Insulated gate bipolar transistors (IGBTs) are widely used in power electronic devices, and their health prediction problems have attracted much attention in the field of power electronic equipment health management. The performance degradation of IGBT gate oxide is one of the most important failure modes. In order to analyze this failure mechanism and the ease of implementation of a monitoring circuit, the gate leakage current of IGBTs was selected as the fault precursor parameter for the degradation of their gate oxide performance, and feature selection and fusion were carried out by using time domain characteristic analysis, grayscale correlation, Mahalanobis distance, Kalman filter, and other methods. Thus, a health indicator was obtained to characterize the degradation of IGBT performance, which was used to indicate the degree of aging of the IGBT gate oxide layer. In this paper, we propose an improved degradation prediction model called MultiScaleFormer, inspired by advanced design ideas of the iTransformer network architecture, combined with the health parameters of IGBTs to construct a degradation prediction model for the IGBT gate oxide layer. MultiScaleFormer showed the highest fitting accuracy compared with the Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Support Vector Regression (SVR), Gaussian Process Regression (GPR), CNN-LSTM, and Transformer models in our experiment. The mean absolute error (MAE) of the MultiScaleFormer prediction was as low as 0.0087. Extraction of the health indicator and the construction and verification of the degradation prediction model were carried out on the dataset released by the NASA-Ames Laboratory. These results demonstrate the feasibility of the gate leakage current as a fault precursor parameter for IGBT gate oxide failure, and the feasibility and accuracy of the MultiScaleFormer prediction model for IGBT performance degradation.

Inverse Optimal Control (IOC) aims to infer the underlying cost functional of an agent from observations of its expert behavior. This paper focuses on the IOC problem within the continuous-time linear quadratic regulator framework, specifically addressing the challenging scenario where both the system dynamics and the cost functional weighting matrices are unknown. A significant limitation of existing methods for this joint estimation problem is the lack of rigorous theoretical guarantees on the convergence and convergence rate of their optimization algorithms, which restricts their application in safety-critical systems. To bridge this theoretical gap, we propose an analytical framework for IOC that provides such guarantees. The core contribution lies in the equivalent reformulation of this non-convex problem of jointly estimating system and cost parameters into a convex second-order cone programming problem. Building on this transformation, we design an efficient iterative solver based on the block successive upper-bound minimization algorithm. We rigorously prove that the proposed algorithm achieves a sublinear convergence rate of $\mathcal{O}(1/k)$. To the best of our knowledge, this is the first solution for the model-free IOC problem that comes with an explicit convergence rate guarantee. Finally, comparative simulation experiments against a state-of-the-art benchmark algorithm validate the superiority of our proposed method. The results demonstrate that our algorithm achieves an order-of-magnitude improvement in convergence speed while also exhibiting significant advantages in reconstruction accuracy and robustness.