Immunity – Based Aircraft Failure Detection and Identification Using an Integrated Hierarchical Multi-Self Strategy

This paper presents the development and application of an integrated artificial immune system-based scheme for the detection and identification of a wide variety of aircraft sensor, actuator, propulsion, and structural failures/damages. The proposed approach is based on a hierarchical multi-self strategy where different self configurations are selected for the identification of specific abnormal conditions. Data collected using a motion-based flight simulator was used to define the self for a sub-region of the flight envelope. The aircraft model represents a supersonic fighter, including model-following 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 the four categories of failures considered

Simulation Environment for the Development and Testing of Immunity-Based Aircraft Failure Detection Schemes

In this paper, a simulation environment is presented developed at West Virginia University (WVU) for the design and testing of integrated schemes for aircraft sub-system failure detection, identification, and evaluation based on the artificial immune system (AIS) paradigm. The simulation environment includes: a non-linear mathematical model of a supersonic military aircraft, implementation of a large variety of failures and damages of aircraft actuators, sensors, structure, and propulsion system, advanced computational tools for off-line AIS detector generation and optimization, a general framework for AIS-based detection schemes, interface with visualization software for desk-top computer simulation, interface with the WVU 6-degrees-of-freedom motion-based flight simulator, and a set of detailed interactive menus for design and simulation scenario setup.The use of the simulation environment is illustrated through an example of an AIS failure detection, identification, and evaluation scheme based on a hierarchical multi-self approach

Integrated Simulation Environment for Unmanned Autonomous Systems—Towards a Conceptual Framework

The paper initiates a comprehensive conceptual framework for an integrated simulation environment for unmanned autonomous systems (UAS) that is capable of supporting the design, analysis, testing, and evaluation from a “system of systems” perspective. The paper also investigates the current state of the art of modeling and performance assessment of UAS and their components and identifies directions for future developments. All the components of a comprehensive simulation environment focused on the testing and evaluation of UAS are identified and defined through detailed analysis of current and future required capabilities and performance. The generality and completeness of the simulation environment is ensured by including all operational domains, types of agents, external systems, missions, and interactions between components. The conceptual framework for the simulation environment is formulated with flexibility, modularity, generality, and portability as key objectives. The development of the conceptual framework for the UAS simulation reveals important aspects related to the mechanisms and interactions that determine specific UAS characteristics including complexity, adaptability, synergy, and high impact of artificial and human intelligence on system performance and effectiveness

Artificial Immune System – Based Aircraft Failure Evaluation over Extended Flight Envelope

This paper describes the design, development, and flight simulation testing of an artificial immune system-based approach for evaluation of different sensor, actuator, propulsion, and structural failures/damages. The evaluation is performed with the estimation of the magnitude/severity of the failure and the prediction of the achievable states leading to an overall assessment of the effects of the failure on reducing the flight envelope of the aircraft. A supersonic fighter model is used, which includes model-following adaptive control laws based on non-linear dynamic inversion and artificial neural network augmentation. 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 proposed approach. The results show the capabilities of the artificial immune system-based scheme to predict or estimate the reduction of the flight envelope in a general manner

Dendritic Cell Mechanism for Aircraft Immunity-based Failure Detection and Identification

The biological dendritic cells perform a complex activation/suppression role in the generation, direction, and control of antibodies. Their action relies on balancing information regarding the external antigen type, amount, and virulence, as well as the state and resources of the host organism. In this paper, an information processing algorithm inspired by the functionality of the dendritic cells is proposed to enhance aircraft subsystem abnormal condition detection and identification, within the artificial immune system paradigm. A hierarchical multi-self strategy is used to produce multiple failure detection and identification outcomes at each sample time over a time window. The artificial dendritic cell is defined as a computational unit that centralizes, fuses, and interprets this information to decide upon a unique detection and identification outcome with reduced false alarms and low number of incorrect identifications. A mathematical formulation of the concept and a detailed implementation algorithm are provided. The proposed methodology is demonstrated using simulation data for a supersonic fighter from a motion-based flight simulator

Development of Immunity-based Framework for Aircraft Abnormal Conditions Detection, Identification, Evaluation, and Accommodation

This paper presents the development of a biologically inspired generalized conceptual framework for the detection, identification, evaluation, and accommodation of aircraft sub-system abnormal conditions. The artificial immune system paradigm in conjunction with other artificial intelligence techniques, analytical tools, and heuristics are used in an attempt to provide a comprehensive solution to the problem of safely operating aircraft under abnormal flight conditions. The main concepts and foundations are established and methodologies and algorithms for implementation are outlined. The approach addresses directly the complexity and multi-dimensionality of aircraft dynamic response in the context of abnormal conditions and is expected to facilitate the design of on-board augmentation systems to increase aircraft survivability, improve operation safety, and optimize performance at both normal and abnormal/upset conditions

In-flight Actuator Failure Detection and Identification for a Reduced Size UAV Using the Artificial Immune System Approach

This paper presents the preliminary development of an actuator failure detection and identification scheme using the artificial immune system technique. The scheme is tested using in-flight data from a reduced size remotely controlled research aircraft equipped with a jet engine. The immunity-based detection is, in principle, similar to the process of self/nonself discrimination, through which the natural immune system recognizes extraneous agents. The process of defining the identifiers and developing the detection and identification scheme is presented. A combined method using positive and negative selection strategy is used to generate detectors. Different sets of flight data are used to design and test the scheme. The evaluation of the scheme is performed in terms of detection rate, number of false alarms, and detection time for normal conditions and upset conditions including one stabilator or aileron locked at trim position. The proposed detection scheme achieves good detection performance for all flight conditions considered. This approach proves promising for coping with the multidimensional characteristics of integrated/comprehensive detection of aircraft sub-system failures

A stochastically optimal feedforward and feedback technique for flight control systems of high performance aircrafts

This paper focuses on a detailed description of a control technique, which has been successfully used in several advanced flight control systems research projects over the past decade. The technique, called Stochastically Optimal Feedforward and Feedback Technique (SOFFT), directly descends from optimal control, and in particular from Explicit Model Following Control (EMFC). Unlike the most used model following techniques, in SOFFT the feedforward and feedback control laws are designed independently of one another. Moreover, this technique relies on different levels of plant models, specifically, a simple plant model is used for the synthesis of the feedback control law, and another plant model, together with a "command" model, are used in the synthesis of the feedforward control laws. It is important to notice that the controller in its final form is nonlinear in nature. This is because the matrices that compose the plant and command models are constantly updated as the aircraft moves throughout the flight envelope, and at least two Algebraic Riccati Equations (ARE) are solved in real time to compute the feedback and feedforward gains