Precise Trajectory Tracking of Multi-Rotor UAVs Using Wind Disturbance Rejection Approach

This paper discusses the resilience of the UAV quadrotor to wind disturbances. An unknown input-state observer is presented that uses the Lipschitz method to estimate the internal states and disturbances of the quadrotor and compensate for them by varying the velocities of the four rotors. The observer intends to use existing sensor measurements to estimate the unknown states of the quadrotor and reconstruct the three-dimensional wind disturbances. The estimated states and external disturbances are sent to the PD controller, which compensates for the disturbances to achieve the desired position and attitude, as well as robustness and accuracy. The Lipschitz observer was designed using the LMI approach, and the results were validated using Matlab/Simulink and using the Parrot Mambo mini quadrotor

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

Aerial Vehicles

This book contains 35 chapters written by experts in developing techniques for making aerial vehicles more intelligent, more reliable, more flexible in use, and safer in operation.It will also serve as an inspiration for further improvement of the design and application of aeral vehicles. The advanced techniques and research described here may also be applicable to other high-tech areas such as robotics, avionics, vetronics, and space

GPS based position control and waypoint navigation of a quad tilt-wing UAV

Unmanned aerial vehicles (UAV) are becoming increasingly capable nowadays and the civilian applications and the military tasks that can be carried out by these vehicles are far more critical than before. There have been remarkable advances in the design and development of UAVs. They are equipped with various sensors which make them capable of accomplishing missions in unconstrained environments which are dangerous or effortful for manned aircrafts. Recently, significant interest in unmanned aerial vehicles has directed researchers towards navigation problem of flying vehicles. This thesis work focuses on GPS based position control and waypoint navigation of a quad tilt-wing unmanned aerial vehicle (SUAVI: Sabanci University Unmanned Aerial Vehicle). The vehicle is capable of vertical take-off and landing (VTOL). It can also fly horizontally due to its tilt-wing structure. Mechanical and aerodynamic designs are first outlined. A nonlinear mathematical model expressed in a hybrid frame is then obtained using Newton-Euler formulation which also includes aerodynamics effects such as wind and gusts. Extended Kalman filtering (EKF) using raw IMU measurements is employed to obtain reliable orientation estimates which is crucial for attitude stabilization of the aerial vehicle. A high-level acceleration controller which utilizes GPS data produces roll and pitch references for the low-level attitude controllers for hovering and trajectory tracking of the aerial vehicle. The nonlinear dynamic equations of the vehicle are linearized around nominal operating points in hovering conditions and gravity compensated PID controllers are designed for position and attitude control. Simulations and several real flight experiments demonstrate success of the developed position control algorithms

A Contribution to the Design of Highly Redundant Compliant Aerial Manipulation Systems

Es ist vorhersehbar, dass die Luftmanipulatoren in den nächsten Jahrzehnten für viele Aufgaben eingesetzt werden, die entweder zu gefährlich oder zu teuer sind, um sie mit herkömmlichen Methoden zu bewältigen. In dieser Arbeit wird eine neuartige Lösung für die Gesamtsteuerung von hochredundanten Luftmanipulationssystemen vorgestellt. Die Ergebnisse werden auf eine Referenzkonfiguration angewendet, die als universelle Plattform für die Durchführung verschiedener Luftmanipulationsaufgaben etabliert wird. Diese Plattform besteht aus einer omnidirektionalen Drohne und einem seriellen Manipulator. Um den modularen Regelungsentwurf zu gewährleisten, werden zwei rechnerisch effiziente Algorithmen untersucht, um den virtuellen Eingang den Aktuatorbefehlen zuzuordnen. Durch die Integration eines auf einem künstlichen neuronalen Netz basierenden Diagnosemoduls und der rekonfigurierbaren Steuerungszuordnung in den Regelkreis, wird die Fehlertoleranz für die Drohne erzielt. Außerdem wird die Motorsättigung durch Rekonfiguration der Geschwindigkeits- und Beschleunigungsprofile behandelt. Für die Beobachtung der externen Kräfte und Drehmomente werden zwei Filter vorgestellt. Dies ist notwendig, um ein nachgiebiges Verhalten des Endeffektors durch die achsenselektive Impedanzregelung zu erreichen. Unter Ausnutzung der Redundanz des vorgestellten Luftmanipulators wird ein Regler entworfen, der nicht nur die Referenz der Endeffektor-Bewegung verfolgt, sondern auch priorisierte sekundäre Aufgaben ausführt. Die Wirksamkeit der vorgestellten Lösungen wird durch umfangreiche Tests überprüft, und das vorgestellte Steuerungssystem wird als sehr vielseitig und effektiv bewertet.:1 Introduction 2 Fundamentals 3 System Design and Modeling 4 Reconfigurable Control Allocation 5 Fault Diagnostics For Free Flight 6 Force and Torque Observer 7 Trajectory Generation 8 Hybrid Task Priority Control 9 System Integration and Performance Evaluation 10 ConclusionIn the following decades, aerial manipulators are expected to be deployed in scenarios that are either too dangerous for human beings or too expensive to be accomplished by traditional methods. This thesis presents a novel solution for the overall control of highly redundant aerial manipulation systems. The results are applied to a reference configuration established as a universal platform for performing various aerial manipulation tasks. The platform consists of an omnidirectional multirotor UAV and a serial manipulator. To ensure modular control design, two computationally efficient algorithms are studied to allocate the virtual input to actuator commands. Fault tolerance of the aerial vehicle is achieved by integrating a diagnostic module based on an artificial neural network and the reconfigurable control allocation into the control loop. Besides, the risk of input saturation of individual rotors is minimized by predicting and reconfiguring the speed and acceleration responses. Two filter-based observers are presented to provide the knowledge of external forces and torques, which is necessary to achieve compliant behavior of the end-effector through an axis-selective impedance control in the outer loop. Exploiting the redundancy of the proposed aerial manipulator, the author has designed a control law to achieve the desired end-effector motion and execute secondary tasks in order of priority. The effectiveness of the proposed designs is verified with extensive tests generated by following Monte Carlo method, and the presented control scheme is proved to be versatile and effective.:1 Introduction 2 Fundamentals 3 System Design and Modeling 4 Reconfigurable Control Allocation 5 Fault Diagnostics For Free Flight 6 Force and Torque Observer 7 Trajectory Generation 8 Hybrid Task Priority Control 9 System Integration and Performance Evaluation 10 Conclusio

Robust and Adaptive Control Methods for Small Aerial Vehicles

Recent advances in sensor and microcomputer technology and in control and aeroydynamics theories has made small unmanned aerial vehicles a reality. The small size, low cost and manoueverbility of these systems has positioned them to be potential solutions in a large class of applications. However, the small size of these vehicles pose significant challenges. The small sensors used on these systems are much noisier than their larger counterparts.The compact structure of these vehicles also makes them more vulnerable to environmental effects. This work develops several different control strategies for two sUAV platforms and provides the rationale for judging each of the controllers based on a derivation of the dynamics, simulation studies and experimental results where possible. First, the coaxial helicopter platform is considered. This sUAV’s dual rotor system (along with its stabilizer bar technology) provides the ideal platform for safe, stable flight in a compact form factor. However, the inherent stability of the vehicle is achieved at the cost of weaker control authority and therefore an inability to achieve aggressive trajectories especially when faced with heavy wind disturbances. Three different linear control strategies are derived for this platform. PID, LQR and H∞ methods are tested in simulation studies. While the PID method is simple and intuitive, the LQR method is better at handling the decoupling required in the system. However the frequency domain design of the H∞ control method is better at suppressing disturbances and tracking more aggressive trajectories. The dynamics of the quadrotor are much faster than those of the coaxial helicopter. In the quadrotor, four independent fixed pitch rotors provide the required thrust. Differences between each of the rotors creates moments in the roll, pitch and yaw directions. This system greatly simplifies the mechanical complexity of the UAV, making quadrotors cheaper to maintain and more accessible. The quadrotor dynamics are derived in this work. Due to the lack of any mechanical stabilization system, these quadrotor dynamics are not inherently damped around hover. As such, the focus of the controller development is on using nonlinear techniques. Linear quadratic regulation methods are derived and shown to be inadequate when used in zones moderately outside hover. Within nonlinear methods, feedback linearization techniques are developed for the quadrotor using an inner/outer loop decoupling structure that avoids more complex variants of the feedback linearization methodology. Most nonlinear control methods (including feedback linearization) assume perfect knowledge of vehicle parameters. In this regard, simulation studies show that when this assumption is violated the results of the flight significantly deteriorate for quadrotors flying using the feedback linearization method. With this in mind, an adaptation law is devised around the nonlinear control method that actively modifies the plant parameters in an effort to drive tracking errors to zero. In simple cases with sufficiently rich trajectory requirements the parameters are able to adapt to the correct values (as verified by simulation studies). It can also adapt to changing parameters in flight to ensure that vehicle stability and controller performance is not compromised. However, the direct adaptive control method devised in this work has the added benefit of being able to modify plant parameters to suppress the effects of external disturbances as well. This is clearly shown when wind disturbances are applied to the quadrotor simulations. Finally, the nonlinear quadrotor controllers devised above are tested on a custom built quadrotor and autopilot platform. While the custom quadrotor is able to fly using the standard control methods, the specific controllers devised here are tested on a test bench that constrains the movement of the vehicle. The results of the tests show that the controller is able to sufficiently change the necessary parameter to ensure effective tracking in the presence of unmodelled disturbances and measurement error

Vision-based Autonomous Tracking of a Non-cooperative Mobile Robot by a Low-cost Quadrotor Vehicle

The goal of this thesis is the detection and tracking of a ground vehicle, in particular a car-like robot, by a quadrotor. The first challenge to address in any pursuit or tracking scenario is the detection and unique identification of the target. From this first challenge, comes the need to precisely localize the target in a coordinate system that is common to the tracking and tracked vehicles. In most real-life scenarios, the tracked vehicle does not directly communicate information such as its position to the tracking one. From this fact, arises a non-cooperative constraint problem. The autonomous tracking aspect of the mission requires, for both the aerial and ground vehicles, robust pose estimation during the mission. The primary and crucial functions to achieve autonomous behaviors are control and navigation. The principal-agent being the quadrotor, this thesis explains in detail the derivation and analysis of the equations of motion that govern its natural behavior along with the control methods that permit to achieve desired performances. The analysis of these equations reveals a naturally unstable system, subject to non-linearities. Therefore, we explored three different control methods capable of guaranteeing stability while mitigating non-linearities. The first two control methods operate in the linear region and consist of the intuitive Proportional Integrate Derivative controller (PID). The second linear control strategy is represented by an optimal controller that is the Linear Quadratic Regulator controller (LQR). The last and final control method is a nonlinear controller designed from the Sliding Mode Control Theory. In addition to the in-depth analysis, we provide assets and limitations of each control method. In order to achieve the tracking mission, we address the detection and localization problems using respectively visual servoing and frame transform techniques. The pose estimation challenge for the aerial robot is cleared up using Kalman Filtering estimation methods that are also explored in depth. The same estimation method is used to mitigate the ground vehicle’s real-time pose estimation and tracking problem. Analysis results are illustrated using Matlab. A simulation and a real implementation using the Robot Operating System are used to support the obtained results

Design, construction and flight control of a quad tilt-wing unmanned aerial vehicle

Unmanned Aerial Vehicles (UAVs) are flying robots that are employed both in civilian and military applications with a steeply increasing trend. They are already used extensively in civilian applications such as law enforcement, earth surface mapping and surveillance in disasters, and in military missions such as surveillance, reconnaissance and target acquisition. As the demand on their utilization increases, novel designs with far more advances in autonomy, flight capabilities and payloads for carrying more complex and intelligent sensors are emerging. With these technological advances, people will find even newer operational fields for UAVs. This thesis work focuses on the design, construction and flight control of a novel UAV (SUAVI: Sabanci University Unmanned Aerial VehIcle). SUAVI is an electric powered compact size quad tilt-wing UAV, which is capable of vertical takeoff and landing (VTOL) like a helicopter, and flying horizontally like an airplane by tilting its wings. It carries onboard cameras for capturing images and broadcasting them via RF communication with the ground station. In the aerodynamic and mechanical design of SUAVI, flight duration, flight speed, size, power source and missions to be carried out are taken into account. The aerodynamic design is carried out by considering the maximization of the aerodynamic efficiency and the safe fiight characteristics. The components in the propulsion system are selected to optimize propulsion efficiency and fulfill the requirements of the control for a stable flight in the entire speed range. Simulation results obtained by ANSYS and NASA FoilSimII are evaluated and motor thrust tests are conducted during this optimization process. The power source is determined by taking the weight and flight duration into account. The wings and the fuselage are shaped iteratively in fluid flow simulations. Additionally, the verification of aerodynamic design and maneuverability are assessed in the wind tunnel tests on the half-body prototype. The mechanical structure is designed to be lightweight, strong and protective, and to allow easy assembly and disassembly of SUAVI for practical use. The safety factors in the mechanical system are determined using FEM analysis in ANSYS environment. Specimens of candidate composite skin materials are prepared and tested for lightness, strength and integrity in mechanical tests. The ready for flight prototype SUAVI is produced from the selected composite material. Dynamical model of SUAVI is obtained using Newton-Euler formulation. Aerodynamic disturbances such as wind gusts are modeled using the wellknown Dryden wind turbulence model. As the flight control system, a supervisory control architecture is implemented where a Gumstix microcomputer and several Atmega16 microcontrollers are used as the high-level and low- level controllers, respectively. Gumstix computer acts as a supervisor which orchestrates switching of low-level controllers into the system and is responsible for decision making, monitoring states of the vehicle and safety checks during the entire flight. It also generates attitude references for the low-level controllers using data from GPS or camera. Various analog and digital filters are implemented to smooth out noisy sensor measurements. Extended Kalman filter is utilized to obtain reliable orientation information by fusing data from low-cost MEMS inertial sensors such as gyros, accelerometers and the compass. PID controllers are implemented for both the high-level GPS based acceleration controller and the low-level altitude and attitude controllers. External disturbances are estimated and compensated by a disturbance observer. Real-time control software is developed for the whole fiight control system. SUAVI can operate in semi-autonomous mode by communicating with the ground station. A quadrotor test platform (SUQUAD: Sabanci University QUADrotor) is also produced and used for the initial performance tests of the fiight control system. After successful fiight tests on this platform, the control system is transferred to SUAVI. Performance of the flight control system is verified by numerous simulations and real flight experiments. VTOL and horizontal flights are successfully realized

Proceedings of the International Micro Air Vehicles Conference and Flight Competition 2017 (IMAV 2017)

The IMAV 2017 conference has been held at ISAE-SUPAERO, Toulouse, France from Sept. 18 to Sept. 21, 2017. More than 250 participants coming from 30 different countries worldwide have presented their latest research activities in the field of drones. 38 papers have been presented during the conference including various topics such as Aerodynamics, Aeroacoustics, Propulsion, Autopilots, Sensors, Communication systems, Mission planning techniques, Artificial Intelligence, Human-machine cooperation as applied to drones