Adaptive Trajectory Control for Autonomous Helicopters

For autonomous helicopter ∞ight, it is common to separate the ∞ight control problem into an inner loop that controls attitude and an outer loop that controls the translational trajectory of the helicopter. In previous work, dynamic inversion and neural-network-based adaptation was used to increase performance of the attitude control system and the method of pseudocontrol hedging (PCH) was used to protect the adaptation pro- cess from actuator limits and dynamics. Adaptation to uncertainty in the attitude, as well as the translational dynamics, is introduced, thus minimiz- ing the efiects of model error in all six degrees of freedom and leading to more accurate position tracking. The PCH method is used in a novel way that enables adaptation to occur in the outer loop without interacting with the attitude dynamics. A pole-placement approach is used that alleviates timescale separation requirements, allowing the outer loop bandwidth to be closer to that of the inner loop, thus, increasing position tracking per- formance. A poor model of the attitude dynamics and a basic kinematics model is shown to be su-cient for accurate position tracking. The theory and implementation of such an approach, with a summary of ∞ight test results, are described.