Attitude control for a quadrotor UAV using adaptive fuzzy backstepping

With improvements on automation, computer, electronics and other technologies, applications of unmanned aerial vehicles (UAVs) have expanded from pure military field to civilian areas. As a multirotor aircraft, a quadrotor UAV has the advantages of simple structure, small size, high manoeuvrability, etc. On the basis of summarizing the current research situation of the quadrotor UAV, a deep research has been conducted on the attitude control system of the quadrotor UAV and two controllers are proposed to generate a stable performance: Back-stepping controller, adaptive fuzzy back-stepping nonlinear controller. The quadrotor UAV consists of two pairs of rotors and propellers, which can generate thrust and air drag. The dynamic model is derived using the Euler-Lagrangian method and Newton method with 6 degrees of freedom. To represent the model of the quadrotor, Euler angles representation is first derived. However, facing the gimbal lock drawback of Euler angles representation, unit quaternion representation is then discussed afterwards. In normal situations, model parameter uncertainties and external disturbances would affect the system output. Due to this problem, an adaptive fuzzy strategy is designed to approximate the uncertain model using back-stepping techniques with the Lyapunov stability theorem. Firstly, simulations are used to prove the mathematical feasibility. And then experimental results will be provided to illustrate the satisfactory performances of the proposed approach in real time

Improving Leader-Follower Formation Control Performance for Quadrotors

This thesis aims to improve the leader-follower team formation flight performance of Unmanned Aerial Vehicles (UAVs) by applying nonlinear robust and optimal techniques, in particular the nonlinear H_infinity and the iterative Linear Quadratic Regulator (iLQR), to stabilisation, path tracking and leader-follower team formation control problems. Existing solutions for stabilisation, path tracking and leader-follower team formation control have addressed a linear or nonlinear control technique for a linearised system with limited disturbance consideration, or for a nonlinear system with an obstacle-free environment. To cover part of this area of research, in this thesis, some nonlinear terms were included in the quadrotors' dynamic model, and external disturbance and model parameter uncertainties were considered. Five different controllers were developed. The first and the second controllers, the nonlinear suboptimal H_infinity control technique and the Integral Backstepping (IBS) controller, were based on Lyapunov theory. The H_infinity controller was developed with consideration of external disturbance and model parameter uncertainties. These two controllers were compared for path tracking and leader-follower team formation control. The third controller was the Proportional Derivative square (PD2), which was applied for attitude control and compared with the H_infinity controller. The fourth and the fifth controllers were the Linear Quadratic Regulator (LQR) control technique and the optimal iLQR, which was developed based on the LQR control technique. These were applied for attitude, path tracking and team formation control and there results were compared. Two features regarding the choice of the control technique were addressed: stability and robustness on the one hand, which were guaranteed using the H_infinity control technique as the disturbance is inherent in its mathematical model, and the improvement in the performance optimisation on the other, which was achieved using the iLQR technique as it is based on the optimal LQR control technique. Moreover, one loop control scheme was used to control each vehicle when these controllers were implemented and a distributed control scheme was proposed for the leader-follower team formation problem. Each of the above mentioned controllers was tested and verified in simulation for different predefined paths. Then only the nonlinear H_infinity controller was tested in both simulation and real vehicles experiments

Enhancing 3D Autonomous Navigation Through Obstacle Fields: Homogeneous Localisation and Mapping, with Obstacle-Aware Trajectory Optimisation

Small flying robots have numerous potential applications, from quadrotors for search and rescue, infrastructure inspection and package delivery to free-flying satellites for assistance activities inside a space station. To enable these applications, a key challenge is autonomous navigation in 3D, near obstacles on a power, mass and computation constrained platform. This challenge requires a robot to perform localisation, mapping, dynamics-aware trajectory planning and control. The current state-of-the-art uses separate algorithms for each component. Here, the aim is for a more homogeneous approach in the search for improved efficiencies and capabilities. First, an algorithm is described to perform Simultaneous Localisation And Mapping (SLAM) with physical, 3D map representation that can also be used to represent obstacles for trajectory planning: Non-Uniform Rational B-Spline (NURBS) surfaces. Termed NURBSLAM, this algorithm is shown to combine the typically separate tasks of localisation and obstacle mapping. Second, a trajectory optimisation algorithm is presented that produces dynamically-optimal trajectories with direct consideration of obstacles, providing a middle ground between path planners and trajectory smoothers. Called the Admissible Subspace TRajectory Optimiser (ASTRO), the algorithm can produce trajectories that are easier to track than the state-of-the-art for flight near obstacles, as shown in flight tests with quadrotors. For quadrotors to track trajectories, a critical component is the differential flatness transformation that links position and attitude controllers. Existing singularities in this transformation are analysed, solutions are proposed and are then demonstrated in flight tests. Finally, a combined system of NURBSLAM and ASTRO are brought together and tested against the state-of-the-art in a novel simulation environment to prove the concept that a single 3D representation can be used for localisation, mapping, and planning

Agile load transportation systems using aerial robots

In this dissertation, we address problems that can occur during load transport using aerial robots, i.e., small scale quadrotors. First, detailed models of such transportation system are derived. These models include nonlinear models of a quadrotor, a model of a quadrotor carrying a fixed load and a model of a quadrotor carrying a suspended load. Second, the problem of quadrotor stabilization and trajectory tracking with changes of the center of gravity of the transportation system is addressed. This problem is solved using model reference adaptive control based on output feedback linearization that compensates for dynamical changes in the center of gravity of the quadrotor. The third problem we address is a problem of a swing-free transport of suspended load using quadrotors. Flying with a suspended load can be a very challenging and sometimes hazardous task as the suspended load significantly alters the flight characteristics of the quadrotor. In order to deal with suspended load flight, we present a method based on dynamic programming which is a model based offline method. The second investigated method we use is based on the Nelder-Mead algorithm which is an optimization technique used for nonlinear unconstrained optimization problems. This method is model free and it can be used for offline or online generation of the swing-free trajectories for the suspended load. Besides the swing-free maneuvers with suspended load, load trajectory tracking is another problem we solve in this dissertation. In order to solve this problem we use a Nelder-Mead based algorithm. In addition, we use an online least square policy iteration algorithm. At the end, we propose a high level algorithm for navigation in cluttered environments considering a quadrotor with suspended load. Furthermore, distributed control of multiple quadrotors with suspended load is addressed too. The proposed hierarchical architecture presented in this doctoral dissertation is an important step towards developing the next generation of agile autonomous aerial vehicles. These control algorithms enable quadrotors to display agile maneuvers while reconfiguring in real time whenever a change in the center of gravity occurs. This enables a swing-free load transport or trajectory tracking of the load in urban environments in a decentralized fashion

Modelling, identification, and control of a quadrotor helicopter

In this dissertation, we focused on the study of an autonomous flight control of quadrotor helicopter. Robust nonlinear control design strategies using observer-based control are developed, which are capable of achieving reliable and accurate tracking control for quadrotor UAV containing dynamic uncertainties, external disturbances. In order to ease readability of this dissertation, detailed explanations of the mathematical model of quadrotor UAV is provided, including the Newton-Euler formalism, Lyapunov-based stability analysis methods, sliding mode control (SMC) and backstepping fundamentals, and observer-based nonlinear control tools. The tracking control problem of a quadrotor in the presence of model uncertainties and external disturbances is investigated. Particularly, this dissertation presents the design and experimental implementation of nonlinear controller of quadrotor with observer to estimate the uncertainties and external disturbances to meet the desired control objectives. Based on a nonlinear model which considers basic aerodynamic forces and external disturbances, the quadrotor UAV model is simulated to perform a variety of maneuvering such as take-off, landing, smooth translation and horizontal and circular trajectory motions. Backstepping and sliding mode techniques combined with observers are studied, tested and compared. Simulation and a real platform were developed to prove the ability of the observer-based controller to successfully perform certain missions in the presence of unknown external disturbances and can obtain good and satisfactory estimation

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

Safe navigation and motion coordination control strategies for unmanned aerial vehicles

Unmanned aerial vehicles (UAVs) have become very popular for many military and civilian applications including in agriculture, construction, mining, environmental monitoring, etc. A desirable feature for UAVs is the ability to navigate and perform tasks autonomously with least human interaction. This is a very challenging problem due to several factors such as the high complexity of UAV applications, operation in harsh environments, limited payload and onboard computing power and highly nonlinear dynamics. Therefore, more research is still needed towards developing advanced reliable control strategies for UAVs to enable safe navigation in unknown and dynamic environments. This problem is even more challenging for multi-UAV systems where it is more efficient to utilize information shared among the networked vehicles. Therefore, the work presented in this thesis contributes towards the state-of-the-art in UAV control for safe autonomous navigation and motion coordination of multi-UAV systems. The first part of this thesis deals with single-UAV systems. Initially, a hybrid navigation framework is developed for autonomous mobile robots using a general 2D nonholonomic unicycle model that can be applied to different types of UAVs, ground vehicles and underwater vehicles considering only lateral motion. Then, the more complex problem of three-dimensional (3D) collision-free navigation in unknown/dynamic environments is addressed. To that end, advanced 3D reactive control strategies are developed adopting the sense-and-avoid paradigm to produce quick reactions around obstacles. A special case of navigation in 3D unknown confined environments (i.e. tunnel-like) is also addressed. General 3D kinematic models are considered in the design which makes these methods applicable to different UAV types in addition to underwater vehicles. Moreover, different implementation methods for these strategies with quadrotor-type UAVs are also investigated considering UAV dynamics in the control design. Practical experiments and simulations were carried out to analyze the performance of the developed methods. The second part of this thesis addresses safe navigation for multi-UAV systems. Distributed motion coordination methods of multi-UAV systems for flocking and 3D area coverage are developed. These methods offer good computational cost for large-scale systems. Simulations were performed to verify the performance of these methods considering systems with different sizes