Multi-core parallelisation of integer optimisation model predictive control for power electronic applications

The deployment of integer optimisation based model predictive control (MPC) for the control of power electronic applications has been limited by the computational burden of the scenario tree exploration and the required fast cycle times. The recent technology trend towards multi-core control platforms offers new possibilities for enabling MPC for power converters. We propose a static scheduling method to efficiently parallelise the tree exploration for converter control using the example of Generalised Model Predictive Direct Torque Control (GMPDTC). We evaluate several scheduling strategies (both suboptimal and optimal) for distributing the work packages over the individual cores with respect to their effectiveness using an eight core platform from Freescale. Moreover, the proposed approach has the advantage that the code has a small memory footprint and every improvement of the sequential code will directly result in an improvement of the parallel version.