Modeling and nonlinear adaptive control of an aerial manipulation system

Autonomous aerial robots have become an essential part of many civilian and military applications. The workspace and agility of these vehicles motivated great research interest resulting in various studies addressing their control architectures and mechanical configurations. Increasing autonomy enabled them to perform tasks such as surveillance, inspection and remote sensing in hazardous and challenging environments. The ongoing research promises further contributions to the society, in both theory and practice. To furthermore extend their vast applications, aerial robots are equipped with the tools to enable physical interaction with the environment. These tasks represent a great challenge due to the technological limitations as well as the lack of sophisticated methods necessary for the control of the system to perform desired operations in an efficient and stable manner. Modeling and control problem of an aerial manipulation is still an open research topic with many studies addressing these issues from different perspectives. This thesis deals with the nonlinear adaptive control of an aerial manipulation system (AMS). The system consists of a quadrotor equipped with a 2 degrees of freedom (DOF) manipulator. The complete modeling of the system is done using the Euler-Lagrange method. A hierarchical nonlinear control structure which consists of outer and inner control loops has been utilized. Model Reference Adaptive Controller (MRAC) is designed for the outer loop where the required command signals are generated to force the quadrotor to move on a reference trajectory in the presence of mass uncertainties and reaction forces coming from the manipulator. For the inner loop, the attitude dynamics of the quadrotor and the joint dynamics of the 2-DOF robotic arm are considered as a fully actuated 5-DOF unified part of the AMS. Nonlinear adaptive control has been utilized for the low-level controller where the changes in inertias have been considered. The proposed controller is tested on a high fidelity AMS model in the presence of uncertainties, wind disturbances and measurement noise, and satisfactory trajectory tracking performance with improved robustness is achieved

Design and Demonstration of a Two-Dimentional Test Bed for UAV Controller Evaluation

A three degree-of-freedom (DOF) planar test bed for Unmanned Aerial Vehicle (UAV) controller evaluation was built. The test-bed consists of an instrumented tether and an experimental twin-rotor, planar UAV mounted with a one DOF manipulator mounted below the UAV body. The tether was constructed to constrain the UAV under test to motion on the surface of a sphere. Experiments can be conducted through the tether, approximating motion in a vertical plane by a UAV under test. The tether provides the means to measure the position and attitude of the UAV under test. The experimental twin-rotor UAV and one-link on-board manipulator, were designed and built to explore a unified control strategy for Manipulator on VTOL Aircraft (MOVA), in which the interaction of UAV body dynamics with the manipulator motion is of primary interest. The dynamics of the propulsion unit was characterized through experiments, based on which a phase lead compensator was designed to improve the UAV frequency response. A \u27separate\u27 controller based on independent nonlinear control of the VTOL aircraft and PD linear control of the on-board manipulator was designed as a reference for comparison to the unified MOVA controller. Tests with the separate controller show the negative effect that a coupled manipulator can have on the UAV body motion, while the tests on MOVA show the potential benefit of explicit compensation of the UAV and manipulator interaction

Unified Dynamics and Control of a Robot Manipulator Mounted on a VTOL Aircraft Platform

An innovative type of mobile manipulator, designated Manipulator on VTOL (Vertical Take-Off and Landing) Aircraft (MOVA), is proposed as a potential candidate for autonomous execution of field work in less-structured indoor and outdoor environments. Practical use of the MOVA system requires a unified controller that addresses the coupled and complex dynamics of the composite system; especially the interaction of the robotic manipulator with the aircraft airframe. Model-based controller design methods require explicit dynamics models of the MOVA system. Preliminary investigation of a two-dimensional MOVA system toward a dynamics model and controller design is presented in preparation for developing the controller of the more complex MOVA system in 3D space. Dynamics of the planar MOVA system are derived using the Lagrangian approach and then transforming the result into a form that facilitates controller design using the concept of a virtual manipulator. A MOVA end-effector trajectory tracking controller was designed with the transformed dynamics equation using the integrator back-stepping control design framework. Validity of the controller is shown via stability analysis, simulation results, and results from a physical test-bed. A systematic approach is illustrated for the derivation of the 3D MOVA system dynamics equations. The resulting dynamics equations are represented abstractly in the standard robot dynamics form and proven to have the skew-symmetric property, which is a useful property for control derivation. An open source Mathematica program was developed to achieve automatic symbolic derivation of the MOVA system dynamics. Accessory tools were also designed to create a tool-chain that starts with an Autodesk Inventor CAD drawing, generates input to the Mathematica program, and then formats the output for direct use in MATLAB and Simulink. A unified nonlinear control algorithm that controls the 3D MOVA system, including both the aircraft and the onboard manipulator, as a single entity was developed to achieve trajectory tracking of the MOVA end-effector position and attitude based on the explicit dynamics equation. Globally Uniformly Ultimately Bounded (GUUB) stability is proven for the controller using Lyapunov-type stability analysis. Physical testing was constructed in order to to demonstrate the performance of the proposed controller on a MOVA system with a two-link onboard manipulator

Design and control of next-generation uavs for effectively interacting with environments

In this dissertation, the design and control of a novel multirotor for aerial manipulation is studied, with the aim of endowing the aerial vehicle with more degrees of freedom of motion and stability when interacting with the environments. Firstly, it presents an energy-efficient adaptive robust tracking control method for a class of fully actuated, thrust vectoring unmanned aerial vehicles (UAVs) with parametric uncertainties including unknown moment of inertia, mass and center of mass, which would occur in aerial maneuvering and manipulation. The effectiveness of this method is demonstrated through simulation. Secondly, a humanoid robot arm is adopted to serve as a 6-degree-of-freedom (DOF) automated flight testing platform for emulating the free flight environment of UAVs while ensuring safety. Another novel multirotor in a tilt-rotor architecture is studied and tested for coping with parametric uncertainties in aerial maneuvering and manipulation. Two pairs of rotors are mounted on two independently-controlled tilting arms placed at two sides of the vehicle in a H configuration to enhance its maneuverability and stability through an adaptive robust control method. In addition, an impedance control algorithm is deployed in the out loop that modifies the trajectory to achieve a compliant behavior in the end-effector space for aerial drilling and screwing tasks

An adaptive hierarchical control for aerial manipulators

This paper addresses the trajectory tracking control problem for a quadrotor aerial vehicle, equipped with a robotic manipulator (aerial manipulator). The controller is organized in two layers: in the top layer, an inverse kinematics algorithm computes the motion references for the actuated variables; in the bottom layer, a motion control algorithm is in charge of tracking the motion references computed by the upper layer. To the purpose, a model-based control scheme is adopted, where modelling uncertainties are compensated through an adaptive term. The stability of the proposed scheme is proven by resorting to Lyapunov arguments. Finally, a simulation case study is proposed to prove the effectiveness of the approach

Behavioral control of unmanned aerial vehicle manipulator systems

In this paper a behavioral control framework is developed to control an unmanned aerial vehicle-manipulator (UAVM) system, composed by a multirotor aerial vehicle equipped with a robotic arm. The goal is to ensure vehicle-arm coordination and manage complex multi-task missions, where different behaviors must be encompassed in a clear and meaningful way. In detail, a control scheme, based on the null space-based behavioral paradigm, is proposed to handle the coordination between the arm and vehicle motion. To this aim, a set of basic functionalities (elementary behaviors) are designed and combined in a given priority order, in order to attain more complex tasks (compound behaviors). A supervisor is in charge of switching between the compound behaviors according to the mission needs and the sensory feedback. The method is validated on a real testbed, consisting of a multirotor aircraft with an attached 6 Degree of Freedoms manipulator, developed within the EU-funded project ARCAS (Aerial Robotics Cooperative Assembly System). At the the best of authors’ knowledge, this is the first time that an UAVM system is experimentally tested in the execution of complex multi-task missions. The results show that, by properly designing a set of compound behaviors and a supervisor, vehicle-arm coordination in complex missions can be effectively managed

Learning-based Predictive Path Following Control for Nonlinear Systems Under Uncertain Disturbances

Accurate path following is challenging for autonomous robots operating in uncertain environments. Adaptive and predictive control strategies are crucial for a nonlinear robotic system to achieve high-performance path following control. In this paper, we propose a novel learning-based predictive control scheme that couples a high-level model predictive path following controller (MPFC) with a low-level learning-based feedback linearization controller (LB-FBLC) for nonlinear systems under uncertain disturbances. The low-level LB-FBLC utilizes Gaussian Processes to learn the uncertain environmental disturbances online and tracks the reference state accurately with a probabilistic stability guarantee. Meanwhile, the high-level MPFC exploits the linearized system model augmented with a virtual linear path dynamics model to optimize the evolution of path reference targets, and provides the reference states and controls for the low-level LB-FBLC. Simulation results illustrate the effectiveness of the proposed control strategy on a quadrotor path following task under unknown wind disturbances.Comment: 8 pages, 7 figures, accepted for publication in IEEE Robotics and Automation Letters ( Volume: 6, Issue: 2, April 2021