Learning and Outperforming the Model Predictive Control with a Linear ϵ-Support Vector Machine for Power Converter

This article presents a linear ϵ error insensitive support vector machine (ϵ-SVM) learned from the model predictive control (MPC) with very limited training samples. Unlike the existing learning-based MPC, which takes the same inputs as the MPC, the ϵ-SVM learns from historical data as well. Such structure is found to have both feedforward and feedback elements that improve performance. Besides, using the ϵ error-insensitive tube also increases the noise cancellation capability. The experiment is carried out on a three-phase inverter with an L-C filter and shows that the linear ϵ-SVM–MPC not only has better performance than the artificial neural networks (ANNs) MPC, but also outperforms the online MPC in both low THD values and small tracking errors regardless the load conditions. Moreover, the use of the linear kernel significantly reduces the time complexity to O (1), which is much less than O (2 ⁿ ) in either online MPC or ANN–MPC. Therefore, the linear ϵ-SVM–MPC has provided an effective way to implement the MPC and it is preferable for the DSP. This article is accompanied by a video demonstrating the real-time operation.