DeepControl: Energy-Efficient Control of a Quadrotor using a Deep Neural Network

Abstract

Synthesis of a feedback controller for nonlinear dynamical systems like a quadrotor requires to deal with the trade-off between performance and online computation requirement of the controller. Model predictive controllers (MPC) provide excellent control performance, but at the cost of high online computation. In this paper, we present our experience in approximating the behavior of an MPC for a quadrotor with a feed-forward neural network. To facilitate the collection of training data, we create a faithful model of the quadrotor and use Gazebo simulator to collect sufficient training data. The deep neural network (DNN) controller learned from the training data has been tested on various trajectories to compare its performance with a model-predictive controller. Our experimental results show that our DNN controller can provide almost similar trajectory tracking performance at a lower control computation cost, which helps in increasing the flight time of the quadrotor. Moreover, the hardware requirement for our DNN controller is significantly less than that for the MPC controller. Thus, the use of DNN based controller also helps in reducing the overall price of a quadrotor.