A Survey of path following control strategies for UAVs focused on quadrotors

The trajectory control problem, defined as making a vehicle follow a pre-established path in space, can be solved by means of trajectory tracking or path following. In the trajectory tracking problem a timed reference position is tracked. The path following approach removes any time dependence of the problem, resulting in many advantages on the control performance and design. An exhaustive review of path following algorithms applied to quadrotor vehicles has been carried out, the most relevant are studied in this paper. Then, four of these algorithms have been implemented and compared in a quadrotor simulation platform: Backstepping and Feedback Linearisation control-oriented algorithms and NLGL and Carrot-Chasing geometric algorithms.Peer ReviewedPostprint (author's final draft

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

Geometric versus Model Predictive Control based guidance algorithms for fixed-wing UAVs in the presence of very strong wind fields.

The recent years have witnessed increased development of small, autonomous fixed-wing Unmanned Aerial Vehicles (UAVs). In order to unlock widespread applicability of these platforms, they need to be capable of operating under a variety of environmental conditions. Due to their small size, low weight, and low speeds, they require the capability of coping with wind speeds that are approaching or even faster than the nominal airspeed. In this thesis, a nonlinear-geometric guidance strategy is presented, addressing this problem. More broadly, a methodology is proposed for the high-level control of non-holonomic unicycle-like vehicles in the presence of strong flowfields (e.g. winds, underwater currents) which may outreach the maximum vehicle speed. The proposed strategy guarantees convergence to a safe and stable vehicle configuration with respect to the flowfield, while preserving some tracking performance with respect to the target path. As an alternative approach, an algorithm based on Model Predictive Control (MPC) is developed, and a comparison between advantages and disadvantages of both approaches is drawn. Evaluations in simulations and a challenging real-world flight experiment in very windy conditions confirm the feasibility of the proposed guidance approach

A novel approach to the control of quad-rotor helicopters using fuzzy-neural networks

Quad-rotor helicopters are agile aircraft which are lifted and propelled by four rotors. Unlike traditional helicopters, they do not require a tail-rotor to control yaw, but can use four smaller fixed-pitch rotors. However, without an intelligent control system it is very difficult for a human to successfully fly and manoeuvre such a vehicle. Thus, most of recent research has focused on small unmanned aerial vehicles, such that advanced embedded control systems could be developed to control these aircrafts. Vehicles of this nature are very useful when it comes to situations that require unmanned operations, for instance performing tasks in dangerous and/or inaccessible environments that could put human lives at risk. This research demonstrates a consistent way of developing a robust adaptive controller for quad-rotor helicopters, using fuzzy-neural networks; creating an intelligent system that is able to monitor and control the non-linear multi-variable flying states of the quad-rotor, enabling it to adapt to the changing environmental situations and learn from past missions. Firstly, an analytical dynamic model of the quad-rotor helicopter was developed and simulated using Matlab/Simulink software, where the behaviour of the quad-rotor helicopter was assessed due to voltage excitation. Secondly, a 3-D model with the same parameter values as that of the analytical dynamic model was developed using Solidworks software. Computational Fluid Dynamics (CFD) was then used to simulate and analyse the effects of the external disturbance on the control and performance of the quad-rotor helicopter. Verification and validation of the two models were carried out by comparing the simulation results with real flight experiment results. The need for more reliable and accurate simulation data led to the development of a neural network error compensation system, which was embedded in the simulation system to correct the minor discrepancies found between the simulation and experiment results. Data obtained from the simulations were then used to train a fuzzy-neural system, made up of a hierarchy of controllers to control the attitude and position of the quad-rotor helicopter. The success of the project was measured against the quad-rotor’s ability to adapt to wind speeds of different magnitudes and directions by re-arranging the speeds of the rotors to compensate for any disturbance. From the simulation results, the fuzzy-neural controller is sufficient to achieve attitude and position control of the quad-rotor helicopter in different weather conditions, paving way for future real time applications

DECENTRALIZED ROBUST NONLINEAR MODEL PREDICTIVE CONTROLLER FOR UNMANNED AERIAL SYSTEMS

The nonlinear and unsteady nature of aircraft aerodynamics together with limited practical range of controls and state variables make the use of the linear control theory inadequate especially in the presence of external disturbances, such as wind. In the classical approach, aircraft are controlled by multiple inner and outer loops, designed separately and sequentially. For unmanned aerial systems in particular, control technology must evolve to a point where autonomy is extended to the entire mission flight envelope. This requires advanced controllers that have sufficient robustness, track complex trajectories, and use all the vehicles control capabilities at higher levels of accuracy. In this work, a robust nonlinear model predictive controller is designed to command and control an unmanned aerial system to track complex tight trajectories in the presence of internal and external perturbance. The Flight System developed in this work achieves the above performance by using: 1 A nonlinear guidance algorithm that enables the vehicle to follow an arbitrary trajectory shaped by moving points; 2 A formulation that embeds the guidance logic and trajectory information in the aircraft model, avoiding cross coupling and control degradation; 3 An artificial neural network, designed to adaptively estimate and provide aerodynamic and propulsive forces in real-time; and 4 A mixed sensitivity approach that enhances the robustness for a nonlinear model predictive controller overcoming the effect of un-modeled dynamics, external disturbances such as wind, and measurement additive perturbations, such as noise and biases. These elements have been integrated and tested in simulation and with previously stored flight test data and shown to be feasible

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

Experimental Investigation of Shrouded Rotor Micro Air Vehicle in Hover and in Edgewise Gusts

Due to the hover capability of rotary wing Micro Air Vehicles (MAVs), it is of interest to improve their aerodynamic performance, and hence hover endurance (or payload capability). In this research, a shrouded rotor conguration is studied and implemented, that has the potential to oer two key operational benets: enhanced system thrust for a given input power, and improved structural rigidity and crashworthiness of an MAV platform. The main challenges involved in realising such a system for a lightweight craft are: design of a lightweight and stiff shroud, and increased sensitivity to external flow disturbances that can affect flight stability. These key aspects are addressed and studied in order to assess the capability of the shrouded rotor as a platform of choice for MAV applications. A fully functional shrouded rotor vehicle (disk loading 60 N/m2) was designed and constructed with key shroud design variables derived from previous studies on micro shrouded rotors. The vehicle weighed about 280 g (244 mm rotor diameter). The shrouded rotor had a 30% increase in power loading in hover compared to an unshrouded rotor. Due to the stiff, lightweight shroud construction, a net payload benefit of 20-30 g was achieved. The different components such as the rotor, stabilizer bar, yaw control vanes and the shroud were systematically studied for system efficiency and overall aerodynamic improvements. Analysis of the data showed that the chosen shroud dimensions was close to optimum for a design payload of 250 g. Risk reduction prototypes were built to sequentially arrive at the nal conguration. In order to prevent periodic oscillations in flight, a hingeless rotor was incorporated in the shroud. The vehicle was successfully flight tested in hover with a proportional-integral-derivative feedback controller. A flybarless rotor was incorporated for efficiency and control moment improvements. Time domain system identification of the attitude dynamics of the flybar and flybarless rotor vehicle was conducted about hover. Controllability metrics were extracted based on controllability gramian treatment for the flybar and flybarless rotor. In edgewise gusts, the shrouded rotor generated up to 3 times greater pitching moment and 80% greater drag than an equivalent unshrouded rotor. In order to improve gust tolerance and control moments, rotor design optimizations were made by varying solidity, collective, operating RPM and planform. A rectangular planform rotor at a collective of 18 deg was seen to offer the highest control moments. The shrouded rotor produced 100% higher control moments due to pressure asymmetry arising from cyclic control of the rotor. It was seen that the control margin of the shrouded rotor increased as the disk loading increased, which is however deleterious in terms of hover performance. This is an important trade-off that needs to be considered. The flight performance of the vehicle in terms of edgewise gust disturbance rejection was tested in a series of bench top and free flight tests. A standard table fan and an open jet wind tunnel setup was used for bench top setup. The shrouded rotor had an edgewise gust tolerance of about 3 m/s while the unshrouded rotor could tolerate edgewise gusts greater than 5 m/s. Free flight tests on the vehicle, using VICON for position feedback control, indicated the capability of the vehicle to recover from gust impulse inputs from a pedestal fan at low gust values (up to 3 m/s)

Optic Flow for Obstacle Avoidance and Navigation: A Practical Approach

This thesis offers contributions and innovations to the development of vision-based autonomous flight control systems for small unmanned aerial vehicles operating in cluttered urban environments. Although many optic flow algorithms have been reported, almost none have addressed the critical issue of accuracy and reliability over a wide dynamic range of optic flow. My aim is to rigorously develop improved optic flow sensing to meet realistic mission requirements for autonomous navigation and collision avoidance. A review of related work enabled development of a new hybrid optic flow algorithm concept combining the best properties of image correlation and interpolation with additional innovations to enhance accuracy, computational speed and reliability. Key analytical work yielded a methodology for determining optic flow dynamic range requirements from system and sensor design parameters and a technique enabling a video sensor to operate as a passive ranging system for closed loop flight control. Detailed testing led to development of the hybrid image interpolation algorithm (HI2A) using improved correlation search strategies, sparse images to reduce processing loads, a solution tracking loop to bypass the more intensive initial estimation process, a frame look-back method to improve accuracy at low optic flow, a modified interpolation technique to improve robustness and an extensive error checking system for validating outputs. A realistic simulation system was developed incorporating independent, precision ground truthing to assess algorithm accuracy. Comparison testing of the HI2A against the commonly-used Lucas Kanade algorithm demonstrates major improvement in accuracy over greatly expanded dynamic range. A reactive flight controller using ranging data from a monocular, forward looking video sensor and rules-based logic was developed and tested in Monte Carlo simulations of a hundred flights. At higher flight speeds than reported in similar tests, collision-free results were obtained in a realistic urban canyon environment. The HI2A algorithm and flight controller software performance on a common PC was up to eight times faster than real-time for outputs of 250 measurements at 50 Hz. The feasibility of terrain mapping in real-time was demonstrated using 3D ranging data from optic flow in an overflight of the urban simulation environment indicating the potential for its use in path planning approaches to navigation and collision avoidance

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