Sensitivity-based multistep MPC for embedded systems

In model predictive control (MPC), ash optimization problem is solved every sampling instant to determine an optimal control for a physical system. We aim to accelerate this procedure for fast systems applications mid address the challenge of implementing the resulting; MPC scheme on all embedded system with limited computing power. We present the sensitivity based multistep MPC, a Strategy which considerably reduces the computing requirements in terms of floating point operations (FLOPS), compared to a standard MPC formulation, while fulfilling closed loop performance expectations. We illustrate by applying the method to a DC-DC converter model and show how a designer can optimally trade off closed-loop performance considerations with computing requirements in order to fit the controller into a resource-constrained embedded system. (C) 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.