and stability of the method is guaranteed for initially feasible solutions. Results from simulated experiments show promising performances and computation times. |
| | Reference: | Model predictive control with memory-based discrete search for switched linear systems. R.B. Larsen, B. Atasoy, R.R. Negenborn. In Proceedings of the 21st IFAC World Congress (IFAC2020), Berlin, Germany, pp. 6851-6856, July 2020. | | | Request: | A
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