Typically, unmanned aerial vehicles are underactuated systems, that is, they have fewer independent control inputs than degrees of freedom. In a helicopter, for example, the body axes roll, pitch, yaw, and altitude are fully actuated. However, lateral and longitudinal translational motion is only possible by tilting the thrust vector. This chapter develops a six 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 that result in loss-of-controllability. The resulting adaptive controller accepts trajectory commands comprising of desired position, velocity, attitude, and angular velocity and produces normalized actuator signals required for flight control. A modification to the baseline adaptive control system is also provided that enables long-term retention of the uncertainty approximation within the adaptive element. This architecture is validated through flight tests on several fixed wing and rotorcraft UAVs, including a 145-lb helicopter UAV (Yamaha RMAX or GTMax), a scale model fixed-wing aircraft (GTEdge), and a small ducted fan (GTSpy).
All Science Journal Classification (ASJC) codes
- Computer Science(all)