GRUPO DE DIAGNÓSTICO Y CONTROL

In the fluid distribution systems, automatic fault monitoring and diagnosis are of great relevance worldwide. The primary purpose of an automatic pipeline monitoring system is to detect leaks and obstructions as quickly as possible, with a minimum of instrumentation and cost. In the case of leaks, these can cause substantial economic losses, damages to the environment and health risks. 

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The aim of this project is to design algorithms to detect leaks in pipelines by considering a combining methodology of model-based and data-driven fault diagnosis approaches. Model-based will be used to obtain an accurate mathematical model of a pipeline system. This information will be used to design fault diagnosis based observers algorithms to detect and isolate leaks by considering extended Kalman filters (EKF), descriptor approaches, state observers, among others.  Additionally, data-driven methods, which apply multivariable statistic and machine learning methods, will be used for the same purpose. The idea is to compare the effectiveness and applicability of both methodologies in order to extend it to real pipelines systems. 

The algorithms will be implemented in a 250m pipeline benchmark, which is instrumented with pressure and flow sensors located on the extremes of the pipelines, which are connected to a SCADA system.

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The design of fault diagnosis and fault tolerant control systems based on Takagi-Sugeno modeling has been receiving considerable attention in the last years.  Nonetheless, most of the works consider the case of measurable premise variables, moreover, it is considered that such variable is measured with high precision. However, from a practical point of view,  these premise variables are measured with a certain degree of uncertainty, e.g.,  sensors with offsets, low resolutions, imprecisions due to calibration, weather changes, instrument quality, etc.  In this case,  it is necessary to consider inexact premise variables in order to design a reliable and effective FDI system. 

This project is dedicated to the study of nonlinear modeling and optimal energy consumptions strategies for electric vehicles.    To attack the mathematical modeling, a dynamic characterization of an EV is considered by a mixed approach, which considers gray box identification and physical principles techniques. 

 

On the other hand, energy consumption strategies are considered through the design of power electronic converters, regenerative breaks, and traction control.

Unmanned aerial vehicles (UAVs) are gaining more and more attention in recent years due to their important contribution and profitable application in various tasks such as surveillance, search, rescue, remote sensing, geographical studies, as well as various military and security applications.  Nonetheless, they cannot take-off and land vertically. On the other hand, multirotor vehicles can take-off, land vertically and stay in a hover position, which could be very convenient for some applications such as surveillance, precision farming, power-line autonomous inspection, delivering.

 

Many research works are focussed on the search of techniques that guarantee stability, control, and robustness for the UAV. However, the increase in civil applications makes necessary to consider new and difficult situations regarding the vehicle's safety, e.g.  flying in an urban environment where the security is a critical target to achieve. For such reason, it is necessary to develop new robust fault detection and isolation systems, which guarantee the security of the UAV during the all-time fly envelope.

 

This project is focussed on the design fault diagnosis and fault tolerant control of diagnosis of Unmanned Aerial Vehicles (UAV) under actuator and sensors with partial or total fault. The developed methods are tested in powerful simulations software as MATLAB, and also in real UAVs, which are developed for the people in the group.