Intelligent Controller Selection for Aggressive Quadrotor Manoeuvring: A reinforcement learning approach

A novel intelligent controller selection method for quadrotor attitude and altitude control is presented that maintains performance in different regimes of the flight envelope. Conventional quadrotor controllers can behave insufficiently during aggressive manoeuvring, in extreme angles the quadrotor is unable to maintain height which may result...

Guaranteed globally optimal continuous reinforcement learning

Self-learning controllers offer various strong benefits over conventional controllers, the most important one being their ability to adapt to unexpected circumstances. Their application is however limited, the most important reason being that, for self-learning controllers to work on continuous domains, nonlinear function approximators are...