Fault detection, identification and accommodation techniques for unmanned airborne vehicles

Unmanned Airborne Vehicles (UAV) are assuming prominent roles in both the commercial and military aerospace industries. The promise of reduced costs and reduced risk to human life is one of their major attractions, however these low-cost systems are yet to gain acceptance as a safe alternate to manned solutions. The absence of a thinking, observing, reacting and decision making pilot reduces the UAVs capability of managing adverse situations such as faults and failures. This paper presents a review of techniques that can be used to track the system health onboard a UAV. The review is based on a year long literature review aimed at identifying approaches suitable for combating the low reliability and high attrition rates of today’s UAV. This research primarily focuses on real-time, onboard implementations for generating accurate estimations of aircraft health for fault accommodation and mission management (change of mission objectives due to deterioration in aircraft health). The major task of such systems is the process of detection, identification and accommodation of faults and failures (FDIA). A number of approaches exist, of which model-based techniques show particular promise. Model-based approaches use analytical redundancy to generate residuals for the aircraft parameters that can be used to indicate the occurrence of a fault or failure. Actions such as switching between redundant components or modifying control laws can then be taken to accommodate the fault. The paper further describes recent work in evaluating neural-network approaches to sensor failure detection and identification (SFDI). The results of simulations with a variety of sensor failures, based on a Matlab non-linear aircraft model are presented and discussed. Suggestions for improvements are made based on the limitations of this neural network approach with the aim of including a broader range of failures, while still maintaining an accurate model in the presence of these failures

Distributed Fault Detection in Formation of Multi-Agent Systems with Attack Impact Analysis

Autonomous Underwater Vehicles (AUVs) are capable of performing a variety of deepwater marine applications as in multiple mobile robots and cooperative robot reconnaissance. Due to the environment that AUVs operate in, fault detection and isolation as well as the formation control of AUVs are more challenging than other Multi-Agent Systems (MASs). In this thesis, two main challenges are tackled. We first investigate the formation control and fault accommodation algorithms for AUVs in presence of abnormal events such as faults and communication attacks in any of the team members. These undesirable events can prevent the entire team to achieve a safe, reliable, and efficient performance while executing underwater mission tasks. For instance, AUVs may face unexpected actuator/sensor faults and the communication between AUVs can be compromised, and consequently make the entire multi-agent system vulnerable to cyber-attacks. Moreover, a possible deception attack on network system may have a negative impact on the environment and more importantly the national security. Furthermore, there are certain requirements for speed, position or depth of the AUV team. For this reason, we propose a distributed fault detection scheme that is able to detect and isolate faults in AUVs while maintaining their formation under security constraints. The effects of faults and communication attacks with a control theoretical perspective will be studied. Another contribution of this thesis is to study a state estimation problem for a linear dynamical system in presence of a Bias Injection Attack (BIA). For this purpose, a Kalman Filter (KF) is used, where we show that the impact of an attack can be analyzed as the solution of a quadratically constrained problem for which the exact solution can be found efficiently. We also introduce a lower bound for the attack impact in terms of the number of compromised actuators and a combination of sensors and actuators. The theoretical findings are accompanied by simulation results and numerical can study examples

Self-Healing Control Framework Against Actuator Fault of Single-Rotor Unmanned Helicopters

Unmanned helicopters (UHs) develop quickly because of their ability to hover and low speed flight. Facing different work conditions, UHs require the ability to safely operate under both external environment constraints, such as obstacles, and their own dynamic limits, especially after faults occurrence. To guarantee the postfault UH system safety and maximum ability, a self‐healing control (SHC) framework is presented in this chapter which is composed of fault detection and diagnosis (FDD), fault‐tolerant control (FTC), trajectory (re‐)planning, and evaluation strategy. More specifically, actuator faults and saturation constraints are considered at the same time. Because of the existence of actuator constraints, usable actuator efficiency would be reduced after actuator fault occurrence. Thus, the performance of the postfault UH system should be evaluated to judge whether the original trajectory and reference is reachable, and the SHC would plan a new trajectory to guarantee the safety of the postfault system under environment constraints. At last, the effectiveness of proposed SHC framework is illustrated by numerical simulations

Model-based fault diagnosis for aerospace systems: a survey

http://pig.sagepub.com/content/early/2012/01/06/0954410011421717International audienceThis survey of model-based fault diagnosis focuses on those methods that are applicable to aerospace systems. To highlight the characteristics of aerospace models, generic nonlinear dynamical modeling from flight mechanics is recalled and a unifying representation of sensor and actuator faults is presented. An extensive bibliographical review supports a description of the key points of fault detection methods that rely on analytical redundancy. The approaches that best suit the constraints of the field are emphasized and recommendations for future developments in in-flight fault diagnosis are provided

Actuator Fault Reconstruction via Dynamic Neural Networks for an Autonomous Underwater Vehicle Model

This paper proposes the development of a scheme for the fault diagnosis of the actuators of a simulated model accurately representing the behaviour of an autonomous underwater vehicle. The Fossen model usually adopted to describe the dynamics of the underwater vehicle has been generalised in this paper to take into account time-varying sea currents. The proposed fault detection and isolation strategy uses a data-driven approach relying on multi-layer perceptron neural networks that include auto-regressive exogenous prototypes that provide the fault reconstruction. These tools are thus exploited to design a bank of dynamic neural networks for residual generation that are trained on the basis of the input and outputmeasurements acquired from the simulator. In this work, the residuals are designed to represent the reconstruction of the fault signals themselves. Moreover, the neural network bank is also able to perform the isolation task, in case of simultaneous and concurrent faults affecting the actuators. The paper firstly describes the steps performed for deriving the proposed fault diagnosis solution. Secondly, the effectiveness of the scheme is demonstrated by means of high-fidelity simulations of a realistic autonomous underwater vehicle, in the presence of faults and marine current

Distributed adaptive fault-tolerant leader-following formation control of nonlinear uncertain second-order multi-agent systems

This paper presents a distributed integrated fault diagnosis and accommodation scheme for leader‐following formation control of a class of nonlinear uncertain second‐order multi‐agent systems. The fault model under consideration includes both process and actuator faults, which may evolve abruptly or incipiently. The time‐varying leader communicates with a small subset of follower agents, and each follower agent communicates to its directly connected neighbors through a bidirectional network with possibly asymmetric weights. A local fault diagnosis and accommodation component are designed for each agent in the distributed system, which consists of a fault detection and isolation module and a reconfigurable controller module comprised of a baseline controller and two adaptive fault‐tolerant controllers, activated after fault detection and after fault isolation, respectively. By using appropriately the designed Lyapunov functions, the closed‐loop stability and asymptotic convergence properties of the leader‐follower formation are rigorously established under different modes of the fault‐tolerant control system