The field of autonomous systems control is moving towards more efficient and robust control methods. Recent developments have focused on improving the accuracy and speed of control systems, particularly in resource-constrained environments. Nonlinear Model Predictive Control (NMPC) has emerged as a powerful approach for controlling highly dynamic systems, and researchers have been working on optimizing its computational efficiency for real-time deployment. Additionally, there is a growing interest in developing control systems that can adapt to unstructured environments, such as those found in agricultural applications. Noteworthy papers in this area include: Efficient Self-Supervised Neuro-Analytic Visual Servoing for Real-time Quadrotor Control, which introduces a self-supervised neuro-analytical model for visual-based quadrotor control. NMPCM: Nonlinear Model Predictive Control on Resource-Constrained Microcontrollers, which presents an efficient solution for generating and deploying NMPC on microcontrollers. A Unified Finite-Time Sliding Mode Quaternion-based Tracking Control for Quadrotor UAVs without Time Scale Separation, which proposes a novel design for finite-time position control of quadrotor UAVs. Multi-Waypoint Path Planning and Motion Control for Non-holonomic Mobile Robots in Agricultural Applications, which presents an integrated navigation framework for autonomous mobile robots in agricultural environments.