Integrated reconfigurable control and guidance based on evaluation of degraded performance

The present paper is focused on analysing an integrated reconfigurable control and guidance approach for recovering a small fixed-wing UAV from different actuator faults, which cover locked in place (stuck) and loss of effectiveness. The model of the UAV Aerosonde is used to develop a reconfigurable control system based on the control allocation technique for a variety of faults, such as locked-in-place control surfaces. It is shown through simulation that the developed technique is successful to recover the aircraft from various faults but cannot guarantee success on the planned mission. For mission scenarios where performance degradation is such that the prescribed trajectory cannot be achieved, a reconfigurable guidance system is developed, which is capable of adapting parameters such as the minimum turning radius and the look-ahead distance for obstacle avoidance, to allow the vehicle to dynamically generate a path which guides the aircraft around the no-fly zones taking into account the post-fault reduced performance. Path following is performed by means of a non-linear lateral guidance law and a collision avoidance algorithm is implemented as well. Finally, the integration of control reconfiguration and guidance adaptation is carried out to maximise probabilities of post-failure success in the mission. A methodology is developed, using an error based control allocation parameter, as a measure of performance degradation, which links both reconfiguration and guidance systems. The developed method, although approximate, is proven to be an efficient way of allocating the required degree of reconfiguration in guidance commands when an accurate prediction of the actual performance is not available

Integrated approaches to handle UAV actuator fault

Unmanned AerialVehicles (UAV) has historically shown to be unreliable when compared to their manned counterparts. Part of the reason is they may not be able to a ord the redundancies required to handle faults from system or cost constraints. This research explores instances when actuator fault handling may be improved with integrated approaches for small UAVs which have limited actuator redundancy. The research started with examining the possibility of handling the case where no actuator redundancy remains post fault. Two fault recovery schemes, combing control allocation and hardware means, for a Quad Rotor UAV with no redundancy upon fault event are developed to enable safe emergency landing. Inspired by the integrated approach, a proposed integrated actuator control scheme is developed, and shown to reduce the magnitude of the error dynamics when input saturation faults occur. Geometrical insights to the proposed actuator scheme are obtained. Simulations using an Aerosonde UAV model with the proposed scheme showed significant improvements to the fault tolerant stuck fault range and improved guidance tracking performance. While much research literature has previously been focused on the controller to handle actuator faults, fault tolerant guidance schemes may also be utilized to accommodate the fault. One possible advantage of using fault tolerant guidance is that it may consider the fault degradation e ects on the overall mission. A fault tolerant guidance reconfiguration method is developed for a path following mission. The method provides an additional degree of freedom in design, which allows more flexibility to the designer to meet mission requirements. This research has provided fresh insights into the handling UAV extremal actuator faults through integrated approaches. The impact of this work is to expand on the possibilities a practitioner may have for improving the fault handling capabilities of a UAV

Control Surface Fault Diagnosis with Specified Detection Probability - Real Event Experiences

Diagnosis of actuator faults is crucial for aircraft since loss of actuation can have catastrophic consequences. For autonomous aircraft the steps necessary to achieve fault tolerance is limited when only basic and non-redundant sensor and actuators suites are present. Through diagnosis that exploits analytical redundancies it is, nevertheless, possible to cheaply enhance the level of safety. This paper presents a method for diagnosing control surface faults by using basic sensors and hardware available on an autonomous aircraft. The capability of fault diagnosis is demonstrated obtaining desired levels of false alarms and detection probabilities. Self-tuning residual generators are employed for diagnosis and are combined with statistical change detection to form a setup for robust fault diagnosis. On-line estimation of test statistics is used to obtain a detection threshold and a desired false alarm probability. A data based method is used to determine the validity of the methods proposed. Verification is achieved using real data and shows that the presented diagnosis method is efficient and could have avoided incidents where faults led to loss of aircraft.(c) 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works

A Framework for Diagnosis of Critical Faults in Unmanned Aerial Vehicles

Unmanned Aerial Vehicles (UAVs) need a large degree of tolerance towards faults. If not diagnosed and handled in time, many types of faults can have catastrophic consequences if they occur during flight. Prognosis of faults is also valuable and so is the ability to distinguish the severity of the different faults in terms of both consequences and the frequency with which they appear. In this paper flight data from a fleet of UAVs is analysed with respect to certain faults and their frequency of appearance. Data is taken from a group of UAV's of the same type but with small differences in weight and handling due to different types of payloads and engines used. Categories of critical faults, that could and have caused UAV crashes are analysed and requirements to diagnosis are formulated. Faults in air system sensors and in control surfaces are given special attention. In a stochastic framework, and based on a large number of data logged during flights, diagnostic methods are employed to diagnose faults and the performance of these fault detectors are evaluated against flight data. The paper demonstrates a significant potential for reducing the risk of unplanned loss of remotely piloted vehicles used by the Danish Navy for target practice.This is the authors' accepted and refereed manuscript to the article. Author's post-print must be released with a Creative Commons Attribution Non-Commercial No Derivatives License

Intelligent Control Agent for Autonomous UAS

A self reconfiguring autopilot system is presented, which is based on a rational agent framework that integrates decision making with abstractions of sensing and actions for next generation unmanned aerial vehicles. The objective of the new intelligent control system is to provide advanced capabilities of self-tuning control for a new UAS airframe or adaptation for an old UAS in the presence of failures in adverse flight conditions. High-level system performance is achieved through on-board dynamical monitoring and estimation associated with controller switching and tuning by the agent. The agent can handle an untuned autopilot or retune the autopilot when dynamical changes occur due to aerodynamic and on-board system changes. The system integrates dynamical modelling, hybrid adaptive control, model validation, flight condition diagnosis, control performance evaluation through software agent development. An important feature of the agent is its abstractions from real-time measurements and also its abstractions from model based on-board simulation. The agent, while tuning and supervising the autopilot, also performs real-time evaluations on the effects of its actions

Observer-Based Optimal Control of a Quadplane with Active Wind Disturbance and Actuator Fault Rejection

Hybrid aircraft configurations with combined cruise and vertical flight capabilities are increasingly being considered for unmanned aircraft and urban air mobility missions. To ensure the safety and autonomy of such missions, control challenges including fault tolerance and windy conditions must be addressed. This paper presents an observer-based optimal control approach for the active combined fault and wind disturbance rejection, with application to a quadplane unmanned aerial vehicle. The quadplane model is linearised for the longitudinal plane, vertical takeoff and landing and transition modes. Wind gusts are modelled using a Dryden turbulence model. An unknown input observer is first developed for the estimation of wind disturbance by defining an auxiliary variable that emulates body referenced accelerations. The approach is then extended to simultaneous rejection of intermittent elevator faults and wind disturbance velocities. Estimation error is mathematically proven to converge to zero, assuming a piecewise constant disturbance. A numerical simulation analysis demonstrates that for a typical quadplane flight profile at 100 m altitude, the observer-based wind gust and fault correction significantly enhances trajectory tracking accuracy compared to a linear quadratic regulator and to a H-infinity controller, which are both taken, without loss of generality, as benchmark controllers to be enhanced. This is done by adding wind and fault compensation terms to the controller with admissible control effort. The proposed observer is also shown to enhance accuracy and observer-based rejection of disturbances and faults compared to three alternative observers, based on output error integration, acceleration feedback and a sliding mode observer, respectively. The proposed approach is particularly efficient for the active rejection of actuator faults under windy conditions.</p

Simultaneous Actuator and Sensor Faults Estimation for Aircraft Using a Jump-Markov Regularized Particle Filter

International audienceThe advances in aircraft autonomy have led to an increased demand for robust sensor and actuator fault detection and estimation methods in challenging situations including the onset of ambiguous faults. In this paper, we consider potential simultaneous fault on sensors and actuators of an Unmanned Aerial Vehicle. The faults are estimated using a Jump-Markov Regularized Particle Filter. The jump Markov decision process is used within a regularized particle filter structure to drive a small subset of particles to test the likelihood of the alternate hypothesis to the current fault mode. A prior distribution of the fault is updated using innovations based on predicted control and measurements. Fault scenarios were focused on cases when the impacts of the actuator and sensor faults are similar. Monte Carlo simulations illustrate the ability of the approach to discriminate between the two types of faults and to accurately and rapidly estimate them. The states are also accurately estimated

Diagnosis of Wing Icing Through Lift and Drag Coefficient Change Detection for Small Unmanned Aircraft

This paper address the issue of structural change, caused by ice accretion, on UAVs by utilising a Neyman Pearson (NP) based statistical change detection approach, for the identi cation of structural changes of xed wing UAV airfoils. A structural analysis is performed on the nonlinear aircraft system and residuals are generated, where a generalised likelihood ratio test is applied to detect faults. Numerical simulations demonstrate a robust detection with adequate balance between false alarm rate and sensitivity.© 2015 Published by Elsevier Ltd. This is the authors' accepted and refereed manuscript to the article. Locked until 2017-01-01

Nonlinear Estimation of Sensor Faults With Unknown Dynamics for a Fixed Wing Unmanned Aerial Vehicle

International audienceIn this paper, the estimation of additive inertial navigation sensor faults with unknown dynamics is considered with application to the longitudinal navigation and control of a fixed wing unmanned aerial vehicle. The faulty measurement is on the pitch angle. A jump Markov regularized particle filter is proposed for fault and state estimation of the nonlinear aircraft dynamics, with a Markovian jump strategy to manage the probabilistic transitions between the fault free and faulty modes. The jump strategy uses a small number of sentinel particles to continue testing the alternate hypothesis under both fault free and faulty modes. The proposed filter is shown to outperform the regularized particle filter for this application in terms of fault estimation accuracy and convergence time for scenarios involving both abrupt and incipient faults, without prior knowledge of the fault models. The state estimation is also more accurate and robust to faults using the proposed approach. The root-mean-square error for the altitude is reduced by 77 % using the jump Markov regularized particle filter under a pitch sensor fault amplitude of up to 10 degrees. Performance enhancement compared to the regularized particle filter was found to be more pronounced when fault amplitudes increase