Francesc Pozo

Semiactive Control Methodologies for Suspension Control With Magnetorheological Dampers

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.

Adaptive Backstepping Control of Hysteretic Base-Isolated Structures

The paper considers a hybrid seismic control system for building structures, which combines a class of passive nonlinear base isolator with an active control system. The objective of the active component is to keep the base displacement relative to the ground, the interstory drift and the absolute acceleration within appropriate ranges. The base isolator device exhibits a hysteretic nonlinear behavior which is described by the Bouc-Wen model. The adaptive backstepping approach is used for the control design in order to cope with the nonlinearity and the presence of uncertainties. The control problem is formulated with representations of the system dynamics using two alternative coordinate sets: absolute (with respect to an inertial frame) and relative to the ground. A comparison between the two strategies is presented by means of numerical simulations.

A structural damage detection indicator based on principal component analysis and statistical hypothesis testing

A comprehensive statistical analysis is performed for structural health monitoring (SHM). The analysis starts by obtaining the baseline principal component analysis (PCA) model and projections using measurements from the healthy or undamaged structure. PCA is used in this framework as a way to compress and extract information from the sensor-data stored for the structure which summarizes most of the variance in a few (new) variables into the baseline model space. When the structure needs to be inspected, new experiments are performed and they are projected into the baseline PCA model. Each experiment is considered as a random process and, consequently, each projection into the PCA model is treated as a random variable. Then, using a random sample of a limited number of experiments on the healthy structure, it can be inferred using the ?2 test that the population or baseline projection is normally distributed with mean ?h and standard deviation ?h. The objective is then to analyse whether the distribution of samples that come from the current structure (healthy or not) is related to the healthy one. More precisely, a test for the equality of population means is performed with a random sample, that is, the equality of the sample mean ?s and the population mean ?h is tested. The results of the test can determine that the hypothesis is rejected (?h????c and the structure is damaged) or that there is no evidence to suggest that the two means are different, so the structure can be considered as healthy. The results indicate that the test is able to accurately classify random samples as healthy or not.

Force‐derivative feedback semi‐active control of base‐isolated buildings using large‐scale MR fluid dampers

The combination of passive and active schemes has been increasingly considered in the structural control com munity as a promising way to design efficient smart hybrid base isola tion systems for seismic protection. This paper considers a hybrid system in which an active feedback control law is derived to b e applied in parallel with a passive isolation device. The ac tive control uses the restoring force supplied by the passive isolator as the main feedback signal. This paper can be divided in two mai n p rts: in the first one, the paper presents the theoretical formulat ion nd stability analysis in the active control strategy; i n the second part, a set of numerical simulations is performed when the force is supplied in a semi-active way to validate and discuss the effi ci ncy of the approach in a more realistic scenario. Moreover, the p erformance of the proposed semi-active control algorithm i s compared with passive-off, passive-on and clipped-optimal control lers. The proposed control scheme reduces the base displace ment without increasing the floor accelerations.

Data-driven methodology to detect and classify structural changes under temperature variations

This paper presents a methodology for the detection and classification of structural changes under different temperature scenarios using a statistical data-driven modelling approach by means of a distributed piezoelectric active sensor network at different actuation phases. An initial baseline pattern for each actuation phase for the healthy structure is built by applying multiway principal component analysis (MPCA) to wavelet approximation coefficients calculated using the discrete wavelet transform (DWT) from ultrasonic signals which are collected during several experiments. In addition, experiments are performed with the structure in different states (simulated damages), pre-processed and projected into the different baseline patterns for each actuator. Some of these projections and squared prediction errors (SPE) are used as input feature vectors to a self-organizing map (SOM), which is trained and validated in order to build a final pattern with the aim of providing an insight into the classified states. The methodology is tested using ultrasonic signals collected from an aluminium plate and a stiffened composite panel. Results show that all the simulated states are successfully classified no matter what the kind of damage or the temperature is in both structures.

A Bioinspired Methodology Based on an Artificial Immune System for Damage Detection in Structural Health Monitoring

Among all the aspects that are linked to a structural health monitoring (SHM) system, algorithms, strategies, or methods for damage detection are currently playing an important role in improving the operational reliability of critical structures in several industrial sectors. This paper introduces a bioinspired strategy for the detection of structural changes using an artificial immune system (AIS) and a statistical data-driven modeling approach by means of a distributed piezoelectric active sensor network at different actuation phases. Damage detection and classification of structural changes using ultrasonic signals are traditionally performed using methods based on the time of flight. The approach followed in this paper is a data-based approach based on AIS, where sensor data fusion, feature extraction, and pattern recognition are evaluated. One of the key advantages of the proposed methodology is that the need to develop and validate a mathematical model is eliminated. The proposed methodology is applied, tested, and validated with data collected from two sections of an aircraft skin panel. The results show that the presented methodology is able to accurately detect damage.

Fault detection in base-isolation systems via a restoring force observer

This paper proposes a fault detection method for hysteretic base-isolation systems. One of the key contributions of this work is a Lyapunov-based restoring force observer that leads to the design of a robust fault detection scheme. The different fault types considered are stiffness and damping variations in the system. The proposed fault estimation method provides a direct estimate of the size and severity of the fault, which can be important in many civil engineering applications. A design procedure is described, and nonlinear simulation results are presented to demonstrate the applicability of the proposed method.

Numerical issues in backstepping control: Sensitivity and parameter tuning

Abstract The backstepping-based adaptive tuning functions design is a control-scheme for uncertain systems that ensures reasonably good stability and performance properties of the closed loop. The complexity of the controller makes inevitable the use of digital computers to perform the calculation of the control signal. This paper addresses the issue of the numerical sensitivity of this control scheme. It is shown that while the increase of the design parameters may be desirable to achieve a good transient performance, it harms the control signal as this increase introduces large high-frequency components due to the numerical errors. The presented results suggest that it is necessary a certain compromise between the choice of the design parameters and the numerical precision of the tools involved in the control design. This compromise can be quantified by explicit expressions.

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

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.

Detection of structural changes through principal component analysis and multivariate statistical inference

This article introduces a new methodology for the detection of structural changes using a statistical data-driven modeling approach by means of a distributed piezoelectric active sensor network at different actuation phases. The three main features that characterize the proposed methodology are (a) the nature of the data used in the test since vectors of principal component analysis projections are used instead of the entire measured response of the structure or the coefficients of an AutoRegressive model, (b) the number of data used since the test is based on two random samples instead of some characteristic indicators, and (c) the samples come from a multidimensional variable and therefore a test for the plausibility of a value for a normal population mean vector is performed. The framework of multivariate statistical inference is used with the objective of the classification of structures in healthy or damaged. The novel scheme for damage detection presented in this article —based on multivariate inference over the principal component analysis projections of the raw data—is applied, validated, and tested on a small aluminum plate. The results show that the presented methodology is able to accurately detect damages, that is, for each actuation phase, a unique and reliable damage detection indicator is obtained no matter the number of sensors and/or actuators. It is worth noting that a major contribution of this article is that there exists an entire range of significance levels where the multivariate statistical inference is able to offer a correct decision although all of the univariate tests make a wrong decision.