High-Speed Finite Control Set Model Predictive Control for Power Electronics

TL;DR

This paper introduces a fast, FPGA-implemented finite control set MPC method for power electronics that significantly reduces computational complexity, enabling real-time control with very short sampling times and improved performance.

Contribution

It presents a novel approximate dynamic programming approach that estimates an infinite horizon value function offline, reducing the MPC horizon and computational load for power electronic control.

Findings

Achieves sampling times below 25 μs on FPGA

Requires only 5.76 μs for horizon N=1 and 17.27 μs for N=2

Outperforms state-of-the-art methods with longer horizons

Abstract

Common approaches for direct model predictive control (MPC) for current reference tracking in power electronics suffer from the high computational complexity encountered when solving integer optimal control problems over long prediction horizons. We propose an efficient alternative method based on approximate dynamic programming, greatly reducing the computational burden and enabling sampling times below 25 $\mu$s. Our approach is based on the offline estimation of an infinite horizon value function which is then utilized as the tail cost of an MPC problem. This allows us to reduce the controller horizon to a very small number of stages while simultaneously improving the overall controller performance. Our proposed algorithm was implemented on a small size FPGA and validated on a variable speed drive system with a three-level voltage source converter. Time measurements showed that our algorithm requires only 5.76 $\mu$s for horizon N = 1 and 17.27 $\mu$s for N = 2, in both cases outperforming state of the art approaches with much longer horizons in terms of currents distortion and switching frequency. To the authors' knowledge, this is the first time direct MPC for current control has been implemented on an FPGA solving the integer optimization problem in real-time and achieving comparable performance to formulations with long prediction horizons.