Filter for detecting and isolating faults for a nonlinear system

In the paper the problem of detecting and isolating multiple faults for nonlinear systems is considered. A strategy of state filtering is derived in order to detect and isolate multiple faults which appear simultaneously or sequentially in a discrete time nonlinear systems with unknown inputs. For the considered system for which a fault isolation condition is fulfilled the proposed method can isolate p simultaneous faults with at least p+q output measurements, where q is the number of unknown inputs or disturbances. A reduced output residual vector of dimension p+q is generated and the elements of this vector are decoupled in a way that each element of the vector is associated with only one fault or unmeasured input

Predictive feedback control using a multiple model approach

A new method of designing predictive controllers for SISO systems is presented. The controller selects the model used in the design of the control law from a given set of models according to a switching rule based on output prediction errors. The goal is to design, at each sample instant, a feedback control law that ensures robust stability of the closed–loop system and gives better performance for the current operating point. The overall multiple model predictive control scheme quickly identifies the closest linear model to the dynamics of the current operating point, and carries out an automatic reconfiguration of the control system to achieve a better performance. The results are illustrated with simulations of a continuous stirred tank reactor

State and parameter estimation approach to monitoring AGR nuclear core

This work concerns with the problem of monitoring an Advanced Gas-cooled Nuclear Reactor (AGR) core. This plant (figure 1) makes use of the heat given by the nuclear efficient reaction to produce electricity by means of steam turbines. These are driven by steam, which is heated, from the AGR gas using a heat exchanger. One of the advantages of a gas cooled reactor is the high temperature that the gas can achieve so that when it is used in conjunction with the heat exchanger and steamed turbine the thermal efficiency is very high

Wiener modelling and model predictive control for wastewater applications

The research presented in this paper aims to demonstrate the application of predictive control to an integrated wastewater system with the use of the wiener modeling approach. This allows the controlled process, dissolved oxygen, to be considered to be composed of two parts: the linear dynamics, and a static nonlinearity, thus allowing control other than common approaches such as gain-scheduling, or switching, for series of linear controllers. The paper discusses various approaches to the modelling required for control purposes, and the use of wiener modelling for the specific application of integrated waste water control. This paper demonstrates this application and compares with that of another nonlinear approach, fuzzy gain-scheduled control

Condition monitoring of an advanced gas-cooled nuclear reactor core

A critical component of an advanced gas-cooled reactor station is the graphite core. As a station ages, the graphite bricks that comprise the core can distort and may eventually crack. Since the core cannot be replaced, the core integrity ultimately determines the station life. Monitoring these distortions is usually restricted to the routine outages, which occur every few years, as this is the only time that the reactor core can be accessed by external sensing equipment. This paper presents a monitoring module based on model-based techniques using measurements obtained during the refuelling process. A fault detection and isolation filter based on unknown input observer techniques is developed. The role of this filter is to estimate the friction force produced by the interaction between the wall of the fuel channel and the fuel assembly supporting brushes. This allows an estimate to be made of the shape of the graphite bricks that comprise the core and, therefore, to monitor any distortion on them

Autonomous navigation with deadlock detection and avoidance

This paper studies alternatives to solve the problem of autonomous mobile robots navigation in unknown indoor environments. The navigation system uses fuzzy logic to combine the information obtained from range sensors and the navigational data to plan the robot’s movements. The strategy is built upon five modules: i) target following, ii) obstacle avoidance, iii) possible path, iv) deadlock detection and v) wall following. Given a possible path and obstacles near the environment of the robot, the controller will modulate the output velocity in order to go to the target and avoid collisions. In case of dead lock situations, a method that enables the robot to detect, escape and reach the target is proposed. The performance and behavior of the proposed navigational system was evaluated through simulations in different conditions, where the effectiveness of the proposed method is demonstrated and compared with previous results.Sociedad Argentina de Informática e Investigación Operativ

Autonomous navigation with deadlock detection and avoidance

This paper studies alternatives to solve the problem of autonomous mobile robots navigation in unknown indoor environments. The navigation system uses fuzzy logic to combine the information obtained from range sensors and the navigational data to plan the robot’s movements. The strategy is built upon five modules: i) target following, ii) obstacle avoidance, iii) possible path, iv) deadlock detection and v) wall following. Given a possible path and obstacles near the environment of the robot, the controller will modulate the output velocity in order to go to the target and avoid collisions. In case of dead lock situations, a method that enables the robot to detect, escape and reach the target is proposed. The performance and behavior of the proposed navigational system was evaluated through simulations in different conditions, where the effectiveness of the proposed method is demonstrated and compared with previous results.Sociedad Argentina de Informática e Investigación Operativ

Spitzer Observations of Cold Dust Galaxies

We combine new Spitzer Space Telescope observations in the mid- and far-infrared with SCUBA 850 micron observations to improve the measurement of dust temperatures, masses and luminosities for 11 galaxies of the SCUBA Local Universe Galaxy Survey (SLUGS). By fitting dust models we measure typical dust masses of 10E7.9 M_sol and dust luminosities of ~ 10E10 L_sol, for galaxies with modest star formation rates. The data presented in this paper combined with previous observations show that cold dust is present in all types of spiral galaxies and is a major contributor to their total luminosity. Because of the lower dust temperature of the SCUBA sources measured in this paper, they have flatter Far-IR nu F_nu(160um)/nu F_nu(850um) slopes than the larger Spitzer Nearby Galaxies Survey (SINGS), the sample that provides the best measurements of the dust properties of galaxies in the nearby universe. The new data presented here added to SINGS extend the parameter space that is well covered by local galaxies, providing a comprehensive set of templates that can be used to interpret the observations of nearby and distant galaxies.Comment: Accepted by A.J. 16 pages, 10 figures, 7 tables. High resolution version at http://mips.as.arizona.edu/~cnaw/slugs_hires.pd

A cellular automata to model epidemics

Compartmental models are very popular in epidemiology, they provide excellent results when the populations satisfy certain hypotheses as large population size or population homogeneity, the complexity of this models is low making their analysis intuitive. In the other hand, they ignore important factors inherent to the problem, such as the nature of contacts between individuals and population heterogeneity. Cellular automata models are adequate to describe natural systems consisting of a massive collection of simple objects. They represent the global system behavior as a colection of simpler objects or cells. In this paper we propouse a cellular automata model to study the time evolution of a heterogeneous population through the various stages of disease resulting from the individuals interactions (epidemic). We validate the model with real data of flu that hit Geneva (Switzerland) in 1918 and then we will test the model under different assumptions discussing the result that each has on the disease dynamics.Sociedad Argentina de Informática e Investigación Operativ

A cellular automata to model epidemics

Compartmental models are very popular in epidemiology, they provide excellent results when the populations satisfy certain hypotheses as large population size or population homogeneity, the complexity of this models is low making their analysis intuitive. In the other hand, they ignore important factors inherent to the problem, such as the nature of contacts between individuals and population heterogeneity. Cellular automata models are adequate to describe natural systems consisting of a massive collection of simple objects. They represent the global system behavior as a colection of simpler objects or cells. In this paper we propouse a cellular automata model to study the time evolution of a heterogeneous population through the various stages of disease resulting from the individuals interactions (epidemic). We validate the model with real data of flu that hit Geneva (Switzerland) in 1918 and then we will test the model under different assumptions discussing the result that each has on the disease dynamics.Sociedad Argentina de Informática e Investigación Operativ