
Title:  Model predictive control with memorybased 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:  R.B. Larsen, B. Atasoy, R.R. Negenborn. Model predictive control with memorybased discrete search for switched linear systems. In Proceedings of the 21st IFAC World Congress (IFAC2020), Berlin, Germany, pp. 68516856, July 2020.   Request:  A
copy of this publication. 

