|Inverse Reinforcement Learning Control for Trajectory Tracking of a Multirotor UAV
Seungwon Choi, Suseong Kim, and H. Jin Kim*
International Journal of Control, Automation, and Systems, vol. 15, no. 4, pp.1826-1834, 2017
Abstract : "The main purpose of this paper is to learn the control performance of an expert by imitating the demonstrations
of a multirotor UAV (unmanned aerial vehicle) operated by an expert pilot. First, we collect a set of several
demonstrations by an expert for a certain task which we want to learn. We extract a representative trajectory from
the dataset. Here, the representative trajectory includes a sequence of state and input. The trajectory is obtained
using hidden Markov model (HMM) and dynamic time warping (DTW). In the next step, the multirotor learns to
track the trajectory for imitation. Although we have data of feed-forward input for each time sequence, using this
input directly can deteriorate the stability of the multirotor due to insufficient data for generalization and numerical
issues. For that reason, a controller is needed which generates the input command for the suitable flight maneuver.
To design such a controller, we learn the hidden reward function of a quadratic form from the demonstrated
flights using inverse reinforcement learning. After we find the optimal reward function that minimizes the trajectory
tracking error, we design a reinforcement learning based controller using this reward function. The simulation and
experiment applied to a multirotor UAV show successful imitation results."
Inverse reinforcement learning, learning from demonstration, multirotor control, particle swarm optimization.