Jennifer Wilburn

Aircraft Failure Detection and Identification over an Extended Flight Envelope Using an Artificial Immune System

An integrated artificial immune system-based scheme that can operate over extended areas of the flight envelope is proposed in this paper for the detection and identification of a variety of aircraft sensor, actuator, propulsion, and structural failures/damages. A hierarchical multi-self strategy has been developed in which different self configurations are selected for detection and identification of specific abnormal conditions. Data collected using a motion-based flight simulator were used to define the self for a wide area of the flight envelope and to test and validate the scheme. The aircraft model represents a supersonic fighter, including model-following direct adaptive control laws based on non-linear dynamic inversion and artificial neural network augmentation. The proposed detection scheme achieves low false alarm rates and high detection and identification rates for all the categories of failures considered.

UAV Adaptive Control Laws Using Non-Linear Dynamic Inversion Augmented with an Immunity-based Mechanism

In this paper, a novel adaptive flight control system is presented, designed to handle failures and malfunctions of aircraft sub-systems as well as general environmental upset conditions. The proposed control laws use a non-linear dynamic inversion approach augmented with an artificial immune system mechanism that relies on a direct compensation inspired primarily by the biological immune system response. This work is an extension of a recently developed artificial immune system-based architecture which implements negative and positive selection algorithms for aircraft fault detection, identification, and evaluation within a hierarchical multi-self scheme. The effectiveness of the approach is demonstrated through simulation examples within the West Virginia University unmanned aerial vehicle simulation environment. The performance of the control laws is evaluated in terms of trajectory tracking errors and control activity during autonomous flight in the presence of atmospheric disturbances and actuator failures. The results show that the proposed fault tolerant adaptive control laws significantly improve the tracking performance of the vehicle at nominal conditions and under a variety of abnormal flight conditions.

Unmanned aerial vehicle trajectory tracking algorithm comparison

Purpose – The purpose of this paper is to analyze and compare the performance of several different UAV trajectory tracking algorithms in normal and abnormal flight conditions to investigate the fault‐tolerant capabilities of a novel immunity‐based adaptive mechanism.Design/methodology/approach – The evaluation of these algorithms is performed using the West Virginia University (WVU) UAV simulation environment. Three types of fixed‐parameter algorithms are considered as well as their adaptive versions obtained by adding an immunity‐based mechanism. The types of control laws investigated are: position proportional, integral, and derivative control, outer‐loop nonlinear dynamic inversion (NLDI), and extended NLDI. Actuator failures on the three channels and increased turbulence conditions are considered for several different flight paths. Specific and global performance metrics are defined based on trajectory tracking errors and control surface activity.Findings – The performance of all of the adaptive contr...

Extended Nonlinear Dynamic Inversion Control Laws for Unmanned Air Vehicles

This paper presents a novel configuration for guidance and tracking control laws for unmanned air vehicles (UAV) based on an extended nonlinear dynamic inversion (NLDI) approach. Current outer/inner loop architectures use dynamic inversion for the outer loop controller while using linear compensation-type control for inner loops. That design, although it performs well at nominal conditions, lacks robustness under certain upset flight conditions. The design proposed in this paper includes inner and outer loop control modules that both rely on the use of an NLDI control scheme. The main objective of the control laws is to minimize forward, lateral, and vertical distances with respect to a desired trajectory, and maintain stability and adequate performance in the presence of sub-system failures and upset environmental conditions. The implementation of this control laws scheme is illustrated through a simulation example using a mathematical model of the West Virginia University (WVU) YF-22 UAV. The performance of the control laws is evaluated during autonomous flight in terms of trajectory tracking errors and control activity at nominal and abnormal conditions including actuator and sensor failures and excessive turbulence. The results obtained with the WVU UAV simulation environment show that for all cases investigated the extended NLDI approach has desirable fault tolerant capabilities.

Implementation of Composite Clothoid Paths for Continuous Curvature Trajectory Generation for UAVs

This paper presents a complete methodology associated with the implementation of the 2dimensional clothoid path planner and trajectory generation algorithm for unmanned aerial vehicles (UAVs) applications. Based on previously suggested approaches for integrating the clothoid into the autonomous navigation and control architecture to produce a continuous curvature profile, the methodology presented in this paper has been extended to identify and provide a solution for the various issues encountered during a complete implementation of the clothoid planner for UAV trajectory generation. This includes a quadrant-based scheme for selecting the shortest path combination based upon the relative position and angle of the poses and a numerical solution of the nonlinear vector equations which define the ultimate path profile. The result is a ready-to-use planner that could easily be implemented on-board UAV hardware. The approach is demonstrated using the West Virginia University UAV Simulation Environment.

A modified genetic algorithm for UAV trajectory tracking control laws optimization

Purpose – The purpose of this paper is to gain trajectory-tracking controllers for autonomous aircraft are optimized using a modified evolutionary, or genetic algorithm (GA). Design/methodology/approach – The GA design utilizes real representation for the individual consisting of the collection of all controller gains subject to tuning. The initial population is generated randomly over pre-specified ranges. Alternatively, initial individuals are produced as random variations from a heuristically tuned set of gains to increase convergence time. A two-point crossover mechanism and a probabilistic mutation mechanism represent the genetic alterations performed on the population. The environment is represented by a performance index (PI) composed of a set of metrics based on tracking error and control activity in response to a commanded trajectory. Roulette-wheel selection with elitist strategy are implemented. A PI normalization scheme is also implemented to increase the speed of convergence. A flexible contr...

Implementation of a 3-Dimensional Dubins-Based UAV Path Generation Algorithm

This paper presents the implementation of a complete 3-D Dubins-based path planner, including non-coplanar and coplanar poses. Mathematical representation of a Dubins path in 3-dimensions has been extended into a complete planning methodology that provides a suitable solution to the implementation issues typically encountered for such a process. These include the numerical solution of the nonlinear implicit vector equations, as well as handling of non-coplanar and coplanar pose combinations. Since the suggested methodology breaks down for the case of coplanar poses, a complete planner is provided by diagnosing coplanar sets of poses, determining the solution within the plane containing them, and converting the 2-D solution to the 3-D environment.