Accurate Tracking of Aggressive Quadrotor Trajectories using Incremental Nonlinear Dynamic Inversion and Differential Flatness

Autonomous unmanned aerial vehicles (UAVs) that can execute aggressive (i.e., high-speed and high-acceleration) maneuvers have attracted significant attention in the past few years. This paper focuses on accurate tracking of aggressive quadcopter trajectories. We propose a novel control law for tracking of position and yaw angle and their derivatives of up to fourth order, specifically, velocity, acceleration, jerk, and snap along with yaw rate and yaw acceleration. Jerk and snap are tracked using feedforward inputs for angular rate and angular acceleration based on the differential flatness of the quadcopter dynamics. Snap tracking requires direct control of body torque, which we achieve using closed-loop motor speed control based on measurements from optical encoders attached to the motors. The controller utilizes incremental nonlinear dynamic inversion (INDI) for robust tracking of linear and angular accelerations despite external disturbances, such as aerodynamic drag forces. Hence, prior modeling of aerodynamic effects is not required. We rigorously analyze the proposed control law through response analysis, and we demonstrate it in experiments. The controller enables a quadcopter UAV to track complex 3D trajectories, reaching speeds up to 12.9 m/s and accelerations up to 2.1g, while keeping the root-mean-square tracking error down to 6.6 cm, in a flight volume that is roughly 18 m by 7 m and 3 m tall. We also demonstrate the robustness of the controller by attaching a drag plate to the UAV in flight tests and by pulling on the UAV with a rope during hover.Comment: To be published in IEEE Transactions on Control Systems Technology. Revision: new set of experiments at increased speed (up to 12.9 m/s), updated controller design using quaternion representation, new video available at https://youtu.be/K15lNBAKDC

Nonlinear MPC for Quadrotor Fault-Tolerant Control

The mechanical simplicity, hover capabilities, and high agility of quadrotors lead to a fast adaption in the industry for inspection, exploration, and urban aerial mobility. On the other hand, the unstable and underactuated dynamics of quadrotors render them highly susceptible to system faults, especially rotor failures. In this work, we propose a fault-tolerant controller using nonlinear model predictive control (NMPC) to stabilize and control a quadrotor subjected to the complete failure of a single rotor. Differently from existing works, which either rely on linear assumptions or resort to cascaded structures neglecting input constraints in the outer-loop, our method leverages full nonlinear dynamics of the damaged quadrotor and considers the thrust constraint of each rotor. Hence, this method could effectively perform upset recovery from extreme initial conditions. Extensive simulations and real-world experiments are conducted for validation, which demonstrates that the proposed NMPC method can effectively recover the damaged quadrotor even if the failure occurs during aggressive maneuvers, such as flipping and tracking agile trajectories.Comment: 9 pages, 13 figure

Pseudospectral Model Predictive Control under Partially Learned Dynamics

Trajectory optimization of a controlled dynamical system is an essential part of autonomy, however many trajectory optimization techniques are limited by the fidelity of the underlying parametric model. In the field of robotics, a lack of model knowledge can be overcome with machine learning techniques, utilizing measurements to build a dynamical model from the data. This paper aims to take the middle ground between these two approaches by introducing a semi-parametric representation of the underlying system dynamics. Our goal is to leverage the considerable information contained in a traditional physics based model and combine it with a data-driven, non-parametric regression technique known as a Gaussian Process. Integrating this semi-parametric model with model predictive pseudospectral control, we demonstrate this technique on both a cart pole and quadrotor simulation with unmodeled damping and parametric error. In order to manage parametric uncertainty, we introduce an algorithm that utilizes Sparse Spectrum Gaussian Processes (SSGP) for online learning after each rollout. We implement this online learning technique on a cart pole and quadrator, then demonstrate the use of online learning and obstacle avoidance for the dubin vehicle dynamics.Comment: Accepted but withdrawn from AIAA Scitech 201

Real-time model predictive control for quadrotors

This paper presents a solution to on-board trajectory tracking control of quadrotors. The proposed approach combines the standard hierarchical control paradigm that separates the control into low-level motor control, mid-level attitude dynamics control, and a high-level trajectory tracking with a model predictive control strategy. We use dynamic reduction of the attitude dynamics and dynamic extension of the thrust control along with feedback linearisation to obtain a linear system of McMillan degree three that models force controlled position and trajectory tracking for the quadrotor. Model predictive control is then used on the feedback equivalent system and its control outputs are transformed back into the inputs for the original system. The proposed structure leads to a low complexity model predictive control algorithm that is implemented in real-time on an embedded hardware. Experimental results on different position and trajectory tracking control are presented to illustrate the application of the derived linear system and controllers

Development of Robust Control Laws for Disturbance Rejection in Rotorcraft UAVs

Inherent stability inside the flight envelope must be guaranteed in order to safely introduce private and commercial UAV systems into the national airspace. The rejection of unknown external wind disturbances offers a challenging task due to the limited available information about the unpredictable and turbulent characteristics of the wind. This thesis focuses on the design, development and implementation of robust control algorithms for disturbance rejection in rotorcraft UAVs. The main focus is the rejection of external disturbances caused by wind influences. Four control algorithms are developed in an effort to mitigate wind effects: baseline nonlinear dynamic inversion (NLDI), a wind rejection extension for the NLDI, NLDI with adaptive artificial neural networks (ANN) augmentation, and NLDI with L1 adaptive control augmentation. A simulation environment is applied to evaluate the performance of these control algorithms under external wind conditions using a Monte Carlo analysis. Outdoor flight test results are presented for the implementation of the baseline NLDI, NLDI augmented with adaptive ANN and NLDI augmented with L1 adaptive control algorithms in a DJI F330 Flamewheel quadrotor UAV system. A set of metrics is applied to compare and evaluate the overall performance of the developed control algorithms under external wind disturbances. The obtained results show that the extended NLDI exhibits undesired characteristics while the augmentation of the baseline NLDI control law with adaptive ANN and L1 output-feedback adaptive control improve the robustness of the translational and rotational dynamics of a rotorcraft UAV in the presence of wind disturbances

Perception-aware time optimal path parameterization for quadrotors

The increasing popularity of quadrotors has given rise to a class of predominantly vision-driven vehicles. This paper addresses the problem of perception-aware time optimal path parametrization for quadrotors. Although many different choices of perceptual modalities are available, the low weight and power budgets of quadrotor systems makes a camera ideal for on-board navigation and estimation algorithms. However, this does come with a set of challenges. The limited field of view of the camera can restrict the visibility of salient regions in the environment, which dictates the necessity to consider perception and planning jointly. The main contribution of this paper is an efficient time optimal path parametrization algorithm for quadrotors with limited field of view constraints. We show in a simulation study that a state-of-the-art controller can track planned trajectories, and we validate the proposed algorithm on a quadrotor platform in experiments.Comment: Accepted to appear at ICRA 202