Improved Model Predictive Control by Robust Prediction and Stability-Constrained Finite States for Three-Phase Inverters With an Output <inline-formula> <tex-math notation="LaTeX">$LC$ </tex-math></inline-formula> Filter

This paper proposes an improved model predictive control (MPC) scheme with a robust prediction and stability-constrained finite states for three-phase voltage source inverters (3&#x03A6;-VSIs) with an LC filter. In this paper, the stability-constrained finite states are selected via the asymptotic stability conditions as a key factor to reduce the total harmonic distortions (THDs) and steady-state errors. Meanwhile, the Kalman filter-based observers improve the overall robustness against model mismatches and noises via a robust prediction. To select the stabilized finite states, the stability conditions are derived by the equivalent feedback-gains and constrained in the exhausting search of the proposed finite-set (FS) MPC. Unlike conventional FS-MPC methods, three control targets (i.e., robustness, stability, and optimality) are simultaneously achieved as the new contributions to remarkably enhance the voltage control performance of the 3&#x03A6;-VSI, which are also the challenge of the conventional control methods. To verify the superiority of the proposed FS-MPC, comparative studies are conducted on a prototype three-wire 3&#x03A6;-VSI system with a TI TMS320F28335 DSP under practical conditions (i.e., parameter mismatches, linear/nonlinear-load step-changes). The experimental results confirm that the performance of the proposed FS-MPC has been significantly improved in terms of lower THDs, smaller steady-state errors, faster dynamic response, and more robustness under critical system changes as compared with the conventional FS-MPC.

Abstract licence: CC BY-NC-ND

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