Robust fault detection for vehicle lateral dynamics: Azonotope-based set-membership approach

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksIn this work, a model-based fault detection layoutfor vehicle lateral dynamics system is presented. The majorfocus in this study is on the handling of model uncertainties andunknown inputs. In fact, the vehicle lateral model is affectedby several parameter variations such as longitudinal velocity,cornering stiffnesses coefficients and unknown inputs like windgust disturbances. Cornering stiffness parameters variation isconsidered to be unknown but bounded with known compactset. Their effect is addressed by generating intervals for theresiduals based on the zonotope representation of all possiblevalues. The developed fault detection procedure has been testedusing real driving data acquired from a prototype vehicle.Index Terms— Robust fault detection, interval models,zonotopes, set-membership, switched uncertain systems, LMIs,input-to-state stability, arbitrary switching.Peer ReviewedPostprint (author's final draft

Robust FDI/FTC using Set-membership Methods and Application to Real Case Studies

This paper reviews the use of set-membership methods in robust fault detection and isolation (FDI) and tolerant control (FTC). Set-membership methods use a deterministic unknown-but-bounded description of noise and parametric uncertainty (interval models). These methods aims to check the consistency between observed and predicted behavior by using simple sets to approximate the set of possible behaviors (in parameter or state space). When an inconsistency is detected a fault can be indicated, otherwise nothing can be stated. The same principle can be used to identify interval models for fault detection and to develop methods for fault tolerance evaluation. Finally, some real application of these methods will end the paper exemplifying the success of these methods in FDI/FTC.Postprint (published version

Tilt angle optimization of photovoltaic panels

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The solar PV power sector in Spain has been developing at a spectacular rate in recent years. The energy cost and the dependence on fossil fuels can be reduced by improving the efficiency of photovoltaic energy production. The performance of a solar radiation conversion system is affected by a tilt angle with the horizontal plane. Thus, the photovoltaic array needs to be tilted at the correct angle to maximize the performance of the system. In this paper, we found the optimum tilt angle and applied for Barcelona, Spain, located at latitude 41o 22' 56'' North and longitude 2o 6'56'' East. The optimal tilt angle for Winter (December, January, February) is 56.46 and the optimum tilt angle for Spring (March, April, May Ls 2 .11°and the optimum tilt angle for Summer(Jun, July, August Ls 13.76° and the optimum tilt angle for Autumn (September, 2ctober, November Ls 8.1 °. finally, the annual optimum tilt angle for our latitude Ls 36.87°, ZLth thLs optimal slope angle, maximum monthly and annual solar radiation is calculated. In this way, we can increase the energy generation when achieved to maximum solar radiation. With this process, we were able to increase 10.54% of energy.Peer ReviewedPostprint (author's final draft

Robust optimization based energy dispatch in smart grids considering demand uncertainty

In this study we discuss the application of robust optimization to the problem of economic energy dispatch in smart grids. Robust optimization based MPC strategies for tackling uncertain load demands are developed. Unexpected additive disturbances are modelled by defining an affine dependence between the control inputs and the uncertain load demands. The developed strategies were applied to a hybrid power system connected to an electrical power grid. Furthermore, to demonstrate the superiority of the standard Economic MPC over the MPC tracking, a comparison (e.g average daily cost) between the standard MPC tracking, the standard Economic MPC, and the integration of both in one-layer and two-layer approaches was carried out. The goal of this research is to design a controller based on Economic MPC strategies, that tackles uncertainties, in order to minimise economic costs and guarantee service reliability of the system.Postprint (author's final draft

Distributed fault diagnosis using minimal structurally over-determined sets: Application to a water distribution network

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksDistributed fault diagnosis is becoming more and more common in industries, to diagnose faults in any large scale system. There are a lot of disadvantages using centralized fault diagnosis in large-scale systems, since in a centralized implementation all the information has to be collected in one location which is generally not possible or very difficult. Moreover, a centralized system needs a high performance centralized unit which generally in most cases is not available. Due to these difficulties in recent years distributed fault diagnosis techniques have been investigated [10]. In distributed fault diagnosis [1] [2], the global diagnoses for the complete system can be computed from the results in all agents and local diagnose is computed from the results of one agent. In distributed fault diagnosis [3] [8], a global coordination process is not necessary and each subsystem depends on a local diagnoser for local diagnosis tasks and communicating with the remaining local diagnosers until a global diagnosis is produced.Accepted versio

Economic MPC with periodic terminal constraints of nonlinear differential-algebraic-equation systems: Application to drinking water networks

© 2026 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksIn this paper, an Economic Model Predictive Control (EMPC) strategy with periodic terminal constraints is addressed for nonlinear differential-algebraic-equation systems with an application to Drinking Water Networks (DWNs). DWNs have some periodic behaviours because of the daily seasonality of water demands and electrical energy price. The periodic terminal constraint and economic terminal cost are implemented in the EMPC controller design for the purpose of achieving convergence. The feasibility of the proposed EMPC strategy when disturbances are considered is guaranteed by means of soft constraints implemented by using slack variables. Finally, the comparison results in a case study of the D-Town water network is provided by applying the EMPC strategy with or without periodic terminal constraints.Accepted versio

Robust fault detection using set-based approaches

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThis paper presents the performance of zonotopic fault detection (FD) for additive and multiplicative fault using direct test and inverse test. Zonotopic set-based approaches use the zonotope to describe the uncertain state, parameter and noise which are assumed unknown but bounded to reduce their influences on FD. These FD test methods aim at checking the consistency between the measured and estimated behaviour obtained from estimator in the parameter or output space. When an inconsistency is detected between these two, a fault can be indicated. At last, a motor model will be used to compare the performance of direct test and inverse test for additive and multiplicative faults.This work has been co-financed by the European Regional Development Fund of the European Union in the framework of the ERDF Operational Program of Catalonia 2014-2020, under the research project 001-P-001643 Agrupacio Loom- íng Factory.Peer ReviewedPostprint (author's final draft

Autonomous vehicle state estimation using a LPV Kalman filter and SLAM

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThis paper presents an optimal approach for state estimation and Simultaneous Localization and Mapping (SLAM) correction using Kalman gain obtained via Linear Matrix Inequality (LMI). The technique utilizes a Linear Parameter Varying (LPV) represention of the system, which allows to model the complex non-linear dynamics in a way that linearization is not required for the estimator or controller design. In addition, the LPV polytopic representation is exploited to obtain a real-time Kalman gain, avoiding expensive optimization of LMIs at every step. The estimation schema is integrated with a Non-linear Model Predictive Control (NMPC) in charge of controlling the vehicle. For the demonstration, the approach is tested in the simulation and for the practical validity, a small-scale autonomous car is used.Peer ReviewedPostprint (author's final draft

Fault diagnosis and fault tolerant control using set-membership approaches: Application to real case studies

This paper reviews the use of set-membership methods in fault diagnosis (FD) and fault tolerant control (FTC). Setmembership methods use a deterministic unknown-but-bounded description of noise and parametric uncertainty (interval models). These methods aims at checking the consistency between observed and predicted behaviour by using simple sets to approximate the exact set of possible behaviour (in the parameter or the state space). When an inconsistency is detected between the measured and predicted behaviours obtained using a faultless system model, a fault can be indicated. Otherwise, nothing can be stated. The same principle can be used to identify interval models for fault detection and to develop methods for fault tolerance evaluation. Finally, some real applications will be used to illustrate the usefulness and performance of set-membership methods for FD and FTC.Peer ReviewedPostprint (published version

Aportación a la Generación de Umbrales Adaptativos a partir de Envolventes de Sistemas con Modelos Aproximados. Aplicación a la Detección y Diagnosis Robusta de Fallos en Procesos Industriales

El objetivo de la presente tesis es desarrollar un nuevo método de generación de umbrales adaptativos mediante la obtención de las respuestas temporales máxima y mínima a cada instante de tiempo, denominadas envolventes, a partir del modelo de un sistema con incertidumbre parmétrica de tipo intercalar en los parámetros. El nuevo algoritmo para la generación de envolventes presentado en esta tesis está basado en una ventana temporal deslizante y optimización. Una vez obtenidas las envolventes a partir del nuevo algoritmo de generación, se utilizarán para la detección robusta de fallos en procesos industriales.La generación de envolventes para su posterior utilización como un umbral adaptativo en un método de detección y diagnóstico de fallo es todavía hoy un problema abierto. Se han propuesto muchos algoritmos para su generación pero ninguno de ellos consiguegarantizar la obtención de las envolventes correctas, entendiendo como correctas aquellas envolventes debidas a la incertidumbre presente en los parámetros del sistema y a la incertidumbre sobre los estados iniciales.El método de generación de envolventes propuesto en esta tesis consigue generar las envolventes con la precisión deseada y con la incertidumbre acumulada por el proceso de generación de las envolventes acotada.Los resultados y aportaciones más significativas que se han obtenido en esta tesis se enumeran a continuación:. Nuevo algoritmo para la generación de envolventes basado en optimización y en el paradigma de las ventanas deslizantes.. Determinación analítica y mediante simulaciones de la longitud de ventana óptima para obtener unas envolventes correctas.. Demostración de que el nuevo algoritmo evita los problemas que padecen la mayoría de algoritmos de generación de envolventes: wrapping, multiincidencias, problemas de óptimos locales y el problema de propagación de la incertidumbre