715 research outputs found
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
Adaptive and Optimal Motion Control of Multi-UAV Systems
This thesis studies trajectory tracking and coordination control problems for single and multi unmanned aerial vehicle (UAV) systems. These control problems are addressed for both quadrotor and fixed-wing UAV cases. Despite the fact that the literature has some approaches for both problems, most of the previous studies have implementation challenges on real-time systems. In this thesis, we use a hierarchical modular approach where the high-level coordination and formation control tasks are separated from low-level individual UAV motion control tasks. This separation helps efficient and systematic optimal control synthesis robust to effects of nonlinearities, uncertainties and external disturbances at both levels, independently. The modular two-level control structure is convenient in extending single-UAV motion control design to coordination control of multi-UAV systems. Therefore, we examine single quadrotor UAV trajectory tracking problems to develop advanced controllers compensating effects of nonlinearities and uncertainties, and improving robustness and optimality for tracking performance. At fi rst, a novel adaptive linear quadratic tracking (ALQT) scheme is developed for stabilization and optimal attitude control of the quadrotor UAV system. In the implementation, the proposed scheme is integrated with Kalman based reliable attitude estimators, which compensate measurement noises. Next, in order to guarantee prescribed transient and steady-state tracking performances, we have designed a novel backstepping based adaptive controller that is robust to effects of underactuated dynamics, nonlinearities and model uncertainties, e.g., inertial and rotational drag uncertainties. The tracking performance is guaranteed to utilize a prescribed performance bound (PPB) based error transformation. In the coordination control of multi-UAV systems, following the two-level control structure, at high-level, we design a distributed hierarchical (leader-follower) 3D formation control scheme. Then, the low-level control design is based on the optimal and adaptive control designs performed for each quadrotor UAV separately. As particular approaches, we design an adaptive mixing controller (AMC) to improve robustness to varying parametric uncertainties and an adaptive linear quadratic controller (ALQC). Lastly, for planar motion, especially for constant altitude flight of fixed-wing UAVs, in 2D, a distributed hierarchical (leader-follower) formation control scheme at the high-level and a linear quadratic tracking (LQT) scheme at the low-level are developed for tracking and formation control problems of the fixed-wing UAV systems to examine the non-holonomic motion case. The proposed control methods are tested via simulations
and experiments on a multi-quadrotor UAV system testbed
Trajectory Generation and Tracking Control for Aggressive Tail-Sitter Flights
We address the theoretical and practical problems related to the trajectory
generation and tracking control of tail-sitter UAVs. Theoretically, we focus on
the differential flatness property with full exploitation of actual UAV
aerodynamic models, which lays a foundation for generating dynamically feasible
trajectory and achieving high-performance tracking control. We have found that
a tail-sitter is differentially flat with accurate aerodynamic models within
the entire flight envelope, by specifying coordinate flight condition and
choosing the vehicle position as the flat output. This fundamental property
allows us to fully exploit the high-fidelity aerodynamic models in the
trajectory planning and tracking control to achieve accurate tail-sitter
flights. Particularly, an optimization-based trajectory planner for
tail-sitters is proposed to design high-quality, smooth trajectories with
consideration of kinodynamic constraints, singularity-free constraints and
actuator saturation. The planned trajectory of flat output is transformed to
state trajectory in real-time with consideration of wind in environments. To
track the state trajectory, a global, singularity-free, and
minimally-parameterized on-manifold MPC is developed, which fully leverages the
accurate aerodynamic model to achieve high-accuracy trajectory tracking within
the whole flight envelope. The effectiveness of the proposed framework is
demonstrated through extensive real-world experiments in both indoor and
outdoor field tests, including agile SE(3) flight through consecutive narrow
windows requiring specific attitude and with speed up to 10m/s, typical
tail-sitter maneuvers (transition, level flight and loiter) with speed up to
20m/s, and extremely aggressive aerobatic maneuvers (Wingover, Loop, Vertical
Eight and Cuban Eight) with acceleration up to 2.5g
Direct Adaptive Control for a Trajectory Tracking UAV
This research focuses on the theoretical development and analysis of a direct adaptive control algorithm to enable a fixed-wing UAV to track reference trajectories while in the presence of persistent external disturbances. A typical application of this work is autonomous flight through urban environments, where reference trajectories would be provided by a path planning algorithm and the vehicle would be subjected to significant wind gust disturbances. Full 6-DOF nonlinear and linear UAV simulation models are developed and used to study the performance of the direct adaptive control system for various scenarios. A stability proof is developed to prove convergence of the direct adaptive control system under certain conditions. Specific adaptive controller implementation details are provided, including the use of a sensor blending algorithm to address the non-minimum phase properties of the UAV models. The robustness of the adaptive system pertaining to the amount of modeling error that can be accommodated by the controller is studied, and the disturbance rejection capabilities and limitations of the controllers are also analyzed. The overall results of this research demonstrate that the direct adaptive control algorithm can enable trajectory tracking in cases where there are both significant uncertainties in the external disturbances and considerable error in the UAV model
A Novel Degree of Freedom in Flapping Wings Shows Promise for a Dual Aerial/Aquatic Vehicle Propulsor
Ocean sampling for highly temporal phenomena, such as harmful algal blooms,
necessitates a vehicle capable of fast aerial travel interspersed with an
aquatic means of acquiring in-situ measurements. Vehicle platforms with this
capability have yet to be widely adopted by the oceanographic community.
Several animal examples successfully make this aerial/aquatic transition using
a flapping foil actuator, offering an existence proof for a viable vehicle
design. We discuss a preliminary realization of a flapping wing actuation
system for use in both air and water. The wing employs an active in-line motion
degree of freedom to generate the large force envelope necessary for propulsion
in both fluid media.Comment: Accepted version of paper for ICRA 2015, 8 pages, 9 figures;
Proceedings of IEEE International Conference on Robotics and Automation
(ICRA), pp. 5830 - 5837, Seattle WA, 201
Path Planning For Persistent Surveillance Applications Using Fixed-Wing Unmanned Aerial Vehicles
This thesis addresses coordinated path planning for fixed-wing Unmanned Aerial Vehicles
(UAVs) engaged in persistent surveillance missions. While uniquely suited to this mission,
fixed wing vehicles have maneuver constraints that can limit their performance in this role.
Current technology vehicles are capable of long duration flight with a minimal acoustic
footprint while carrying an array of cameras and sensors. Both military tactical and civilian
safety applications can benefit from this technology. We make three main contributions:
C1 A sequential path planner that generates a C2 flight plan to persistently acquire a
covering set of data over a user designated area of interest. The planner features the
following innovations:
• A path length abstraction that embeds kino-dynamic motion constraints to estimate feasible path length
• A Traveling Salesman-type planner to generate a covering set route based on the path length abstraction
• A smooth path generator that provides C2 routes that satisfy user specified curvature constraints
C2 A set of algorithms to coordinate multiple UAVs, including mission commencement
from arbitrary locations to the start of a coordinated mission and de-confliction of
paths to avoid collisions with other vehicles and fixed obstacles
iv
C3 A numerically robust toolbox of spline-based algorithms tailored for vehicle routing
validated through flight test experiments on multiple platforms. A variety of tests
and platforms are discussed.
The algorithms presented are based on a technical approach with approximately equal
emphasis on analysis, computation, dynamic simulation, and flight test experimentation.
Our planner (C1) directly takes into account vehicle maneuverability and agility constraints
that could otherwise render simple solutions infeasible. This is especially important when
surveillance objectives elevate the importance of optimized paths. Researchers have devel
oped a diverse range of solutions for persistent surveillance applications but few directly
address dynamic maneuver constraints.
The key feature of C1 is a two stage sequential solution that discretizes the problem so that
graph search techniques can be combined with parametric polynomial curve generation.
A method to abstract the kino-dynamics of the aerial platforms is then presented so that
a graph search solution can be adapted for this application. An A* Traveling Salesman
Problem (TSP) algorithm is developed to search the discretized space using the abstract
distance metric to acquire more data or avoid obstacles. Results of the graph search are
then transcribed into smooth paths based on vehicle maneuver constraints. A complete
solution for a single vehicle periodic tour of the area is developed using the results of the
graph search algorithm. To execute the mission, we present a simultaneous arrival algorithm
(C2) to coordinate execution by multiple vehicles to satisfy data refresh requirements and
to ensure there are no collisions at any of the path intersections.
We present a toolbox of spline-based algorithms (C3) to streamline the development of C2
continuous paths with numerical stability. These tools are applied to an aerial persistent
surveillance application to illustrate their utility. Comparisons with other parametric poly
nomial approaches are highlighted to underscore the benefits of the B-spline framework.
Performance limits with respect to feasibility constraints are documented
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