This study investigates nonlinear reliable output tracking control issues. Both passive and active reliable control laws are proposed using Variable Structure Control technique. These reliable laws need not the solution of Hamilton-Jacobi (HJ) equation or inequality, which are essential for optimal approaches such as LQR and H reliable designs. As a matter of fact, this approach is able to relax the computational burden for solving the HJ equation. The proposed reliable designs are also applied to a bank-to-turn missile system to illustrate their benefits.

During the COVID-19 pandemic, a robust system for masked face recognition has been required. Most existing solutions used many samples per identity for the model to recognize, but the processes involved are very laborious in a real-life scenario. Therefore, we propose “CPNet” as a suitable and reliable way of recognizing masked faces from only a few samples per identity. The prototype classifier uses a few-shot learning paradigm to perform the recognition process. To handle complex and occluded facial features, we incorporated the covariance structure of the classes to refine the class distance calculation. We also used sharpness-aware minimization (SAM) to improve the classifier. Extensive in-depth experiments on a variety of datasets show that our method achieves remarkable results with accuracy as high as 95.3%, which is 3.4% higher than that of the baseline prototype network used for comparison.