Articles de revista
http://hdl.handle.net/2117/1125
Wed, 25 May 2016 19:07:34 GMT2016-05-25T19:07:34ZRobust fault detection of singular LPV systems with multiple time-varying delays
http://hdl.handle.net/2117/87177
Robust fault detection of singular LPV systems with multiple time-varying delays
Hassanabadi, Amir Hossein; Shafiee, Masoud; Puig Cayuela, Vicenç
In this paper, the robust fault detection problem for LPV singular delayed systems in the presence of disturbances and actuator faults is considered. For both disturbance decoupling and actuator fault detection, an unknown input observer (UIO) is proposed. The aim is to compute a residual signal which has minimum sensitivity to disturbances while having maximum sensitivity to faults. Robustness to unknown inputs is formulated in the sense of the H-infinity-norm by means of the bounded real lemma (BRL) for LPV delayed systems. In order to formulate fault sensitivity conditions, a reference model which characterizes the ideal residual behavior in a faulty situation is considered. The residual error with respect to this reference model is computed. Then, the maximization of the residual fault effect is converted to minimization of its effect on the residual error and is addressed by using the BRL. The compromise between the unknown input effect and the fault effect on the residual is translated into a multi-objective optimization problem with some LMI constraints. In order to show the efficiency and applicability of the proposed method, a part of the Barcelona sewer system is considered.
Thu, 19 May 2016 09:12:00 GMThttp://hdl.handle.net/2117/871772016-05-19T09:12:00ZHassanabadi, Amir HosseinShafiee, MasoudPuig Cayuela, VicençIn this paper, the robust fault detection problem for LPV singular delayed systems in the presence of disturbances and actuator faults is considered. For both disturbance decoupling and actuator fault detection, an unknown input observer (UIO) is proposed. The aim is to compute a residual signal which has minimum sensitivity to disturbances while having maximum sensitivity to faults. Robustness to unknown inputs is formulated in the sense of the H-infinity-norm by means of the bounded real lemma (BRL) for LPV delayed systems. In order to formulate fault sensitivity conditions, a reference model which characterizes the ideal residual behavior in a faulty situation is considered. The residual error with respect to this reference model is computed. Then, the maximization of the residual fault effect is converted to minimization of its effect on the residual error and is addressed by using the BRL. The compromise between the unknown input effect and the fault effect on the residual is translated into a multi-objective optimization problem with some LMI constraints. In order to show the efficiency and applicability of the proposed method, a part of the Barcelona sewer system is considered.Formalism for a multiresolution time series database model
http://hdl.handle.net/2117/87125
Formalism for a multiresolution time series database model
Llusa Serra, Aleix; Vila Marta, Sebastià; Escobet Canal, Teresa
We formalise a specialised database management system model for time series using a multiresolution approach. These special purpose database systems store time series lossy compressed in a space-bounded storage. Time series can be stored at multiple resolutions, using distinct attribute aggregations and keeping its temporal attribute managed in a consistent way.
The model exhibits a generic approach that facilitates its customisation to suit better the actual application requirements in a given context. The elements, the meaning of which depends on a real application, are of generic nature.
Furthermore, we consider some specific time series properties that are a challenge in the multiresolution approach. We also describe a reference implementation of the model and introduce a use case based on real data.
Tue, 17 May 2016 15:17:06 GMThttp://hdl.handle.net/2117/871252016-05-17T15:17:06ZLlusa Serra, AleixVila Marta, SebastiàEscobet Canal, TeresaWe formalise a specialised database management system model for time series using a multiresolution approach. These special purpose database systems store time series lossy compressed in a space-bounded storage. Time series can be stored at multiple resolutions, using distinct attribute aggregations and keeping its temporal attribute managed in a consistent way.
The model exhibits a generic approach that facilitates its customisation to suit better the actual application requirements in a given context. The elements, the meaning of which depends on a real application, are of generic nature.
Furthermore, we consider some specific time series properties that are a challenge in the multiresolution approach. We also describe a reference implementation of the model and introduce a use case based on real data.Time-varying scheme for non-centralized model predictive control of large-scale systems
http://hdl.handle.net/2117/85452
Time-varying scheme for non-centralized model predictive control of large-scale systems
Nuñez Vicencio, Alfredo; Ocampo-Martínez, Carlos; Maestre Torreblanca, José María; De Schutter, Bart
The Non-Centralized Model Predictive Control (NC-MPC) framework in this paper refers to any distributed, hierarchical, or decentralized model predictive controller (or a combination of them) the structure of which can change over time and the control actions of which are not obtained based on a centralized computation. Within this framework, we propose suitable on-line methods to decide which information is shared and how this information is used between the different local predictive controllers operating in a decentralized, distributed, and/or hierarchical way. Evaluating all the possible structures of the NC-MPC controller leads to a combinatorial optimization problem. Therefore, we also propose heuristic reduction methods, to keep tractable the number of NC-MPC problems to be solved. To show the benefits of the proposed framework, a case study of a set of coupled water tanks is presented.
Fri, 08 Apr 2016 17:43:57 GMThttp://hdl.handle.net/2117/854522016-04-08T17:43:57ZNuñez Vicencio, AlfredoOcampo-Martínez, CarlosMaestre Torreblanca, José MaríaDe Schutter, BartThe Non-Centralized Model Predictive Control (NC-MPC) framework in this paper refers to any distributed, hierarchical, or decentralized model predictive controller (or a combination of them) the structure of which can change over time and the control actions of which are not obtained based on a centralized computation. Within this framework, we propose suitable on-line methods to decide which information is shared and how this information is used between the different local predictive controllers operating in a decentralized, distributed, and/or hierarchical way. Evaluating all the possible structures of the NC-MPC controller leads to a combinatorial optimization problem. Therefore, we also propose heuristic reduction methods, to keep tractable the number of NC-MPC problems to be solved. To show the benefits of the proposed framework, a case study of a set of coupled water tanks is presented.Set-membership identification and fault detection using a Bayesian framework
http://hdl.handle.net/2117/84709
Set-membership identification and fault detection using a Bayesian framework
Fernández Canti, Rosa M.; Blesa Izquierdo, Joaquim; Puig Cayuela, Vicenç; Tornil Sin, Sebastián
This paper deals with the problem of set-membership identification and fault detection using a Bayesian framework. The paper presents how the set-membership model estimation problem can be reformulated from the Bayesian viewpoint in order to, first, determine the feasible parameter set in the identification stage and, second, check the consistency between the measurement data and the model in the fault-detection stage. The paper shows that, assuming uniform distributed measurement noise and uniform model prior probability distributions, the Bayesian approach leads to the same feasible parameter set than the well-known set-membership technique based on approximating the feasible parameter set using sets. Additionally, it can deal with models that are nonlinear in the parameters. The single-output and multiple-output cases are addressed as well. The procedure and results are illustrated by means of the application to a quadruple-tank process.
Fri, 18 Mar 2016 14:10:52 GMThttp://hdl.handle.net/2117/847092016-03-18T14:10:52ZFernández Canti, Rosa M.Blesa Izquierdo, JoaquimPuig Cayuela, VicençTornil Sin, SebastiánThis paper deals with the problem of set-membership identification and fault detection using a Bayesian framework. The paper presents how the set-membership model estimation problem can be reformulated from the Bayesian viewpoint in order to, first, determine the feasible parameter set in the identification stage and, second, check the consistency between the measurement data and the model in the fault-detection stage. The paper shows that, assuming uniform distributed measurement noise and uniform model prior probability distributions, the Bayesian approach leads to the same feasible parameter set than the well-known set-membership technique based on approximating the feasible parameter set using sets. Additionally, it can deal with models that are nonlinear in the parameters. The single-output and multiple-output cases are addressed as well. The procedure and results are illustrated by means of the application to a quadruple-tank process.Ouput-feedback control of combined sewer networks through receding horizon control with moving horizon estimation
http://hdl.handle.net/2117/84090
Ouput-feedback control of combined sewer networks through receding horizon control with moving horizon estimation
Joseph Duran, Bernat; Ocampo-Martínez, Carlos; Cembrano Gennari, Gabriela
An output-feedback control strategy for pollution mitigation in combined sewer networks is presented. The proposed strategy provides means to apply model-based predictive control to large-scale sewer networks, in-spite of the lack of measurements at most of the network sewers. In previous works, the authors presented a hybrid linear control-oriented model for sewer networks together with the formulation of Optimal Control Problems (OCP) and State Estimation Problems (SEP). By iteratively solving these problems, preliminary Receding Horizon Control with Moving Horizon Estimation (RHC/MHE) results, based on flow measurements, were also obtained. In this work, the RHC/MHE algorithm has been extended to take into account both flow and water level measurements and the resulting control loop has been extensively simulated to assess the system performance according to different measurement availability scenarios and rain events. All simulations have been carried out using a detailed physically-based model of a real case-study network as virtual reality.
Wed, 09 Mar 2016 18:20:31 GMThttp://hdl.handle.net/2117/840902016-03-09T18:20:31ZJoseph Duran, BernatOcampo-Martínez, CarlosCembrano Gennari, GabrielaAn output-feedback control strategy for pollution mitigation in combined sewer networks is presented. The proposed strategy provides means to apply model-based predictive control to large-scale sewer networks, in-spite of the lack of measurements at most of the network sewers. In previous works, the authors presented a hybrid linear control-oriented model for sewer networks together with the formulation of Optimal Control Problems (OCP) and State Estimation Problems (SEP). By iteratively solving these problems, preliminary Receding Horizon Control with Moving Horizon Estimation (RHC/MHE) results, based on flow measurements, were also obtained. In this work, the RHC/MHE algorithm has been extended to take into account both flow and water level measurements and the resulting control loop has been extensively simulated to assess the system performance according to different measurement availability scenarios and rain events. All simulations have been carried out using a detailed physically-based model of a real case-study network as virtual reality.Robust fault diagnosis of proton exchange membrane fuel cells using a Takagi-Sugeno interval observer approach
http://hdl.handle.net/2117/83960
Robust fault diagnosis of proton exchange membrane fuel cells using a Takagi-Sugeno interval observer approach
Rotondo, Damiano; Fernández Canti, Rosa M.; Tornil Sin, Sebastián; Blesa Izquierdo, Joaquim; Puig Cayuela, Vicenç
In this paper, the problem of robust fault diagnosis of proton exchange membrane (PEM) fuel cells is addressed by introducing the Takagi-Sugeno (TS) interval observers that consider uncertainty in a bounded context, adapting TS observers to the so-called interval approach. Design conditions for the TS interval observer based on regional pole placement are also introduced to guarantee the fault detection and isolation (FDI) performance. The fault detection test is based on checking the consistency between the measurements and the output estimations provided by the TS observers. In presence of bounded uncertainty, this check relies on determining if all the measurements lie inside their corresponding estimated interval bounds. When a fault is detected, the measurements that are inconsistent with their corresponding estimations are annotated and a fault isolation procedure is triggered. By using the theoretical fault signature matrix (FSM), which summarizes the effects of the different faults on the available residuals, the fault is isolated by means of a logic reasoning that takes into account the bounded uncertainty, and if the number of candidate faults is more than one, a correlation analysis is used to obtain the most likely fault candidate. Finally, the proposed approach is tested using a PEM fuel cell case study proposed in the literature.
Tue, 08 Mar 2016 11:22:35 GMThttp://hdl.handle.net/2117/839602016-03-08T11:22:35ZRotondo, DamianoFernández Canti, Rosa M.Tornil Sin, SebastiánBlesa Izquierdo, JoaquimPuig Cayuela, VicençIn this paper, the problem of robust fault diagnosis of proton exchange membrane (PEM) fuel cells is addressed by introducing the Takagi-Sugeno (TS) interval observers that consider uncertainty in a bounded context, adapting TS observers to the so-called interval approach. Design conditions for the TS interval observer based on regional pole placement are also introduced to guarantee the fault detection and isolation (FDI) performance. The fault detection test is based on checking the consistency between the measurements and the output estimations provided by the TS observers. In presence of bounded uncertainty, this check relies on determining if all the measurements lie inside their corresponding estimated interval bounds. When a fault is detected, the measurements that are inconsistent with their corresponding estimations are annotated and a fault isolation procedure is triggered. By using the theoretical fault signature matrix (FSM), which summarizes the effects of the different faults on the available residuals, the fault is isolated by means of a logic reasoning that takes into account the bounded uncertainty, and if the number of candidate faults is more than one, a correlation analysis is used to obtain the most likely fault candidate. Finally, the proposed approach is tested using a PEM fuel cell case study proposed in the literature.A methodology and a software tool for sensor data validation/reconstruction : application to the Catalonia regional water retwork
http://hdl.handle.net/2117/83957
A methodology and a software tool for sensor data validation/reconstruction : application to the Catalonia regional water retwork
Cugueró Escofet, Miquel Àngel; García Valverde, Diego; Quevedo Casín, Joseba Jokin; Puig Cayuela, Vicenç; Espin, Santiago; Roquet, Jaume
In this paper, a sensor data validation/reconstruction methodology applicable to water networks and its implementation by means of a software tool are presented. The aim is to guarantee that the sensor data are reliable and complete in case that sensor faults occur. The availability of such dataset is of paramount importance in order to successfully use the sensor data for further tasks e.g. water billing, network efficiency assessment, leak localization and real-time operational control. The methodology presented here is based on a sequence of tests and on the combined use of spatial models (SM) and time series models (TSM) applied to the sensors used for real-time monitoring and control of the water network. Spatial models take advantage of the physical relations between different system variables (e.g. flow and level sensors in hydraulic systems) while time series models take advantage of the temporal redundancy of the measured variables (here by means of a Holt–Winters (HW) time series model). First, the data validation approach, based on several tests of different complexity, is described to detect potential invalid or missing data. Then, the reconstruction process is based on a set of spatial and time series models used to reconstruct the missing/invalid data with the model estimation providing the best fit. A software tool implementing the proposed data validation and reconstruction methodology is also described. Finally, results obtained applying the proposed methodology to a real case study based on the Catalonia regional water network is used to illustrate its performance.
Tue, 08 Mar 2016 11:09:20 GMThttp://hdl.handle.net/2117/839572016-03-08T11:09:20ZCugueró Escofet, Miquel ÀngelGarcía Valverde, DiegoQuevedo Casín, Joseba JokinPuig Cayuela, VicençEspin, SantiagoRoquet, JaumeIn this paper, a sensor data validation/reconstruction methodology applicable to water networks and its implementation by means of a software tool are presented. The aim is to guarantee that the sensor data are reliable and complete in case that sensor faults occur. The availability of such dataset is of paramount importance in order to successfully use the sensor data for further tasks e.g. water billing, network efficiency assessment, leak localization and real-time operational control. The methodology presented here is based on a sequence of tests and on the combined use of spatial models (SM) and time series models (TSM) applied to the sensors used for real-time monitoring and control of the water network. Spatial models take advantage of the physical relations between different system variables (e.g. flow and level sensors in hydraulic systems) while time series models take advantage of the temporal redundancy of the measured variables (here by means of a Holt–Winters (HW) time series model). First, the data validation approach, based on several tests of different complexity, is described to detect potential invalid or missing data. Then, the reconstruction process is based on a set of spatial and time series models used to reconstruct the missing/invalid data with the model estimation providing the best fit. A software tool implementing the proposed data validation and reconstruction methodology is also described. Finally, results obtained applying the proposed methodology to a real case study based on the Catalonia regional water network is used to illustrate its performance.Non-linear set-membership identification approach based on the Bayesian framework
http://hdl.handle.net/2117/83901
Non-linear set-membership identification approach based on the Bayesian framework
Fernández Canti, Rosa M.; Tornil Sin, Sebastián; Blesa Izquierdo, Joaquim; Puig Cayuela, Vicenç
This study deals with the problem of set-membership identification of non-linear-in-the-parameters models. To solve this problem, this study illustrates how the Bayesian approach can be used to determine the feasible parameter set (FPS) by assuming uniform distributed estimation error and flat model prior probability distributions. The key point of the methodology is the interval evaluation of the likelihood function and the result is a set of boxes with associated credibility indices. For each box, the credibility index is in the interval (0, 1] and gives information about the amount of consistent models inside the box. The union of the boxes with credibility value equal to one provides an inner approximation of the FPS, whereas the union of all boxes provides an outer estimation. The boxes with credibility value smaller than one are located around the boundary of the FPS and their credibility index can be used to iteratively refine the inner and outer approximations up to a desired precision. The main issues and performance of the developed algorithms are discussed and illustrated by means of examples.
Mon, 07 Mar 2016 15:49:29 GMThttp://hdl.handle.net/2117/839012016-03-07T15:49:29ZFernández Canti, Rosa M.Tornil Sin, SebastiánBlesa Izquierdo, JoaquimPuig Cayuela, VicençThis study deals with the problem of set-membership identification of non-linear-in-the-parameters models. To solve this problem, this study illustrates how the Bayesian approach can be used to determine the feasible parameter set (FPS) by assuming uniform distributed estimation error and flat model prior probability distributions. The key point of the methodology is the interval evaluation of the likelihood function and the result is a set of boxes with associated credibility indices. For each box, the credibility index is in the interval (0, 1] and gives information about the amount of consistent models inside the box. The union of the boxes with credibility value equal to one provides an inner approximation of the FPS, whereas the union of all boxes provides an outer estimation. The boxes with credibility value smaller than one are located around the boundary of the FPS and their credibility index can be used to iteratively refine the inner and outer approximations up to a desired precision. The main issues and performance of the developed algorithms are discussed and illustrated by means of examples.Fault detection and isolation for a wind turbine benchmark using a mixed Bayesian/set-membership approach
http://hdl.handle.net/2117/83898
Fault detection and isolation for a wind turbine benchmark using a mixed Bayesian/set-membership approach
Fernández Canti, Rosa M.; Blesa Izquierdo, Joaquim; Tornil Sin, Sebastián; Puig Cayuela, Vicenç
This paper addresses the problem of fault detection and isolation of wind turbines using a mixed Bayesian/Set-membership approach. Modeling errors are assumed to be unknown but bounded, following the set-membership approach. On the other hand, measurement noise is also assumed to be bounded, but following a statistical distribution inside the bounds. To avoid false alarms, the fault detection problem is formulated in a set-membership context. Regarding fault isolation, a new fault isolation scheme that is inspired on the Bayesian fault isolation framework is developed. Faults are isolated by matching the fault detection test results, enhanced by a complementary consistency index that measures the certainty of not being in a fault situation, with the structural information about the faults stored in the theoretical fault signature matrix. The main difference with respect to the classical Bayesian approach is that only models of fault-free behavior are used. Finally, the proposed FDI method is assessed against the wind turbine FDI benchmark proposed in the literature, where a set of realistic fault scenarios in wind turbines are proposed.
Mon, 07 Mar 2016 15:29:46 GMThttp://hdl.handle.net/2117/838982016-03-07T15:29:46ZFernández Canti, Rosa M.Blesa Izquierdo, JoaquimTornil Sin, SebastiánPuig Cayuela, VicençThis paper addresses the problem of fault detection and isolation of wind turbines using a mixed Bayesian/Set-membership approach. Modeling errors are assumed to be unknown but bounded, following the set-membership approach. On the other hand, measurement noise is also assumed to be bounded, but following a statistical distribution inside the bounds. To avoid false alarms, the fault detection problem is formulated in a set-membership context. Regarding fault isolation, a new fault isolation scheme that is inspired on the Bayesian fault isolation framework is developed. Faults are isolated by matching the fault detection test results, enhanced by a complementary consistency index that measures the certainty of not being in a fault situation, with the structural information about the faults stored in the theoretical fault signature matrix. The main difference with respect to the classical Bayesian approach is that only models of fault-free behavior are used. Finally, the proposed FDI method is assessed against the wind turbine FDI benchmark proposed in the literature, where a set of realistic fault scenarios in wind turbines are proposed.Dilated LMI characterization for the robust finite time control of discrete-time uncertain linear systems
http://hdl.handle.net/2117/83887
Dilated LMI characterization for the robust finite time control of discrete-time uncertain linear systems
Rotondo, Damiano; Nejjari Akhi-Elarab, Fatiha; Puig Cayuela, Vicenç
This paper provides new dilated linear matrix inequalities (LMIs) characterizations for the finite time boundedness (FTB) and the finite time stability (FTS) analysis of discrete-time uncertain linear systems. The dilated LMIs are later used to design a robust controller for the finite time control of discrete-time uncertain linear systems. The relevant feature of the proposed approach is the decoupling between the Lyapunov and the system matrices, that allows considering a parameter-dependent Lyapunov function. In this way, the conservativeness with respect to previous results is decreased. Numerical examples are used to illustrate the results.
Mon, 07 Mar 2016 13:58:25 GMThttp://hdl.handle.net/2117/838872016-03-07T13:58:25ZRotondo, DamianoNejjari Akhi-Elarab, FatihaPuig Cayuela, VicençThis paper provides new dilated linear matrix inequalities (LMIs) characterizations for the finite time boundedness (FTB) and the finite time stability (FTS) analysis of discrete-time uncertain linear systems. The dilated LMIs are later used to design a robust controller for the finite time control of discrete-time uncertain linear systems. The relevant feature of the proposed approach is the decoupling between the Lyapunov and the system matrices, that allows considering a parameter-dependent Lyapunov function. In this way, the conservativeness with respect to previous results is decreased. Numerical examples are used to illustrate the results.