Nonlinear High-Speed Model Predictive Control with Long Prediction Horizons for Power-Converter Systems

Beschreibung

The utilization of AC/DC- and DC/DC-power converters and their coupling is emerging in the field of electrified powertrains in the automotive industry or in the renewable energy sector via photovoltaic (PV) cells or wind turbines and in battery systems for energy storage. For all these tasks, highly dynamic, efficient and accurately controlled power converters are needed. This work presents nonlinear high speed model predictive control (MPC) strategies for (interleaved switching) AC/DC- and DC/DC-power converter and coupled power converter systems. Compared to known MPC strategies the proposed approaches allow for longer prediction horizons while keeping the calculation times in the range of a few μs. This is achieved by tailoring the MPC strategies to an efficient implementation on an FPGA, exploiting the inherent parallel computation capabilities of FPGAs. Moreover, the utilization of a numerically efficient envelope model in the MPC is an important building block to reach the desired fast calculation times. The MPC strategies systematically take into account input and state constraints, provide a high dynamic and accurate control performance, exhibit constant switching frequencies and enable an interleaved switching operation. The proposed MPC strategies are tested in detailed simulations and in hardware-in-the-loop (HIL) experiments. An accurate and fast closed-loop dynamics can be achieved, also with respect to unknown fast load variations and parameter uncertainties. It is finally shown that the proposed MPC strategies have the potential to outperform the control performance of existing state-of-the-art control strategies for power converter systems.

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Thomas Hausberger

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AC/DC power converter DC/DC power converter FPGA implementation cooperative control strategy coupled power converter systems fast parallel algorithms hardware-in-the-loop tests model predictive control

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