DeepControl: EnergyEfficient Control of a Quadrotor using a Deep Neural Network Synthesis of a feedback controller for nonlinear dynamical systems like a quadrotor requires to deal with the tradeoff 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 feedforward 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 modelpredictive 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.

Flying through a narrow gap using neural network: an endtoend planning and control approach In this paper, we investigate the problem of enabling a drone to fly through a tilted narrow gap, without a traditional planning and control pipeline. To this end, we propose an endtoend policy network, which imitates from the traditional pipeline and is finetuned using reinforcement learning. Unlike previous works which plan dynamical feasible trajectories using motion primitives and track the generated trajectory by a geometric controller, our proposed method is an endtoend approach which takes the flight scenario as input and directly outputs thrustattitude control commands for the quadrotor. Key contributions of our paper are: 1) presenting an imitatereinforce training framework. 2) flying through a narrow gap using an endtoend policy network, showing that learning based method can also address the highly dynamic control problem as the traditional pipeline does (see attached video1). 3) propose a robust imitation of an optimal trajectory generator using multilayer perceptrons. 4) show how reinforcement learning can improve the performance of imitation learning, and the potential to achieve higher performance over the modelbased method.