Francesc Pozo

Bio: Francesc Pozo is an academic researcher from Polytechnic University of Catalonia. The author has contributed to research in topics: Structural health monitoring & Fault detection and isolation. The author has an hindex of 22, co-authored 96 publications receiving 1421 citations. Previous affiliations of Francesc Pozo include University of Barcelona & Instituto Politécnico Nacional.

Semiactive Control Methodologies for Suspension Control With Magnetorheological Dampers

Abstract: Suspension systems are one of the most critical components of transportation vehicles. They are designed to provide comfort to the passengers to protect the chassis and the freight. Suspension systems are normally provided with dampers that mitigate these harmful and uncomfortable vibrations. In this paper, we explore two control methodologies (in time and frequency domain) used to design semiactive controllers for suspension systems that make use of magnetorheological dampers. These dampers are known because of their nonlinear dynamics, which requires the use of nonlinear control methodologies for an appropriate performance. The first methodology is based on the backstepping technique, which is applied with adaptation terms and H∞ constraints. The other methodology to be studied is the quantitative feedback theory (QFT). Despite QFT is intended for linear systems, it can still be applied to nonlinear systems. This can be achieved by representing the nonlinear dynamics as a linear system with uncertainties that approximately represents the true behavior of the plant to be controlled. The semiactive controllers are simulated in MATLAB/Simulink for performance evaluation.

A Sensor Data Fusion System Based on k-Nearest Neighbor Pattern Classification for Structural Health Monitoring Applications

Abstract: Civil and military structures are susceptible and vulnerable to damage due to the environmental and operational conditions. Therefore, the implementation of technology to provide robust solutions in damage identification (by using signals acquired directly from the structure) is a requirement to reduce operational and maintenance costs. In this sense, the use of sensors permanently attached to the structures has demonstrated a great versatility and benefit since the inspection system can be automated. This automation is carried out with signal processing tasks with the aim of a pattern recognition analysis. This work presents the detailed description of a structural health monitoring (SHM) system based on the use of a piezoelectric (PZT) active system. The SHM system includes: (i) the use of a piezoelectric sensor network to excite the structure and collect the measured dynamic response, in several actuation phases; (ii) data organization; (iii) advanced signal processing techniques to define the feature vectors; and finally; (iv) the nearest neighbor algorithm as a machine learning approach to classify different kinds of damage. A description of the experimental setup, the experimental validation and a discussion of the results from two different structures are included and analyzed.

A European Association for the Control of Structures joint perspective. Recent studies in civil structural control across Europe

Abstract: SUMMARY Structural control has been comprehensively studied over the world as a multidisciplinary research field. The present work is motivated by an attempt to give a common frame to the recent research and applications of structural control technology in civil engineering across Europe. They include novel passive dampers, functional materials and semi-active dampers, active control systems, and their performance investigations. Design methods for the vibrations reduction of buildings, bridges, and wind turbines are discussed with reference to case studies. Control algorithms and dimension reduction techniques are also studied. Adaptation strategies and techniques based on the potential offered by piezoelectricity are reviewed. Copyright © 2014 John Wiley & Sons, Ltd. Copyright © 2014 John Wiley & Sons, Ltd.

Damage Identification in Structural Health Monitoring: A Brief Review from its Implementation to the Use of Data-Driven Applications.

Abstract: The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

The Hysteresis Bouc-Wen Model, a Survey

Abstract: Structural systems often show nonlinear behavior under severe excitations generated by natural hazards. In that condition, the restoring force becomes highly nonlinear showing significant hysteresis. The hereditary nature of this nonlinear restoring force indicates that the force cannot be described as a function of the instantaneous displacement and velocity. Accordingly, many hysteretic restoring force models were developed to include the time dependent nature using a set of differential equations. This survey contains a review of the past, recent developments and implementations of the Bouc-Wen model which is used extensively in modeling the hysteresis phenomenon in the dynamically excited nonlinear structures.

Reliable Fuzzy Control for Active Suspension Systems With Actuator Delay and Fault

Abstract: This paper is focused on reliable fuzzy H∞ controller design for active suspension systems with actuator delay and fault. The Takagi-Sugeno (T-S) fuzzy model approach is adapted in this study with the consideration of the sprung and the unsprung mass variation, the actuator delay and fault, and other suspension performances. By the utilization of the parallel-distributed compensation scheme, a reliable fuzzy H∞ performance analysis criterion is derived for the proposed T-S fuzzy model. Then, a reliable fuzzy H∞ controller is designed such that the resulting T-S fuzzy system is reliable in the sense that it is asymptotically stable and has the prescribed H∞ performance under given constraints. The existence condition of the reliable fuzzy H∞ controller is obtained in terms of linear matrix inequalities (LMIs) Finally, a quarter- vehicle suspension model is used to demonstrate the effectiveness and potential of the proposed design techniques.