Long-Horizon Direct Model Predictive Control for Power Converters With State Constraints

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Abstract
The article explores a new approach to model predictive control (MPC) where both discrete-level input constraints and state constraints are expressed in terms of Gaussian variables with unknown variances. The computations boil down to repeating Kalman-type recursions, with linear complexity in the prediction horizon. In consequence, the proposed approach can handle long prediction horizons with both discrete-level input constraints and state constraints, which has been a largely unresolved problem. The article demonstrates and evaluates the application of this approach by applying it to the control problem of a three-level power converter with an $LC$ filter. In this application, long horizons are mandatory to obtain low harmonic current distortions, and certain state constraints must be imposed to prevent damage to the converter. The proposed controller can easily handle 100 or more time steps and is shown to perform remarkably well, not only in the steady state, but also in transients and in the case of a phase-to-ground fault.

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