The field of control systems is witnessing significant developments, particularly in Model Predictive Control (MPC) and robust control design. Researchers are exploring innovative approaches to improve the efficiency and performance of MPC, such as combining model-based and model-free gradient estimation methods, and using data-driven frameworks to accelerate MPC. Additionally, there is a growing interest in designing robust controllers that can handle uncertain systems and disturbances. Noteworthy papers in this area include: Policy Optimization for Unknown Systems using Differentiable Model Predictive Control, which introduces a novel policy optimization framework for MPC-based policies, and Robust Control Design Using a Hybrid-Gain Finite-Time Sliding-Mode Controller, which proposes a hybrid-gain finite-time sliding-mode control strategy for perturbed nonlinear systems. These advancements have the potential to significantly impact various applications, including robotics, mechanical systems, and process control.