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Currently, the use of Global Navigation Satellite Systems-GNSS has been widely disseminated for the most different applications, from the aeronautical navigation to the car traffic, being the Global Positioning System-GPS the most used system for such objectives. New applications have presented challenges in terms of the main requirements associated to such systems, namely: precision, reliability, availability, continuity and integrity. It is because proposed solutions, such as satellite or ground-based augmentation systems, depend on signals provided by the GNSS satellite constellation. It constitutes a limitation for using such systems for position and velocity estimations. On other hand, Inertial Navigation Systems-INS, being independent of external signals, have a big potential to be applied on these circumstances; furthermore, they present characteristics that may be considered complementary to the GNSS.

Systems such as satellites, aircrafts, automobiles and air traffic controls are becoming increasingly complex and/or highly integrated, as prescribed by the standard SAE-ARP 4754A Standard. They integrate many technologies and they work in very demanding environments, sometimes with little or no maintenance, due to the severe conditions of operation. To survive such harsh operating conditions, they require very high levels of dependability, to be reached by a diversity of approaches, processes, components, etc. Some are suggested by the SAE-ARP-4754A as one of the highest level standards to be met. So, it is important to know it and its consequences for product and staff deeply. The aim of this paper is to present: a discussion on the standard SAE-ARP-4754A and a proposal for using it in product certification and qualification of staff.

In this work, the problem of fault detection, isolation, and reconfiguration (FDIR) for Networked-Control Systems (NCS) of aerospace vehicles is discussed. The concept of fault-tolerance is introduced from a generic structure, and a review on quantitative and qualitative methods (state estimation, parameter estimation, parity space, statistic testing, neural networks, etc.) for FDIR is then performed. Afterwards, the use of networks as loop-closing elements is introduced, followed by a discussion on advantages (flexibility, energy demand, etc.) and challenges (networks effects on performance, closed-loop fault-effects on safety, etc.) represented thereby. Finally, examples of applications on aerospace vehicles illustrate the importance of the discussion herein exposed.

In this work we discuss some types of simulation architectures and standards, their characteristics and applications to the simulation and control of aerospace vehicles. This includes: the basic definitions, types and characteristics of simulators and simulations (physical, computational, hybrid, etc.; discrete events, discrete time, continuous time, etc; deterministic, stochastic, etc.) their basic compromise (simplicity x fidelity), their man-machine interfaces and interactions (virtual, constructive, live, etc.), their evolution law (time, events, mixed, etc.), their architectures (“stand-alone”, PIL, HIL, MIL, DIS, HLA, etc.), their standards (OMBA, SIMNET, ALSP, DIS, HLA 1.3, HLA 1516, ASIA, AP2633, etc.) and their applications to the simulation and control of aerospace vehicles. This is illustrated by some examples driven from the aerospace industry

In this work we discuss some types of simulation environments and laboratories, their characteristics and applications to the simulation and control of aerospace vehicles. This includes: the basic definitions, types and characteristics of simulators and simulations (physical, computational, hybrid, etc.; discrete events, discrete time, continuous time, etc; deterministic, stochastic, etc.) their basic compromise (simplicity × fidelity), their man-machine interfaces and interactions (virtual, constructive, live, etc.), their evolution law (time, events, mixed, etc.), their architectures (“stand-alone”, PIL, HIL, MIL, DIS, HLA, etc.), and especially, their environments (discrete, continuous, hybrid, etc.) and laboratories (physical, computational, hybrid, etc.), and their applications to the simulation and control of aerospace vehicles. This is illustrated by some examples driven from the aerospace industry.

In this work we discuss some types of simulators and simulations, their characteristics and applications to the simulation and control of aerospace vehicles. This includes: the basic definitions, types and characteristics of simulators and simulations (physical, computational, hybrid, etc.; discrete events, discrete time, continuous time, etc; deterministic, stochastic, etc.) their basic compromise (simplicity × fidelity), their man-machine interfaces and interactions (virtual, constructive, live, etc.), their evolution law (time, events, mixed, etc.), their architectures (“standalone”, PIL, HIL, MIL, DIS, HLA, etc.), their environments (discrete, continuous, hybrid, etc.) and their applications to the simulation and control of aerospace vehicles. This is illustrated by some examples driven from the aerospace industry

The measure of time intervals with relatively high accuracy (of 1 milisecond, at least) in PC computers is a relatively hard task to solve. But this is essential for the digital simulation, with sensors in the loop, of fast control systems. This work allows the reading of the programmable internal timer 8253 present in a typical PC, reaching 1 ms resolution, at least, through a C high level language routine. The determination of the angular velocity of a 53M2-30H Contraves 3-axis dynamic simulator used in that simulation was improved by the use of this work, allowing the acquisition of consecutive measures of angles and angular velocities with a time interval smaller than 10 ms in some cases. Using this routine and other simulator control and monitoring softwares we estimated the angular velocity faster (100 ms × 210 ms)and better than the simulator Rate Readout Module, and used it in a fast real time control simulation.