Estimating body-fixed frame velocity and attitude from inertial measurements for a quadrotor vehicle

© 2014 IEEE. A key requirement for effective control of quadrotor vehicles is estimation of both attitude and linear velocity. Recent work has demonstrated that it is possible to measure horizontal velocities of a quadrotor vehicle from strap-down ac-celerometers along with a system model. In this paper we extend this to full body-fixed-frame velocity measurement by exploiting recent work in aerodynamic modeling of rotor performance and measurements of mechanical power supplied to the rotor hub. We use these measurements in a combined attitude and velocity nonlinear observer design to jointly estimate attitude and body-fixed-frame linear velocity. Almost global asymptotic stability of the resulting system is demonstrated using Lyapunov analysis of the resulting error system. In the current work, we ignore bias and leave it for future work. The performance of the observer is verified by simulation results

Constructive Equivariant Observer Design for Inertial Velocity-Aided Attitude

Inertial Velocity-Aided Attitude (VAA) is an important problem in the control of Remotely Piloted Aerial Systems (RPAS), and involves estimating the velocity and attitude of a vehicle using gyroscope, accelerometer, and inertial-frame velocity (e.g. GPS velocity) measurements. Existing solutions tend to be complex and provide limited stability guarantees, relying on either high gain designs or assuming constant acceleration of the vehicle. This paper proposes a novel observer for inertial VAA that exploits Lie group symmetries of the system dynamics, and shows that the observer is synchronous with the system trajectories. This is achieved by adding a virtual state of only three dimensions, in contrast to the larger virtual states typically used in the literature. The error dynamics of the observer are shown to be almost globally asymptotically and locally exponentially stable. Finally, the observer is verified in simulation, where it is shown that the estimation error converges to zero even with an extremely poor initial condition.Comment: 11 pages, 2 figures, submitted to NOLCOS 202

Discrete-time Stable Geometric Controller and Observer Designs for Unmanned Vehicles

In the first part of this dissertation, we consider tracking control of underactuated systems on the tangent bundle of the six-dimensional Lie group of rigid body motions, SE(3). We formulate both asymptotically and finite-time stable tracking control schemes for underactuated rigid bodies that have one translational and three rotational degrees of freedom actuated, in discrete time. Rigorous stability analyses of the tracking control schemes presented here guarantee the nonlinear stability of these schemes. The proposed schemes here are developed in discrete time as it is more convenient for onboard computer implementation and ensures stability irrespective of the sampling period. A stable convergence of translational and rotational tracking errors to the desired trajectory is guaranteed for both asymptotically and finite-time stable schemes. In the second part, a nonlinear finite-time stable attitude estimation scheme for a rigid body that does not require knowledge of the dynamics is developed. The proposed scheme estimates the attitude and constant angular velocity bias vector from a minimum of two known linearly independent vectors for attitude, and biased angular velocity measurements made in the body-fixed frame. The constant bias in angular velocity measurements is also estimated. The estimation scheme is proven to be almost globally finite-time stable in the absence of measurement errors using a Lyapunov analysis. In addition, the behavior of this estimation scheme is compared with three state-of-the-art filters for attitude estimation, and the comparison results are presented

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

Neural Network Output Feedback Control of a Quadrotor UAV

A neural network (NN) based output feedback controller for a quadrotor unmanned aerial vehicle (UAV) is proposed. The NNs are utilized in the observer and for generating virtual and actual control inputs, respectively, where the NNs learn the nonlinear dynamics of the UAV online including uncertain nonlinear terms like aerodynamic friction and blade flapping. It is shown using Lyapunov theory that the position, orientation, and velocity tracking errors, the virtual control and observer estimation errors, and the NN weight estimation errors for each NN are all semi-globally uniformly ultimately bounded (SGUUB) in the presence of bounded disturbances and NN functional reconstruction errors while simultaneously relaxing the separation principle