Knowledge transfer for high-performance quadrocopter maneuvers

Iterative Learning Control algorithms are based on the premise that “practice makes perfect”. By iteratively performing an action, repetitive errors can be learned and accounted for in subsequent iterations, in a non-causal and feedforward manner. This method has been previously implemented for a quadrocopter system, enabling the quadrocopter to learn to accurately track high-performance slalom trajectories. However, one major limitation of this system is that knowledge from previously learned trajectories is not generalized or transferred to new trajectories; these must be learned from a state of zero experience. This paper experimentally shows that the major dynamics of the Iterative Learning Control process can be captured by a linear map, trained on previously learned slalom trajectories. This map enables this prior knowledge to be used to improve the initialization of an unseen trajectory. Experimental results show that prediction based on a single prior is enough to reduce the initial tracking error for an unseen trajectory by an order of magnitude.

Authors

• Michael Hamer
• Markus Waibel
• Raffaello D'Andrea

Keywords

• Trajectory
• Training
• Prediction algorithms
• Vectors
• Knowledge transfer
• Accuracy
• Heuristic algorithms
• Linear Function
• Tracking Error
• Subsequent Iterations
• Learning Trajectories
• Practice Team
• Iterative Learning Control
• Root Mean Square Error
• Training Set
• Learning Process
• System Dynamics
• Feedback Control
• Future Conditions
• Transfer Learning
• Parameter Vector
• Least-squares Fitting
• Single Trajectory
• Multiple Trajectories
• Knowledge Of Problems
• End Of The Trajectory
• Input Trajectory
• Nominal Trajectory
• Output Trajectory