488,048 research outputs found

    The NASA Lewis integrated propulsion and flight control simulator

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    A new flight simulation facility was developed at NASA-Lewis. The purpose of this flight simulator is to allow integrated propulsion control and flight control algorithm development and evaluation in real time. As a preliminary check of the simulator facility capabilities and correct integration of its components, the control design and physics models for a short take-off and vertical landing fighter aircraft model were shown, with their associated system integration and architecture, pilot vehicle interfaces, and display symbology. The initial testing and evaluation results show that this fixed based flight simulator can provide real time feedback and display of both airframe and propulsion variables for validation of integrated flight and propulsion control systems. Additionally, through the use of this flight simulator, various control design methodologies and cockpit mechanizations can be tested and evaluated in a real time environment

    GT2006-90165 GROUND TEST DATA VALIDATION USING A SUBSCALE F/A-22 ENGINE INLET EMPIRICAL MODEL

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    ABSTRACT The US Air Force's two main aeropropulsion test centers, Arnold Engineering Development Center and the Air Force Flight Test Center, are developing a common suite of modeling and simulation tools employing advanced predictive modeling technologies. These modeling and simulation tools incorporate real-time data validation, system identification, parameter estimation, model calibration, and automated model updating as new test results or operational data become available. The expected benefit is improved efficiency and accuracy for online diagnostic monitoring of Air Force assets. This paper describes the integrated approach to real-time data validation. Implementation of a software package to enable efficient model handoff between test groups and centers and extension of the capability to aeropropulsion models is discussed. An F/A-22 inlet model is used to demonstrate the approach. Compact polynomial function models of the distortion and recovery flow descriptors and 40-probe pressure values are derived from quasisteady and instantaneous subscale wind tunnel data. The total-pressure inlet distortion and recovery models are integrated in a real-time equipment health monitoring system designed to support test operations, and preliminary results are given. A companion paper describes the integrated approach to system identification, parameter estimation, and model updating

    Validating a neural network-based online adaptive system

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    Neural networks are popular models used for online adaptation to accommodate system faults and recuperate against environmental changes in real-time automation and control applications. However, the adaptivity limits the applicability of conventional verification and validation (V&V) techniques to such systems. We investigated the V&V of neural network-based online adaptive systems and developed a novel validation approach consisting of two important methods. (1) An independent novelty detector at the system input layer detects failure conditions and tracks abnormal events/data that may cause unstable learning behavior. (2) At the system output layer, we perform a validity check on the network predictions to validate its accommodation performance.;Our research focuses on the Intelligent Flight Control System (IFCS) for NASA F-15 aircraft as an example of online adaptive control application. We utilized Support Vector Data Description (SVDD), a one-class classifier to examine the data entering the adaptive component and detect potential failures. We developed a decompose and combine strategy to drastically reduce its computational cost, from O(n 3) down to O( n32 log n) such that the novelty detector becomes feasible in real-time.;We define a confidence measure, the validity index, to validate the predictions of the Dynamic Cell Structure (DCS) network in IFCS. The statistical information is collected during adaptation. The validity index is computed to reflect the trustworthiness associated with each neural network output. The computation of validity index in DCS is straightforward and efficient.;Through experimentation with IFCS, we demonstrate that: (1) the SVDD tool detects system failures accurately and provides validation inferences in a real-time manner; (2) the validity index effectively indicates poor fitting within regions characterized by sparse data and/or inadequate learning. The developed methods can be integrated with available online monitoring tools and further generalized to complete a promising validation framework for neural network based online adaptive systems

    Centralized MPC for Autonomous Intersection Crossing

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    This paper develops a method for a safe and autonomous intersection crossing. A centralized system controls autonomous vehicles within a certain surrounding of the intersection and generates optimized trajectories for all vehicles in the area. A recently proposed design approach, [10], where this problem is expressed as a convex optimization problem using space sampling instead of time sampling, is formulated as a MPC problem solved by a QP algorithms so that it can be executed in real time. The MPC controller is then integrated in CarMaker using Matlab/Simulink so that the controller can be validated against the advanced vehicle models and sensor models available in CarMaker. Preliminary results of this validation are presented. Also, a method is designed to obtain time gaps between the vehicles to prevent the optimization problem to become infeasible when sensors give noisy measurements

    Next Generation Hydro Software

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    A few years ago Deltares started a large multidisciplinary project named Next Generation Hydro Software. The main focus of the project is to improve, harmonize and integrate existing hydro software that has been developed throughout the years. Important technological innovations include development of the new computational core D-Flow Flexible Mesh, as well as the user-friendly, open modelling environment Delta Shell. The project involves more than 40 scientists and software engineers. The new integrated system will allow both water managers and modellers to do their work better and faster. The unique characteristic of the project is that it focuses on the possibility of setting up integrated models of the whole aquatic chain from the source to the sea, resulting in complex model configurations. The challenges further increase because of the involvement of experts from many different fields within the aforementioned aquatic chain. Furthermore, the project addresses the complete workflow of a modeller, including model setup, calibration and validation. For this purpose the system includes new scientific visualization, analysis and interactive modeling tools that enable users to improve their understanding of the modelled processes. Applications of the system show the successful integration of 0D (lumped hydrological models and real-time control rules), 1D (river flow and water quality models) and 2D/3D model components (river, estuary and coastal areas). In this paper some of the preliminary results of the project are demonstrated, as well as its current status and a preview of possible future developments

    Development and Validation of Nonlinear Models for Helicopter Dynamics

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    The need for validated nonlinear helicopter models and methods to validate these models directly is identified. Published validation methods for validating nonlinear dynamic models are reviewed and the need for an integrated approach is established. Sensitivity coefficient based validation techniques are investigated. Single value sensitivity coefficients are found to be useful for parameter and output variable selection. Examination of sensitivity coefficients time histories is found to be a useful addition to parametric validation methods. A model distortion technique is evaluated. The method is tested with simple systems and simulated data as well as a helicopter model and real ffight data. The method is discussed. Its application to helicopter dynamics is rejected because of noise problems. A nonlinear one degree of freedom yaw model for an Aerospatiale SA.330 PUMA helicopter is improved and validated using analogue matching and a parameter estimation method which uses a linear search. The importance of physical knowledge of the system being modelled is highlighted in the development of the model. A nonlinear mathematical model of a helicopter main rotor is validated in two specific areas. These are the lag damper and the engine/rotor speed model. The validation techniques used are maximum likelihood parameter estimation with sensitivity coefficient examination and analogue matching of time response data. The importance of good physical knowledge of the system being modelled is again indicated. The structure of the model in the identified areas is validated. The validation methods are brought together in a specification for an interactive inodel validation computer package. The benefits of an integrated approach are identified and the computer program is specified so as to take advantage of this. Through this package, the user will interact with the model, the available validation methods and the experimental data and will be able to develop and validate dynamic models easily and efficiently

    A Property Specification Pattern Catalog for Real-Time System Verification with UPPAAL

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    Context: The goal of specification pattern catalogs for real-time requirements is to mask the complexity of specifying such requirements in a timed temporal logic for verification. For this purpose, they provide frontends to express and translate pattern-based natural language requirements to formulae in a suitable logic. However, the widely used real-time model checking tool UPPAAL only supports a restricted subset of those formulae that focus only on basic and non-nested reachability, safety, and liveness properties. This restriction renders many specification patterns inapplicable. As a workaround, timed observer automata need to be constructed manually to express sophisticated requirements envisioned by these patterns. Objective: In this work, we fill these gaps by providing a comprehensive specification pattern catalog for UPPAAL. The catalog supports qualitative and real-time requirements and covers all corresponding patterns of existing catalogs. Method: The catalog we propose is integrated with UPPAAL. It supports the specification of qualitative and real-time requirements using patterns and provides an automated generator that translates these requirements to observer automata and TCTL formulae. The resulting artifacts are used for verifying systems in UPPAAL. Thus, our catalog enables an automated end-to-end verification process for UPPAAL based on property specification patterns and observer automata. Results: We evaluate our catalog on three UPPAAL system models reported in the literature and mostly applied in an industrial setting. As a result, not only the reproducibility of the related UPPAAL models was possible, but also the validation of an automated, seamless, and accurate pattern- and observer-based verification process. Conclusion: The proposed property specification pattern catalog for UPPAAL enables practitioners to specify qualitative and real-time requirements...Comment: Accepted Manuscrip

    BIOLOGICAL INSPIRED INTRUSION PREVENTION AND SELF-HEALING SYSTEM FOR CRITICAL SERVICES NETWORK

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    With the explosive development of the critical services network systems and Internet, the need for networks security systems have become even critical with the enlargement of information technology in everyday life. Intrusion Prevention System (IPS) provides an in-line mechanism focus on identifying and blocking malicious network activity in real time. This thesis presents new intrusion prevention and self-healing system (SH) for critical services network security. The design features of the proposed system are inspired by the human immune system, integrated with pattern recognition nonlinear classification algorithm and machine learning. Firstly, the current intrusions preventions systems, biological innate and adaptive immune systems, autonomic computing and self-healing mechanisms are studied and analyzed. The importance of intrusion prevention system recommends that artificial immune systems (AIS) should incorporate abstraction models from innate, adaptive immune system, pattern recognition, machine learning and self-healing mechanisms to present autonomous IPS system with fast and high accurate detection and prevention performance and survivability for critical services network system. Secondly, specification language, system design, mathematical and computational models for IPS and SH system are established, which are based upon nonlinear classification, prevention predictability trust, analysis, self-adaptation and self-healing algorithms. Finally, the validation of the system carried out by simulation tests, measuring, benchmarking and comparative studies. New benchmarking metrics for detection capabilities, prevention predictability trust and self-healing reliability are introduced as contributions for the IPS and SH system measuring and validation. Using the software system, design theories, AIS features, new nonlinear classification algorithm, and self-healing system show how the use of presented systems can ensure safety for critical services networks and heal the damage caused by intrusion. This autonomous system improves the performance of the current intrusion prevention system and carries on system continuity by using self-healing mechanism

    Multi-Model Specifications and their application to Classification Systems

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    Many safety-critical systems are required to have their correctness validated prior to deployment. Such validation is typically performed using models of the run-time behaviour that the system is expected to exhibit and experience during run-time. However, these systems may be subject to different requirements under different circumstances; also, there may be multiple stakeholders involved, each with a somewhat different perspective on correctness. We examine the use of a multi-model framework based on assumptions (Pre and Rely conditions) and obligations (Post and Guarantee conditions) to represent the workload and resource related needs of complex AI system components such as DNN classifiers. We identify three kinds of multi-models that are of particular interest: Independent, Integrated and Hierarchical. All the individual models comprising an independent multi-model must remain valid at all times during run-time; at least one of the models comprising an integrated multi-model must always be valid. With hierarchical multi-models all models are initially valid but the component's behaviour may gracefully degrade through a series of models with successively weaker assumptions and commitments (we show that Mixed-Criticality Systems, widely studied in the real-time computing community, are particularly well-suited for representation via hierarchical multi-models). We explain how this modelling framework is intended to be used, and present algorithms for determining the worst-case timing behaviour of systems that are specified using multi-models
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