Disturbance rejection flight control for small fixed-wing unmanned aerial vehicles

Disturbance rejection flight control for small fixed-wing unmanned aerial vehicle

Optimization-based safety analysis of obstacle avoidance systems for unmanned aerial vehicles

The integration of Unmanned Aerial Vehicles (UAVs) in airspace requires new methods to certify collision avoidance systems. This paper presents a safety clearance process for obstacle avoidance systems, where worst case analysis is performed using simulation based optimization in the presence of all possible parameter variations. The clearance criterion for the UAV obstacle avoidance system is defined as the minimum distance from the aircraft to the obstacle during the collision avoidance maneuver. Local and global optimization based verification processes are developed to automatically search the worst combinations of the parameters and the worst-case distance between the UAV and an obstacle under all possible variations and uncertainties. Based on a 6 Degree of Freedom (6DoF) kinematic and dynamic model of a UAV, the path planning and collision avoidance algorithms are developed in 3D space. The artificial potential field method is chosen as a path planning and obstacle avoidance candidate technique for verification study as it is a simple and widely used method. Different optimization algorithms are applied and compared in terms of the reliability and efficiency

Piecewise constant model predictive control for autonomous helicopters

This paper introduces an optimisation based control framework for autonomous helicopters. The framework contains a high-level model predictive control (MPC) and a low-level linear controller. The proposed MPC works in a piecewise constant fashion to reduce the computation burden and to increase the time available for performing online optimisation. The linear feedback controller responds to fast dynamics of the helicopter and compensates the low bandwidth of the high-level controller. This configuration allows the computationally intensive algorithm applied on systems with fast dynamics. The stability issues of the high-level MPC and the overall control scheme are discussed. Simulations and flight tests on a small-scale helicopter are carried out to verify the proposed control scheme

Disturbance observer based control for gust alleviation of a small fixed-wing UAS

This paper outlines a method of applying a linear disturbance observer to a small Unmanned Aerial System (UAS)to reduce the influence of unpredictable gusts on the non-linear aircraft dynamics. This work aims to show that by using a linear state-space model it is possible to estimate external disturbances and use the available control surfaces to alleviate the influence of gusts on aircraft dynamics accordingly. This paper focuses on the longitudinal channel of a small UAS to demonstrate the strategy. A baseline Linear Quadratic Regulator with Integral Action (LQI) is first developed; the disturbance observer based control strategy is then patched into this baseline controller to demonstrate the performance improvement. Simulations are conducted using the designed linear observer to alleviate various disturbance sources on a fully non-linear simulation of the UAS. This aims to demonstrate performance of an observer in a realistic situation where uncertainties between the linear observer and non-linear plant to be controlled are present

Information based mobile sensor planning for source term estimation of a non-continuous atmospheric release

This paper presents a method to estimate the original location and the mass of an instantaneous release of hazardous material into the atmosphere. It is formulated as an inverse problem, where concentration observations from a mobile sensor are fused with meteorological information and a Gaussian puff dispersion model to characterise the source. Bayes’ theorem is used to estimate the parameters of the release taking into account the uncertainty that exists in the dispersion parameters and meteorological variables. An information based reward is used to guide an unmanned aerial vehicle equipped with a chemical sensor to the expected most informative measurement locations. Simulation results compare the performance between a single mobile sensor with various amounts of static sensors

The effect of droplet ejection frequency on inkjet-etched micro via holes

Inkjet etching has been identified as a potential route to formation of micro via holes in polymer dielectrics. Such vias could facilitate three-dimensional integration and sequential build-up fabrication in printed electronics. In the research reported in this paper, ethanol droplets were jetted onto a poly(4-vinyl phenol) (also known as PVP or PVPh) layer at different frequencies in order to observe the effect of droplet ejection frequency on the diameters of the via holes produced. The results demonstrate that via holes remain the same diameter at a low drop ejection frequency, while they enlarge at a relatively high frequency. A mechanism for this behaviour is proposed for which high speed photography provides evidence

Boustrophedon coverage path planning for UAV aerial surveys in wind

© 2017 IEEE. In the quickly developing world of precision agriculture UAV remote sensing, there is a need for a greater understanding of winds effect on fixed wing aerial surveying, as this is missing from current literature. This paper presents a method to define and calculate flight times in a Boustrophedon aerial survey coverage path in wind, for a given convex polygon, at a given sweep angle. It is shown that there exists no easy way to define a sweep angle relative to the wind that minimises flight time. This method is validated by comparing the numerical simulated path and times with a number of surveys run in the high fidelity X-Plane simulator

Situation awareness for UAV operating in terminal areas using bearing-only observations and circuit flight rules

Situation awareness is required for an Unmanned Aerial Vehicle (UAV) when it makes an arrival at an uncontrolled airfield. Since no air traffic control service is available, the UAV needs to detect and track other traffic aircraft by using its onboard sensors. General aviation pilots obtain enough situation awareness to operate in these environments, only using their vision and radio messages heard from other traffic aircraft. To improve the target tracking performance of a UAV, the circuit flight rules and standard radio messages are incorporated to provide extra knowledge about the target behaviour. This is achieved by using the multiple models to describe the target motions in different flight phases and characterising the phase transition in a stochastic manner. Consequently, an interacting multiple model particle filter with state-dependent transition probabilities is developed to perform Bayesian filtering with bearing-only observations from a vision sensor

An auxiliary particle filtering algorithm with inequality constraints

For nonlinear non-Gaussian stochastic dynamic systems with inequality state constraints, this paper presents an efficient particle filtering algorithm, constrained auxiliary particle filtering algorithm. To deal with the state constraints, the proposed algorithm probabilistically selects particles such that those particles far away from the feasible area are less likely to propagate into the next time step. To improve on the sampling efficiency in the presence of inequality constraints, it uses a highly effective method to perform a series of constrained optimization so that the importance distributions are constructed efficiently based on the state constraints. The caused approximation errors are corrected using the importance sampling method. This ensures that the obtained particles constitute a representative sample of the true posterior distribution. A simulation study on vehicle tracking is used to illustrate the proposed approach

Particle filtering with soft state constraints for target tracking

In practice, additional knowledge about the target to be tracked, other than its fundamental dynamics, can often be modelled as a set of soft constraints and utilised in a filtering process to improve the tracking performance. This paper develops a general approach to the modelling of soft inequality constraints, and investigates particle filtering with soft state constraints for target tracking. We develop two particle filtering algorithms with soft inequality constraints, i.e. a sequential-importanceresampling particle filter and an auxiliary sampling mechanism. The latter probabilistically selects the candidate particles from the soft inequality constraints of the state variables so that they are more likely to comply with the soft constraints. The performances of the proposed algorithms are evaluated using Monte Carlo simulations in a target tracking scenario