Adaptive Nonlinear Control for Unmanned Aerial Vehicles: Visual Servoing and Aerial Manipulation
Creators: Rafique, Muhammad Awais
Publisher: name: University of Alberta Library
Year: 2022

<p>With the improvements in the autonomy and capability of Unmanned Aerial Vehicles (UAVs), there is an increased interest in their applications in infrastructure inspection and maintenance. The focus of this thesis is to study new methods for improved UAV autonomy. In this regard, the motion control of two UAV configurations is studied. First, a novel visual servoing control is presented, intended for inspection of linear structures \eg power transmission lines and pipelines. Second, the motion control of an Unmanned Aerial Manipulator (UAM) that attaches a robotic arm to the UAV is proposed. This configuration makes a UAV capable of tasks involving interaction with the environment \eg maintenance or disaster recovery. </p>

<p>The inspection of linear structures such as power transmission lines depends on high-quality video used for managing maintenance and repair. A UAV with an onboard camera is ideally suited to safely and efficiently collect this inspection data. Accurate and robust motion control is key to obtaining quality line video. Traditionally, UAV position and linear velocity estimates are obtained using a Global Positioning System (GPS). However, GPS lacks the accuracy needed for close inspection and its dependence on an external signal limits the vehicle&#39;s autonomy. Also, the infrastructure spans a vast area and is often inaccurately mapped. Image-Based Visual Servoing (IBVS) is an appropriate framework for accurately controlling the relative position between the UAV and a linear target. IBVS detects the line in the image and describes its relative position and yaw using image features. The coordinates of these features are used directly in the state feedback control for UAVs. We present a new IBVS method with a number of features, including an output feedback design that removes the need for linear velocity measurements. The control adapts to change in sensor bias, vehicle thrust constant and external disturbances. Also, the proposed approach is robust against variations in vehicle mass and camera focal length. An inner-outer loop control structure is used. The feature and linear velocity estimate errors are shown to be exponentially convergent using Lyapunov stability analysis. The effectiveness of the approach is evaluated in simulation and experiment.</p>

<p>The second part of the thesis considers the motion control of a UAM, which is a UAV combined with a multiple degrees of freedom (DOF) robot arm. Unlike traditional UAVs, a UAM is designed to interact with the environment and used in maintenance and similar applications. Motion control of a UAM is a challenging problem, given the system&#39;s coupled dynamics. The effect of arm motion on the UAV results in a highly nonlinear and complex system model. We present the dynamic model of the UAM and simplify it to obtain UAV dynamics which includes uncertainty, to model the effect of the arm. We propose two motion controllers using the adaptive backstepping approach based on this model. An inner-outer loop control structure, which treats translational and rotational dynamics of UAV subsystem separately and designs respective outer and inner loop controllers, is employed. The outer loop provides thrust input and roll and pitch references that serve as reference trajectories for the inner loop control, providing torque inputs. Since the inner-outer loop control approach lacks the stability analysis for the entire closed-loop, another adaptive backstepping method that uses the entire UAV dynamics is presented. The proposed approaches are rigorously tested in a multi-body simulation environment. </p>