Deep Iterative Learning Control for Quadrotor's Trajectory Tracking

Iterative learning control (ILC) is an effective control technique to enhance system performance. ILC requires the system to execute identical operations repetitively such that the system performance can be improved by learning from previous iterations. To remove the requirement of repetitive operation, this paper leverages recent advances in deep learning and proposes a new ILC scheme, named Deep ILC, with detailed formulation, analysis, and validation. The proposed Deep ILC consists of two main components, the long short-term memory (LSTM) neural network based prediction and the optimization based learning filter design. In particular, we formulate the learning filter design problem into an optimization problem by purposely constructing an augmented dynamic feedback system, of which the to-be-designed learning filter is in the feedback loop. The proposed Deep ILC is applied to the trajectory tracking of quadrotor unmanned aerial vehicles (UAVs) and its effectiveness is validated through experimental studies.