Adaptive Control of Unmanned Aerial Vehicles - Theory and Flight Tests

Typically, unmanned aerial vehicles are underactuated systems i.e., they have fewer independent control inputs than degrees of freedom. In a helicopter for example, the body-axis roll, pitch, yaw and altitude axis are fully actuated. However, lateral and longitudinal translational motion is only possible by tilting the thrust vector. This chapter develops a 6 degree-of-freedom flight control algorithm that can track both position and attitude trajectories. Approximate inverse models for vehicle attitude and position dynamics are used for feedback linearization leading to an inner-loop that tracks attitude and angular rate and an outer-loop that tracks position and velocity commands. A single adaptive element is used to compensate for inversion errors (uncertainty) in both loops. A key challenge in realizing an adaptive control design on real aircraft is dealing with actuator magnitude and rate saturation. Such saturation elements cannot be easily captured in inverse models and leads to incorrect learning in the adaptive element during periods of saturation. A mechanism to exactly remove such incorrect learning is provided. Additionally, nonlinear reference models are introduced to mitigate the risks of the closed loop system entering regions of the flight envelope