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Current systems such as satellites, aircrafts, automobiles, turbines, wind power generators and traffic controls are becoming increasingly complex and/or highly integrated as prescribed by the SAE-ARP-4754 Standard. Such systems frequently require accurate generation, distribution and time or phase synchronization of signals with different frequencies that may be based on one reference signal and frequency. But the environment fluctuations or the non-linear dynamics of these operations cause uncertainties (skew and jitter) in the phase or time of the reference signal and its derived signals. So, techniques to reduce those causes or their effects are becoming important aspects to consider in the design of such systems. The PLL techniques are useful for establishing coherent phase or time references, jitter reduction, skew suppression, frequency synthesis, and clock recovery in numerous systems such as communication, wireless systems, digital circuits, rotors, and others.

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.

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.

On several engineering applications high Reliability is one of the most wanted features. The aspects of Reliability play a key role in design projects of aircraft, spacecraft, automotive, medical, bank systems, and so, avoiding loss of life, property, or costly recalls. The highly reliable systems are designed to work continuously, even upon external threats and internal Failures. Very convenient is the fact that the term 'Failure' may have its meaning tailored to the context of interesting, as its general definition refers to it as "any deviation from the specified behavior of a system". The above-mentioned 'deviation' may refer to: performance degradation, operational misbehavior, deviation of environmental qualification levels, Safety hazards, etc. Nevertheless, Reliability is not the only requirement for a modern system. Other features as Availability, Integrity, Security and Safety are always part of the same technical specification, in a same level of importance.

Life-critical aeronautical projects are increasing their lifecycle up to the point where a product is developed to be available in the market for more than 70 years, but requiring daily service support and replacement parts. Therefore, unavailability of components or services can have a severe impact over the product through its lifecycle. In this work we study some causes of unavailability of components and services and their effects over the lifecycle of an aeronautical project, to propose recommendations, alternatives and actions to be taken in the early phases of project development to help mitigate such effects over the product lifecycle. To do that, we initially present the causes of unavailability of components and services. Then, we discuss some of their effects over the lifecycle of an aeronautical project.

This paper presents an analytical and simulation study of the stabilization and improvement of the active vibration damping of a system modeled by a simple analog harmonic oscillator driven by discrete time control. Initially, this control is the Bilinear (or Tustin) s-z mapping equivalent of a continuous-time asymptotically stable Proportional plus Derivative (PD) control. It is tested with high values of the sampling period. It is shown that all classical mappings (Tustin, Schneider, etc.) tested may instabilize the system. To circumvent this, we propose and use a new (ST1) mapping that behaves better than the classical ones tested under the same conditions. We also model an active discrete control of a suspension of a vehicle, and compare the performance between the PD controllers designed by Bilinear and by the new (ST1) S-Z mappings, for this example.

In this work, still under development, we study the stability degradation due to delays in a networked control system. Our networked system is composed by: 1) a computer with Rate Monotonic Scheduler policy and, 2) a communication network based on TDMA access. Under this scenario, we analyze an integrated communication-computing delay and define the worst delay. The simulations shows that the presence of a worst delay can be determined only with an extensive analysis. The simulations were done in Matlab/Simulink with the help of Truetime toolbox.

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

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 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

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.

The realization of modern systems subjected to automatic control, such as aircraft, automobiles, satellites, rocket launchers, cargo and military ships, and so forth; increasingly assume, within its very set of requirements, the task of providing better dependability, i.e.: safety, reliability, and availability altogether. Towards this demand, fault-tolerant control greatly meets such growing demand of dependability, by its ability of recognizing the occurrence of potentially hazardous/hazardous faults within the overall (closed-loop) system, and by taking remedial action whenever necessary/mandatory. The process of fault tolerance can be segregated into two fundamental steps: (1) that of fault diagnosis, comprising fault detection-isolation-identification, and, (2) control adjustment/reconfiguration. This paper focuses on the second step, of control adjustment/reconfiguration.

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.

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.

Control systems that can switch between control or plant modes have the advantage of being simpler to design than an equivalent system with a single mode. However, the transition between these modes can introduce steps or overshootings in the state variables, and this can degrade the performance or even damage the system. This is can be of extreme importance in fields such as aerospace and automobilistic, as the switching between manual and autopilot modes or the switching of gears In this work, we will use integral criteria in original ways, to determine a coefficient on the system which should optimize the trajectory of the control signal, during the switching between two modes. Effectively, each transition will be done by a subsystem specific for it, according to the selected criterion. The simulations will be made in MATRIXx, MatLab or both, using models chosen from aerospace or automobilistic fields.

A major trend in modern aerospace and automotive systems is to integrate computing, communication and control into different levels of the vehicle and/or its supervision. A well fitted architecture adopted by this trend is the Common Bus Network Architecture. A Networked Control System (NCS) is called when the control loop is closed through a communication network. The presence of this communication network introduces new characteristics (sharing bus, delays, jitter etc.) to be considered at design time of a control system. This work focuses on the influences of data bus protocols on an aircraft Fly-By-Wire (FBW) networked control system. We intent to show, through simulations, the influences of sharing bus on a real time control system. To compare effects, we choose the CAN Bus protocol where the medium access control is event driven; and the TTP protocol where the medium access control is time driven.

This work presents the identification of the longitudinal mode of an aircraft by using time and frequency response methods. To do this, the transfer function was identified based on the sampled response to a step input. The transfer function was validated comparing the model step response with the original system step response. The identification of the system transfer function was performed by using the Fast Fourier Transform (FFT) and Bode Graphs methods. The model validation quantification was performed by means of the mean quadratic-error method applied to the step response difference. Based on that, the identified model was considered to be quite representative, thus proving the suitability of the applied methods.

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.

This works presents the generation and customization of real time code for embedded controllers using a modeling and simulation environment. When the controller model is considered satisfactory, the developers can use a code generation tool to build a real time source code capable to be migrated to an embedded target processor. The code generation tool used is capable to generate real time code in ANSI C or ADA 95 languages. This process can be customized to adequate to a target processor and/or a Real Time Operating System (RTOS). The code customization can be achieved using a specific Template Programming Language (TPL) that specifies how the code will be generated. This technique makes it possible the instantiation of real time embedded controllers code using the same controller model to a wide variety of target processors and/or RTOSs.

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.