Model predictive control with memory-based discrete search for switched linear systems
Rie B. Larsen (TU Delft - Transport Engineering and Logistics)
B. Atasoy (TU Delft - Transport Engineering and Logistics)
R.R. Negenborn (TU Delft - Transport Engineering and Logistics)
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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.
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