W. Geri

Nonlinear actuator fault detection and isolation for a general aviation aircraft

Abstract This paper addresses the problem of detection and isolation of actuator faults for a general aviation aircraft, where the nonlinear simulation aircraft model embeds the effects of wind gusts, atmospheric turbulence and measurement errors. The approach is based on the recently introduced NonLinear Geometric Approach (NLGA) to the Fault Detection and Isolation (FDI) problem. The actuators FDI is performed in a coordinated turn, hence with coupled longitudinal and lateral–directional dynamics. This work represents the first complete NLGA FDI scheme for a general aviation aircraft in a condition with tight–coupled dynamics. The results obtained in the simulation of the faulty behaviour of a nonlinear Piper PA30 aircraft are finally reported.

Design and analysis of robust fault diagnosis schemes for a simulated aircraft model

Several procedures for sensor fault detection and isolation (FDI) applied to a simulated model of a commercial aircraft are presented. The main contributions of the paper are related to the design and the optimisation of two FDI schemes based on a linear polynomial method (PM) and the nonlinear geometric approach (NLGA). The FDI strategies are applied to the aircraft model, characterised by tight-coupled longitudinal and lateral dynamics. The robustness and the reliability properties of the residual generators related to the considered FDI techniques are investigated and verified by simulating a general aircraft reference trajectory. Extensive simulations exploiting the Monte Carlo analysis tool are also used for assessing the overall performance capabilities of the developed FDI schemes, in the presence of turbulence, measurement, and model errors. Comparisons with other disturbance-decoupling methods for FDI based on neural networks (NNs) and unknown input kalman filter (UIKF) are finally reported.

Residual Generator Computation for Fault Detection of a General Aviation Aircraft

Abstract This paper addresses the problem of the detection and isolation of actuator faults on a general aviation aircraft, characterised by a nonlinear model, in the presence of wind gust disturbances. In particular, this work investigates the design of residual generators in order to realise complete diagnosis schemes when additive faults are present. The use of a canonical input-output polynomial description for the linearised model of the aircraft allows to compute in a straightforward way minimal order residual generators. These tools lead to dynamic filters that can guarantee both disturbance signal decoupling and robustness properties with respect to linearisation errors. The results obtained in the simulation of the faulty behaviour of a Piper PA30 are finally reported.

Aircraft Guidance Law Analysis

Abstract This paper proposes an alternative formulation of the guidance problem, particularly suitable in autonomous aircraft applications. The Lyapunovs direct method is adopted as main tool to prove some properties of the considered guidance law. The aim of this work is designing a new feedback implemented algorithm that asymptotically stabilizes a generic aircraft to the imposed reference bearing in a local earth reference frame, instead of the velocity vector stabilization towards a reference way-point.

Application of fault diagnosis methodologies to a general aviation aircraft

Abstract This paper addresses the problem of the detection and isolation of input sensor faults on a general aviation aircraft, characterised by a nonlinear model, in the presence of wind gust disturbance and measurement errors. In particular, this work studies and compares different residual generator designs in order to realise complete diagnosis schemes, when additive faults are present. These different methods comprise linear and nonlinear filters, neural networks and unknown input Kalman filters, that can achieve disturbance signal de-coupling and robustness properties with respect to modelling error and sensor measurement noise. The results obtained in the simulation of the faulty behaviour of a PIPER PA30 longitudinal aircraft model are finally reported.

Fault Diagnosis Strategies for a Simulated Nonlinear Aircraft Model

Abstract In this work, different procedures for sensor Fault Detection and Isolation (FDI) applied to a simulated model of a commercial aircraft are presented. The main point of the paper regards the design of two FDI schemes based on a linear Polynomial Method (PM) and the NonLinear Geometric Approach (NLGA). The obtained results highlight a good trade–off between solution complexity and achieved performances. The FDI schemes are applied to the aircraft model, characterised by tight–coupled longitudinal and lateral dynamics. The properties of the residual generators are experimentally investigated and verified by simulating a general aircraft reference trajectory. The overall performance of the developed FDI schemes are analysed in the presence of turbulence, measurement and model errors. Comparisons with other FDI methods based on Neural Networks (NN) and Unknown Input Kalman Filter (UIKF) are finally reported.

Residual Generator Design and Performance Evaluation for Aircraft Simulated Model FDI

In this work, a model-based procedure exploiting analytical redundancy for the FDI of faults on a multivariable dynamic process is presented. The main point of the paper consists of exploiting a disturbance decoupling scheme in connection with a dynamic filter design procedure for diagnostic purpose. It is shown that the suggested approach to fault diagnosis is in particular advantageous in terms of solution complexity and performance. Moreover, the presented method is especially useful when robust solutions are considered for minimising the effects of modelling errors and noise, while maximising fault sensitivity. In order to verify the robustness of the solution achieved, the proposed design has been experimented with the data of a simulated aircraft model in the presence of both measurement and modelling errors. Finally, extensive simulations of the test-bed process and Monte Carlo analysis are the tools used for assessing the overall capabilities of the developed FDI scheme, when compared also with different data-driven diagnosis methods.

DEIS UAV: GNC Design and Avionics Implementation Issues

Abstract The School of Aerospace Engineering of the Forli Branch of the University of Bologna is currently developing an Unmanned Aerial Vehicle (UAV) which, thanks to a partially autonomous flight capability, aims to be a very low cost and compact flying platform capable of performing significant flight tests of general GNC algorithms or mission oriented experiments. The development activity includes the project, the synthesis and the assembly of the on-board avionics equipment, together with the software implementation for both data collection and some basic GNC algorithms. A demonstrative application will be the test of some integrity monitoring functions and algorithms using data collected during some flights.

AIRCRAFT GUIDANCE LAW WITH TRAJECTORY CONSTRAINTS

Abstract This paper presents two new algorithms with guidance purposes for autonomous aircraft applications. The adopted approach to the guidance problem is based on a reference bearing instead of the classical way–point. The new guidance laws are an extension of an earlier nonlinear algorithm and are designed in the framework of variable structure systems. The aim of the new guidance laws is to improve the aircraft trajectory during wide-angle turns, if the motion is restricted to a plane, by constraining the radius of curvature.

Design and performance analysis of residual generators for the FDI of aircraft model sensors

Abstract In this work, a model–based procedure exploiting analytical redundancy for the detection and isolation of faults on a multivariable linear dynamic process is presented. The main point of the paper consists of exploiting a disturbance ***decoupling scheme in connection with dynamic filter design procedure for diagnostic purpose. It is shown that the suggested approach to fault diagnosis is in particular advantageous in terms of solution complexity and performance. Moreover, the presented method is especially useful when robust solutions are considered for minimising the effects of modelling errors and noise, while maximising fault sensitivity. In order to verify the robustness of the solution achieved, the proposed design has been experimented with the data of a simulated aircraft model in the presence of both measurement and modelling errors. Finally, extensive simulations of the test–bed process and Monte Carlo analysis are the tools used for assessing the overall capabilities of the developed FDI scheme, when compared also with different data–driven diagnosis methods.