The field of control systems is moving towards the development of more sophisticated and robust control strategies for autonomous vehicles and mechanical systems. Recent research has focused on the design of model predictive control (MPC) strategies that can handle complex constraints and uncertainties, such as time-varying coupled constraints and nonlinear dynamics. Additionally, there is a growing interest in the use of geometric control methods, such as sliding mode control and geometric algebra, to design control systems that can effectively handle the complexities of mechanical systems with symmetries. Another area of research is the development of control barrier functions (CBFs) for constrained nonlinear systems, which can provide a powerful tool for ensuring safety and stability in autonomous systems. Noteworthy papers in this area include: High-Performance Trajectory Tracking MPC for Quadcopters with Coupled Time-Varying Constraints and Stability Proofs, which presents a cascade control structure for trajectory tracking in quadcopters. Geometric Control of Mechanical Systems with Symmetries Based on Sliding Modes, which proposes a framework for designing sliding mode controllers for mechanical systems with symmetry. Leveraging Equivariances and Symmetries in the Control Barrier Function Synthesis, which explores how equivariances in the dynamics and symmetries in the constraints can be leveraged in the CBF synthesis.