Locality-Preserving Graph Laplacian Manifold Learning Based Model Predictive Control for Three-Phase Inverters

This paper presents a model predictive control (MPC) strategy for three-phase inverters based on locality preserving projections (LPP). Unlike conventional machine learning (ML)–based MPC approaches that rely on predefined or high-dimensional input features, the proposed LPP-MPC automatically extracts compact, informative representations by preserving the data's intrinsic geometric structure. This dimensionality reduction enables fast linear control-law evaluation with computational complexityO(1), making the controller well-suited for real-time implementation. Experimental results demonstrate that the LPP-MPC achieves lower total harmonic distortion (THD) and reduced tracking error compared to quadratic-programming (QP) MPC under both linear and nonlinear load conditions, and the proposed controller maintains consistently lower THD throughout load transients than other methods such as two-degree-of-freedom (2DOF) MPC. Compared to existing model-free (MF) MPC and deep learning neural network (DLNN), the LPP-MPC has the lowest THD and root mean square error (RMSE) with the least computational time owing to its efficient linear structure and strong generalization capability.