Control Allocation

Control allocation addresses the problem of distributing desired control commands among redundant actuators in systems like robots and aircraft, optimizing performance and ensuring feasibility within hardware limitations. Current research focuses on developing efficient algorithms, including model predictive control (MPC), and leveraging machine learning techniques, such as artificial neural networks, to improve computational speed and handle nonlinear systems. These advancements are crucial for enhancing the agility, robustness, and safety of complex robotic and aerospace systems, particularly in demanding applications like aerial manipulation and high-performance flight.