Robust position control of a tilt-wing quadrotor

This paper presents a robust position controller for a tilt-wing quadrotor to track desired trajectories under external wind and aerodynamic disturbances. Wind effects are modeled using Dryden model and are included in the dynamic model of the vehicle. Robust position control is achieved by introducing a disturbance observer which estimates the total disturbance acting on the system. In the design of the disturbance observer, the nonlinear terms which appear in the dynamics of the aerial vehicle are also treated as disturbances and included in the total disturbance. Utilization of the disturbance observer implies a linear model with nominal parameters. Since the resulting dynamics are linear, only PID type simple controllers are designed for position and attitude control. Simulations and experimental results show that the performance of the observer based position control system is quite satisfactory

Robust hovering control of a quad tilt-wing UAV

This paper presents design of a robust hovering controller for a quad tilt-wing UAV to hover at a desired position under external wind and aerodynamic disturbances. Wind and the aerodynamic disturbances are modeled using the Dryden model. In order to increase the robustness of the system, a disturbance observer is utilized to estimate the unknown disturbances acting on the system. Nonlinear terms which appear in the dynamics of the vehicle are also treated as disturbances and included in the total disturbance. Proper compensation of disturbances implies a linear model with nominal parameters. Thus, for robust hovering control, only PID type simple controllers have been employed and their performances have been found very satisfactory. Proposed hovering controller has been verified with several simulations and experiments

Sensor Fusion and Obstacle Avoidance for an Unmanned Ground Vehicle

In recent years, the capabilities and potential value of unmanned autonomous systems (UAS) to perform an extensive variety of missions have significantly increased. It is well comprehended that there are various challenges associated with the realization of autonomous operations in complex urban environments. These difficulties include the requirement for precision guidance and control in conceivably GPS-denied conditions as well as the need to sense and avoid stationary and moving obstructions within the scene. The small size of some of these vehicles restricts the size, weight and power consumption of the sensor payload and onboard computational processing that can accommodated by UAS. This thesis analyzes the development and implementation of terrain mapping, path planning and control algorithms on an unmanned ground vehicle. Data from GPS, IMU and LIDAR sensors are fused in order to compute and update a dense 3D point cloud that is used by an implicit terrain algorithm to provide detailed mathematical representations of complex 3D structures generally found in urban environments. A receding horizon path planning algorithm is employed to adaptively produce a kinematically-feasible path for the unmanned ground vehicle. This path planning algorithm incorporates obstacle avoidance constraints and provides a set of waypoints to be followed by the unmanned ground vehicle. A waypoint controller is designed and implemented to enable the vehicle to follow the waypoints from the path planner. Open-loop experiments are provided with an unmanned ground vehicle in order to demonstrate terrain generation with real sensor data. Closed-loop results are then presented for a simulated ground vehicle in order to demonstrate the performance of the receding horizon path planning and control algorithms using the terrain map generated from the open-loop experiments

Autonomous Quadrocopter for Search, Count and Localization of Objects

This chapter describes and evaluates the design and implementation of a new fully autonomous quadrocopter, which is capable of self‐reliant search, count and localization of a predefined object on the ground inside a room

Modeling, identification and navigation of autonomous air vehicles

The main interest of this work is autonomous navigation of autonomous air vehicles, specifically quadrotor helicopters (quadrocopters), and the focus is on convergence to a target destination with collision avoidance. The controller computes a collision-free path leading to the target position and is based on a navigation function approach and waypoints are followed exploiting PID controller

Quadrocopter Control Design and Flight Operation

A limiting factor in control system design and analysis for spacecraft is the inability to physically test new algorithms quickly and cheaply. Test flights of space vehicles are costly and take much preparation. As such, EV41 recently acquired a small research quadrocopter that has the ability to be a test bed for new control systems. This project focused on learning how to operate, fly, and maintain the quadrocopter, as well as developing and testing protocols for its use. In parallel to this effort, developing a model in Simulink facilitated the design and analysis of simple control systems for the quadrocopter. Software provided by the manufacturer enabled testing of the Simulink control system on the vehicle

Input uncertainty sensitivity enhanced non-singleton fuzzy logic controllers for long-term navigation of quadrotor UAVs

Input uncertainty, e.g., noise on the on-board camera and inertial measurement unit, in vision-based control of unmanned aerial vehicles (UAVs) is an inevitable problem. In order to handle input uncertainties as well as further analyze the interaction between the input and the antecedent fuzzy sets (FSs) of non-singleton fuzzy logic controllers (NSFLCs), an input uncertainty sensitivity enhanced NSFLC has been developed in robot operating system (ROS) using the C++ programming language. Based on recent advances in non-singleton inference, the centroid of the intersection of the input and antecedent FSs (Cen-NSFLC) is utilized to calculate the firing strength of each rule instead of the maximum of the intersection used in traditional NSFLC (Tra-NSFLC). An 8-shaped trajectory, consisting of straight and curved lines, is used for the real-time validation of the proposed controllers for a trajectory following problem. An accurate monocular keyframe-based visual-inertial simultaneous localization and mapping (SLAM) approach is used to estimate the position of the quadrotor UAV in GPS denied unknown environments. The performance of the Cen-NSFLC is compared with a conventional proportional integral derivative (PID) controller, a singleton FLC (SFLC) and a Tra-NSFLC. All controllers are evaluated for different flight speeds, thus introducing different levels of uncertainty into the control problem. Visual-inertial SLAM-based real time quadrotor UAV flight tests demonstrate that not only does the Cen-NSFLC achieve the best control performance among the four controllers, but it also shows better control performance when compared to their singleton counterparts. Considering the bias in the use of model based controllers, e.g. PID, for the control of UAVs, this paper advocates an alternative method, namely Cen-NSFLCs, in uncertain working environments