Browse Topic: Flight control systems
This article provides a comprehensive review of existing literature on AI-based functions and verification methods within vehicular systems. Initially, the introduction of these AI-based functions in these systems is outlined. Subsequently, the focus shifts to synthetic environments and their pivotal role in the verification process of AI-based vehicle functions. The algorithms used within the AI-based functions focus primarily on the paradigm of deep learning. We investigate the constituent components of these synthetic environments and the intricate relationships with vehicle systems in the verification and validation domain of the system. In the following, alternative approaches are discussed, serving as complementary methods for verification without direct involvement in synthetic environment development. These approaches include data-oriented methodologies employing statistical techniques and AI-centric strategies focusing solely on the core deep learning algorithm.
This SAE Aerospace Recommended Practice (ARP) provides an algorithm aimed to analyze flight control surface actuator movements with the objective to generate duty cycle data applicable to hydraulic actuator dynamic seals.
There are certain situations when landing an Advanced Air Mobility (AAM) aircraft is required to be performed without assistance from GPS data. For example, AAM aircraft flying in an urban environment with tall buildings and narrow canyons may affect the ability of the AAM aircraft to effectively use GPS to access a landing area. Incorporating a vision-based navigation method, NASA Ames has developed a novel Alternative Position, Navigation, and Timing (APNT) solution for AAM aircraft in environments where GPS is not available.
The intent of this AIR is twofold: (1) to present descriptive summary of aircraft nosewheel steering and centering systems, and (2) to provide a discussion of problems encountered and “lessons learned” by various airplane manufacturers and users. This document covers both military aircraft (land-based and ship-based) and commercial aircraft. It is intended that the document be continually updated as new aircraft and/or new “lessons learned” become available.
This SAE Aerospace Information Report (AIR) provides the hydraulic and flight-control system designer with the various design options and techniques that are currently available to enhance the survivability of military aircraft. The AIR addresses the following major topics: a Design concepts and architecture (see 3.2, 3.5, and 3.6) b Design implementation (see 3.3, 3.6, and 3.7) c Means to control external leakage (see 3.4) d Component design (see 3.8)
This Technical Governance is part of the SAE UCS Architecture Library and is primarily concerned with the UCS Architecture Model (AS6518) starting at Revision A and its user extensions. Users of the Model may extend it in accordance with AS6513 to meet the needs of their UCS Products. UCS Products include software components, software configurations and systems that provide or consume UCS services. For further information, refer to AS6513 Revision A or later. Technical Governance is part of the UCS Architecture Framework. This framework governs the UCS views expressed as Packages and Diagrams in the UCS Architecture Model.
This aerospace recommended practice provides a framework and suggested procedures or values for requirements for the design, performance, and test of hydraulically powered servoactuators for use in aircraft flight control systems. The original version of this document was intended for military usage: consequently, the requirements still often reflect such use. However, the basic requirements of this ARP may and should be applicable to commercial usage as well, provided that appropriate considerations are given for the applicable FAR/JAR 25 regulations, hydraulic fluids, and environmental conditions.
Over the past few decades, aircraft automation has progressively increased. Advances in digital computing during the 1980s eliminated the need for onboard flight engineers. Avionics systems, exemplified by FADEC for engine control and Fly-By-Wire, handle lower-level functions, reducing human error. This shift allows pilots to focus on higher-level tasks like navigation and decision-making, enhancing overall safety. Full automation and autonomous flight operations are a logical continuation of this trend. Thanks to aerospace pioneers, most functions for full autonomy are achievable with legacy technologies. Machine learning (ML), especially neural networks (NNs), will enable what Daedalean terms Situational Intelligence: the ability to understand and make sense of the current environment and situation but also anticipate and react to a future situation, including a future problem. By automating tasks traditionally limited to human pilots - like detecting airborne traffic and identifying
Advanced flight control system, aviation battery and motor technologies are driving the rapid development of eVTOL to offer possibilities for Urban Air Mobility. The safety and airworthiness of eVTOL aircraft and systems are the critical issues to be considered in eVTOL design process. Regarding to the flight control system, its complexity of design and interfaces with other airborne systems require detailed safety assessment through the development process. Based on SAE ARP4754A, a forward architecture design process with comprehensive safety assessment is introduced to achieve complete safety and hazard analysis. The new features of flight control system for eVTOL are described to start function capture and architecture design. Model-based system engineering method is applied to establish the functional architecture in a traceable way. SFHA and STPA methods are applied in a complementary way to identify the potential safety risk caused by failure and unsafe control action. PSSA with
The presence of a slung-load during the flight of a quadrotor generates swing effects that can greatly influence the dynamics of the quadrotor. These effects have the potential to threaten the stability of the system’s attitude. This study presents a disturbance compensation strategy that is designed based on the utilization of an adaptive harmonic extended state observer (AHESO) in order to solve this problem and achieve precise attitude control. To derive the aforementioned algorithm, a comprehensive mathematical model for the quadrotor-slung-load system is built. The periodic features of disturbance are derived by considering the movement of the slung-load. Subsequently, by taking the periodic features of the disturbances into account, the AHESO for accurate disturbance estimation is designed. In this observer, an online frequency estimator for the harmonic disturbances is introduced. Lyapunov theory is introduced to examine the stability of the AHESO. In addition, backstepping
Artificial intelligence (AI) has become prevalent in many fields in the modern world, ranging from vacuum cleaners to lawn mowers and commercial automobiles. These capabilities are continuing to evolve and become a part of more products and systems every day, with numerous potential benefits to humans. AI is of particular interest in autonomous vehicles (AVs), where the benefits include reduced cognitive workload, increased efficiency, and improved safety for human operators. Numerous investments from academia and industry have been made recently with the intent of improving the enabling technologies for AVs. Google and Tesla are two of the more well-known examples in industry, with Google developing a self-driving car and Tesla providing its Full Self-Driving (FSD) autopilot system. Ford and BMW are also working on their own AVs.
This AS covers subsonic and supersonic Mach meter instruments which, when connected to sources of static (Ps), and total (Pt), or impact (Pt-Ps), pressure provide indication of Mach number. These instruments are known as Type A. This AS also covers servo-operated repeater or digital display instruments which indicate Mach number when connected to the appropriate electrical output of a Mach transducer of Air Data Computer. These instruments are known as Type B.
This Aerospace Standard covers all automatic pressure altitude code generating equipment manufactured under this standard and complying with the requirements specified herein up to the maximum range of pressure altitude as indicated on the equipment nameplate. In those cases where the code generating equipment forms part of an aircraft system, such as a pressure altimeter, an air data computer or an ATC Transponder, this standard applies only to the code generating equipment as defined in paragraph 1.2.
This document provides recommended practices regarding how System Theoretic Process Analysis (STPA) may be applied to safety-critical systems in any industry.
This SAE Aerospace Information Report (AIR) has been compiled to provide information on hydraulic systems fitted to the following categories of military vehicles. Attack Airplanes Fighter Airplanes Bombers Anti-Sub, Fixed Wing Airplanes Transport Airplanes Helicopters Boats
This SAE Aerospace Information Report (AIR) provides descriptions of aircraft flight control actuation system failure-detection methods. The fault-detection methods are those used for ground and in-flight detection of failures in electrohydraulic actuation systems for primary flight controls.
This SAE Aerospace Recommended Practice (ARP) provides design guidelines for aircraft mechanical control systems and components. Topics contained in this document include design requirements, system design and installation guidelines, and component design practices for primary flight controls, secondary flight controls, and utility controls.
This document is applicable to commercial and military aircraft fuel quantity indication systems. It is intended to give guidance for system design and installation. It describes key areas to be considered in the design of a modern fuel system and builds upon experiences gained in the industry in the last 10 years.
This SAE Aerospace Standard (AS) covers the following basic types: Type I - Pitot pressure, straight and L-shaped, electrically heated. Type II - Pitot and static pressures, straight and L-shaped, electrically heated.
This SAE Aerospace Standard (AS) specifies minimum performance requirements for pressure altimeter systems other than air data computers. This document covers altimeter systems that measure and display altitude as a function of atmospheric pressure. The pressure transducer may be contained within the instrument display case or located remotely. Requirements for air data computers are specified in AS8002. Some requirements for nontransducing servoed altitude indicators are included in AS791. This document does not address RVSM requirements because general RVSM requirements cannot be independently detailed at the component level. The instrument system specified herein does not include aircraft pressure lines. Unless otherwise specified, whenever the term “instrument” is used, it is to be understood to be the complete system of pressure transducer components, any auxiliary equipment, and display components. The test procedures specified herein apply specifically to mechanical type
General Atomics Aeronautical Systems, Inc., Poway, CA 858-312-2810
In the field of space technology, Centre Suisse d’Electronique et de Microtechnique (CSEM) has been a partner of the European Space Agency (ESA) for many years. One focus of their joint research is eliminating vibration emissions originating from components aboard satellites. In addition to limiting the precision of attitude control for satellites, these micro-vibrations lead to higher energy consumption and (in the case of imaging missions) cause deterioration of image quality.
Automated Driving Systems (ADSs) for road vehicles will be capable of performing the entire Dynamic Driving Task (DDT) without the active involvement of a human driver. Further, many ADSs will use Machine Learning (ML) to progressively adapt their driving functionality during in-service operation. This presents challenges for traditional regulatory frameworks, which do not readily support automated driving without a human driver or support safety-critical systems using ML to modify driving functionality post-market entry. However, these challenges are not entirely unique to ADSs. A review was undertaken into approaches taken in other domains to assure safety-critical systems that enable automated operation and adaptive functionality. Other transport modes were reviewed, including adaptive flight control systems in aviation, autonomous ship control systems in maritime, and automated train operation in rail. Non-transport domains were also reviewed, including medical devices in
This SAE Aerospace Information Report (AIR) provides a review of real-time modeling methodologies for gas turbine engine performance. The application of real-time models and modeling methodologies are discussed. The modeling methodologies addressed in this AIR concentrate on the aerothermal portion of the gas turbine propulsion system. Characteristics of the models, the various algorithms used in them, and system integration issues are also reviewed. In addition, example cases of digital models in source code are provided for several methodologies.
This SAE Aerospace Information Report (AIR) provides data and general analysis methods for calculation of internal and external, pressurized and unpressurized airplane compartment pressures during rapid discharge of cabin pressure. References to the applicable current FAA and EASA rules and advisory material are provided. While rules and interpretations can be expected to evolve, numerous airplanes have been approved under current and past rules that will have a continuing need for analysis of production and field modifications, alterations and repairs. The data and basic principles provided by this report are adaptable to any compartment decompression analysis requirement.
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