Attitude Control

Attitude control, the process of orienting and stabilizing objects in three-dimensional space, is a critical area of research across robotics and aerospace engineering. Current efforts focus on developing robust and efficient controllers for diverse platforms, including quadrotors, modular satellites, and underwater robots, often employing model predictive control (MPC) variants, sliding mode control (SMC), and deep reinforcement learning (DRL) algorithms. These advancements are driven by the need for improved performance in challenging environments and applications such as agile maneuvers, precise manipulation, and autonomous operation in unstructured settings. The resulting improvements in control algorithms have significant implications for the development of more capable and reliable autonomous systems across various domains.