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Companies are gradually developing: 1) complex and/or highly integrated systems including vehicles (as satellites, airplanes, cars, etc.) or equipment (as computers, cell phones, no breaks, etc.) to use under 2) increasingly varied or inhospitable environments, and to survive under 3) increasingly long life cycles and unavoidable changes in staff & facilities & technologies. The overall decision to use (by time, cost, quality, of functions, services, etc.) such end systems under 2 require 4) high Dependability (Reliability, Maintainability, Availability, Correction, Safety, Security, etc.) of them. The overall survival in use (by health monitoring, housekeeping, retrofit, upgrade, etc.) of such end systems under 3 require 5) high Suportability (Maintainability, Adaptability, Availability, Robustness, etc.) of them coupled with the support systems.

One challenge that the space, aeronautical and automotive industries are facing today is the fast growing number of vehicles versus the slowly growing number of useful orbits, routes, and speedways. Furthermore, the adoption of “free-flight”, “speed-drive”, etc. policies in the near future will only aggravate it. All these factors increase the risk of collisions and the frequency of deviation maneuvers to avoid them. But they also create the opportunity to devise policies to mitigate such problems, including algorithms to propagate the uncertainties in vehicle motions and to predict the risk of their collisions. This work discusses the development and simulation of an algorithm for the propagation of navigation uncertainties in the trajectory of aerospace vehicles, to minimize the risk of collisions. The scenario of Satellites Formation Flying shall be used for the simulations, with focus on the prediction of the collision probability.

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

This work presents the distributed simulation of the longitudinal mode of an aircraft by using the DoD High Level Architecture (HLA). The HLA is a general-purpose architecture for simulation reuse and interoperability. This architecture was developed under the leadership of the Defense Modeling and Simulation Office (DMSO) to support reuse and interoperability across the large numbers of different types of simulations developed and maintained by the DoD. To do this, the transfer function of the longitudinal mode of a hypothetical aircraft was implemented by means of a SystemBuild/MATRIXx model. The output of this model was connected to a Run-Time Infrastructure (RTI) and monitored on a remote computer. The connection between the model and the RTI was implemented by using a wrapper which was developed in C++. The HLA RTI implementation used in this work was the poRTIco.

This work presents the analysis, design and simulation of the reconfigurable control architecture for the contingency mode of the MultiMission Platform (MMP). The MMP is a generic service module currently under design at INPE. Its control system can be switched among nine main Modes of Operation and other Sub-Modes, according to ground command or information coming from the control system, mainly alarms. The implementation followed the specifications when they were found, otherwise it was designed. They cover operations from detumbling after launcher separation and solar acquisition, to achieving payload nominal attitude and orbital corrections maneuvers. The manager block of the control system was implemented as a finite state machine. The tests are based in simulations with the MatriX/SystemBuild software. They focused mainly on the worst cases that the satellite is supposed to endure in its mission, be it during modes or transitions between modes and submodes.

Complex systems such as satellites, aircrafts, automobiles and air traffic controls are becoming increasingly complex and highly integrated as prescribed by the SAE ARP 4754 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 reliability, to be reached by a diversity of approaches, processes, components, etc. By their turn, the processes of analysis and decision making shall be improved progressively, as experience accumulates and suggests modifications and adaptations. According to this philosophy, in this work, we discuss a proposal for improving the results of the Reliability Analysis and FMEA/FMECA of the CBERS Satellite Program, conducted at the National Institute for Space Research-INPE, since 1987.

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

This paper presents some techniques for fault diagnosis in aerospace and automotive systems. A diagnosis technique is an algorithm to detect and isolate fault components in a dynamic process, such as sensor biases, actuator malfunctions, leaks and equipment deterioration. Fault diagnosis is the first step to achieve fault tolerance, but the redundancy has to be included in the system. This redundancy can be either by hardware or software. In situations in which it is not possible to use hardware redundancy only the analytical redundancy approach can be used to design fault diagnosis systems. Methods based on analytical redundancy need no extra hardware, since they are based on mathematical models of the system.

The increasing use of Global Navigation Satellite Systems-GNSS in future Aeronautical Navigation Systems-ANS is a current trend in the aeronautical operation and regulation communities. This trend implies the adoption of elements and interactions of a degree of complexity that is still being discussed around the world. Faced with that, we believe that a requirements based approach is an effective tool to deal with such highly complex and integrated systems. In this work we discuss a requirements based approach to future Aeronautical Navigation Systems based on Global Navigation Satellite Systems. To do that, we first briefly present the concept of Communication, Navigation, Surveillance/Air Traffic Management - CNS/ATM, and the current and potential benefits of the adoption of its paradigms.

Systems such as satellites, aircrafts, automobiles and air traffic controls are becoming increasingly complex and highly integrated, as prescribed by the SAE ARP 4754 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 reliability, to be reached by a diversity of approaches, processes, components, etc. By their turn, the processes of analysis and decision making shall be improved progressively, as experience accumulates and suggests modifications. Most of this can be translated in models. According to this philosophy, in this work, we discuss the use of Model Based Reliability for improving the results of the Reliability Analysis and FMEA/FMECA of a satellite program, as those conducted at the National Institute for Space Research-INPE, since 1979.

Current systems such as satellites, aircrafts, automobiles, turbines, power controls and traffic controls are becoming increasingly complex and/or highly integrated as prescribed by the SAE-ARP-4754a Standard. Such systems operate in a real time distributed environment which frequently requires a common knowledge of time among different devices, levels and granularities. So, temporal correctness is mostly needed, besides logical correctness. It can be achieved by hardware clocks and devices, software clocks and algorithms, or both, to avoid or tolerate, within appropriate margins, the time faults or failures that may occur in aerospace and automotive systems. This paper presents an overview of clock synchronization algorithms and their uses in aerospace and automotive systems. It is based on a review of the literature, discussion and comparison of some clock synchronization algorithms with different policies.

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.

Modeling and Simulation (M&S) of dynamic systems based on computers is a multidisciplinary field that involves several knowledge areas and tools, and is broadly used in all development areas of space industry such as rocket and satellite design and construction. Once space systems are divided into several subsystems for ease of engineering, their models are divided the same way for the same reason. Such models may be done using different computational tools that are based on either physical flows, informational flows, or hybrid flows, depending on the subsystem nature. This is specially true for a satellite propulsion subsystem, and its physical (volume, mass, energy, enthalpy, entropy, linear momentum, etc.) flows. This paper presents the modeling and simulation of a satellite propulsion subsystem by physical and signal flows. To accomplish this task, two different computational tools were used: AMESim and MatLab.

In this work we discuss the fault avoidance and the fault tolerance approaches for increasing the reliability of aerospace and automotive systems. This includes: the basic definitions/concepts (reliability, maintainability, availability, redundancy, etc.), and characteristics (a priori analysis, a posteriori analysis, physical/hardware redundancy, analytical/software redundancy, etc.) of both approaches, their mathematical background and models (exponential, Weilbull, etc.), their basic theory, their methods and techniques (fault trees, dependence diagrams, Markov chains, etc.), some of their standards (SAE-ARP4761, AC 25.1309, etc.) and simulation environments (Cafta, etc.), and their applications to the reliability analysis and reliability improvement of aerospace and automotive vehicles. This is illustrated by some examples driven from the aerospace and automotive industries.

With the growing complexity and integration of systems as satellites, automobiles, aircrafts, turbines, power controls and traffic controls, as prescribed by SAE-ARP-4754A Standard, the time de-synchronization can cause serious or even catastrophic failures. Time synchronization is a very important aspect to achieve high performance, reliability and determinism in networked control systems. Such systems operate in a real time distributed environment which frequently requires a consistent time view among different devices, levels and granularities. So, to guarantee high performance, reliability and determinism it is required a performance evaluation of time synchronization of the overall system. This time synchronization performance evaluation can be done in different ways, as experiments and/or model and simulation.