La Tesis Doctoral estudia la observaci'on en tiempo real de la concentraci'on en el colector de escape de 'oxidos de nitr'ogeno (NOx) y del dosado en motores diesel sobrealimentados (? '1 ). Para ello se combinan dos fuentes de informaci'on diferentes: ? Sensores capaces de proporcionar una media de dichas variables, ? y modelos orientados a control que estiman estas variables a partir de otras medidas del motor. El trabajo parte de la evaluaci'on de la precisi'on de los sensores, realizada mediante la comparaci'on de su medida con la proporcionada por equipos anal'?ticos de alta precisi'on, que son usados como est'andares de calibraci'on est'atica. Tambi'en se desarrollan en la Tesis m'etodos para la calibraci'on de la din'amica del sensor; dichos m'etodos permiten identi?car un modelo de comportamiento del sensor y revelar su velocidad de respuesta. En general, estos sensores demuestran ser precisos pero relativamente lentos. Por otra parte, se proponen modelos r'apidos para la estimaci'on de NOx y ? '1 . Estos m'etodos, basados en relaciones f'?sicas, tablas de par'ametros y una serie de correcciones, emplean las medidas proporcionadas por otros sensores con el ?n de proporcionar una estimaci'on de las variables de inter'es. Los modelos permiten una estimaci'on muy r'apida, pero resultan afectados por efectos de deriva que comprometen su precisi'on. Con el ?n de aprovechar las caracter'?sticas din'amicas del modelo y mantener la precisi'on en estado estacionario del sensor, se proponen t'ecnicas de fusi'on de la informaci'on basadas en la aplicaci'on de ?ltros de Kalman (KF). En primer lugar, se dise?na un KF capaz de combinar ambas fuentes de informaci'on y corregir en tiempo real el sesgo entre las dos se?nales. Posteriormente, se estudia la adaptaci'on en tiempo real de los par'ametros del modelo con el ?n de corregir de forma autom'atica los problemas de deriva asociados al uso de modelos. Todos los m'etodos y procedimientos desarrollados a lo largo de la presente Tesis Doctoral se han aplicado de forma experimental a la estimaci'on de NOx y ? '1 . De forma adicional, la Tesis Doctoral desarrolla aspectos relativos a la transferencia de estos m'etodos a los motores de serie.

Modelling and Observation of Exhaust Gas Concentrations for Diesel Engine Control

To achieve rapid and flexible vertical profile exploration of deep-sea hybrid structures, a multi-joint autonomous underwater vehicle (MJ-AUV) with orthogonal joints was designed. This paper focuses on the 3-dimensional (3D) modeling and attitude control of the designed vehicle. Considering the situation of gravity and buoyancy imbalance, a 3D model of the MJ-AUV was established according to Newton’s second law and torque balance principle. And then the numerical simulation was carried out to verify the credibility of the model. To solve the problems that the pitch and yaw attitude of the MJ-AUV are coupled and the disturbance is unknown, a linear quadratic regulator (LQR) decoupling control method based on a linear extended state observer (LESO) was proposed. The system was decoupled into pitch and yaw subsystems, treated the internal forces and external disturbances of each subsystem as total disturbances, and estimated the total disturbances with LESO. The control law was divided into two parts. The first part was the total disturbance compensator, while the second part was the linear state feedback controller. The simulation results show that the overshoot of the controlled system in the dynamic process is nearly 0 rad, reaching the design value very smoothly. Moreover, when the controlled system is in a stable state, the control precision is within 0.005%.

3-Dimensional Modeling and Attitude Control of Multi-Joint Autonomous Underwater Vehicles

The requirement for humans to be in control of machines i.e. complex systems gives rise to a need for human-computer interface (HCI) architecting to become part of the system design process. Current evidence tends to indicate that human error may be an increasing contributor to system failures. The degree to which human error appears to be on the increase may be related to the increased degree of system complexity. More specifically, complexity is a function of increased software intensiveness and computer hardware architecture dominance. HCI system architectures focus on the need for complex human-computer interface designs to be revisited to mitigate source conditions that contribute to human error.

HCI Architecting for System Reliability

Information optimization for decision-making is a system architecture definition problem as well as cognitive engineering or human factors issue. Human information processing, decision making and control are top-level functions of system architectures, but human information processing and decision making are not currently defined at that level. Present system design principles do not articulate a philosophy nor a body of practice about placing man in the system at the system architecture level. Since these functions do not appear in the top level architecture, they do not appear at the intermediate and lower levels of the design specifications. Having not been defined in the specifications, human decision making and control performance are generally not tested during the subsequent test and verification cycles. Decision making is no longer a uniquely human function in complex systems. Indeed, the speed and complexity of many system processes often preclude the human from decision and control functions. Several types of decision making processes can and should be automated. Optimized information processing for decision-making requires that cost allocations be made to provide for information generation, software and computer hardware architectures at the system-requirements levels.

Information optimization for decision making

In recent years, progressive attention has been paid to the research of man–machine interface. In many fields of application software system development, experts have listed man–machine interface as one of the commands for the urgent research and advancement. This article has proposed the improvement to the man–machine interface system control design for the practical value of life, and has presented an approach for algorithms using Machine Learning. The method used in the paper includes the spatial layout of human–machine interface supported by the design cognition model and the use of Machine Learning algorithms. Based on Max–Min Ant System, the optimal path of ant construction is obtained, which is the optimal layout scheme. The experimental result in the paper shows that: with the support of the cognitive model, the cabin man–machine interface system control design method based on Genetic Algorithm-Ants Algorithm is obtained. The layout design principles were summarized by the cognitive model, and the layout optimization objective function was constructed according to each principle, and the problem of solving layout parties and cases was transformed into a combinatorial optimization problem. The form of fitness function, pheromone, and heuristic information for layout optimization was studied, and the algorithm of manual optimization process was implemented based on Genetic Algorithm and Ant Algorithm, in order to obtain a good optimization performance and time performance as a result. The practical value of the control design of man–machine interface system with the Machine Learning algorithm is proved.

Research on algorithms for control design of human–machine interface system using ML

Technology now permits the building of very complex man-machine systems with centralized controls, with the result that many processes can be run by relatively few individual workers. Studies of failures within these complex systems indicate that they are usually the consequence of a series of highly complex coincidences. There is an institutional neglect or misunderstanding of the implications of low-probability, high-consequence events for the design of complex man-machine systems. We must stop designing systems in which we virtually guarantee that operator errors will occur with catastrophic consequences. The greatest payback in reducing high risk system accidents is to reduce catastrophes induced or exacerbated by human error. This paper discusses some task breakdowns between the human element and software/hardware. These task allocations allow the complex man-machine system to be designed more robustly and prevent human error so as to reduce possible catastrophic consequences. >

Minimizing human-machine interface failures in high risk systems

An analytical solution is obtained for a steady-state Kalman filter tracker with a random power spectral density as process noise Great insight is obtained from these analytic solutions of trackers Optimal relationships are obtained between the gain variables A unitless tracking index is defined as the only variable driving the steady-state Kalman filter tracker This unitless tracking index value is defined as: /spl Lambda/=/spl radic/(psd8(/spl Delta/T)/sup 3///spl sigma//sub m//sup 2/) Optimal gains and minimum covariance are analytically calculated given the tracking index /spl Lambda/A

Analytical solution for a steady-state Kalman filter tracker with random power spectral density process noise

Human error is the most complex and least understood factor in the failures of complex systems, accounting for as much as 60% to 80% of complex system failures and as much as 96% of simple system failures. We must stop designing systems in which we virtually guarantee that operator errors will occur with catastrophic consequences. The greatest payback in reducing high risk system accidents is to reduce catastrophes induced or exacerbated by human error. This paper discusses some task breakdowns between the human element and software/hardware. These task allocations allow the complex man-machine system to be designed more robustly and prevent human error so as to reduce possible catastrophic consequences. >

A transformation of unit vectors between Cartesian earth centered and local north, east, and up coordinates is presented to simplify derivations on an ellipsoid Earth. The unit vector relationships an used to simplify the derivation of a transformation for sending track files from a sensor platform (latitude, longitude) with local Cartesian coordinates (Xs-East, Ys-North, Zs-Up) to a receiving platform (latitude', longitude') with local Cartesian coordinates (Xr-East, Yr-North, Zr-Up). Zero altitude for both coordinate systems is taken to be sea level. Tracks may be ballistic, air, surface and/or subsurface.

http://ieeexplore.ieee.org/iel3/3912/11342/00522019.pdf

J.J. Sudano

Papers

Minimizing human-machine interface failures in high risk systems

Analytical solution for a steady-state Kalman filter tracker with random power spectral density process noise

Minimizing human-machine interface failures in high risk systems

A transformation of unit vectors to simplify derivations between Earth-centered and local North-East-and-up on an ellipsoid Earth