Adaptive control and automated design for aerial robotic challenges

Aerial robots have become ubiquitous but, like most robots, still struggle to operate at high speed in unstructured, cramped environments. Mark Mueller presents two ideas for creating more capable aerial systems. The first designs the control and mechanical properties of a system simultaneously, applied to carrying a load with multiple aerial robots: rather than fixing open-loop characteristics such as thrust-to-weight ratio first and designing the feedback controller afterwards, it uses insights from robust control to design both together, based on models of the system dynamics and external disturbances. The second is a learning approach for robust and adaptable quadcopter control strategies, demonstrated in experiments to search over very large parametric uncertainty (inertia spanning two orders of magnitude) and to respond almost instantaneously to parameter changes such as offsets in centre of mass or reduced motor effectiveness.