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Publications de l'équipe DISCO

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17112
01/12/2017

Integrated vehicle control through the coordination of longitudinal/lateral and vertical dynamics controllers: Flatness and LPV/H ∞ based design

S.FERGANI, L.MENHOUR, O.SENAME, L.DUGARD, B.DANDREA NOVEL

DISCO, URCA, GIPSA-Lab, Mines ParisTech

Revue Scientifique : International Journal of Robust and Nonlinear Control, Vol.27, N°18, pp.4992-5007, Décembre 2017, doi 10.1002/rnc.3846 , N° 17112

Lien : https://hal.archives-ouvertes.fr/hal-01509787

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Abstract

This paper deals with Global Chassis Control (GCC) of ground vehicles. It focuses on the coordination of suspensions and steering/braking vehicle controllers based on the interaction between the vertical and lateral behaviors of the vehicle. Indeed, the roll motion of the car can generate increasing load transfers that affect considerably the suspension system and vehicle stability. The load transfers can be described using the lateral acceleration. Then, the coordination is highlighted, in this work, through the relationship between the suspension behavior and the lateral acceleration in the framework of the Linear Paramter Varying (LPV) approach. The proposed control law is designed in hierarchical way to improve the overall dynamics of the vehicle. This global control strategy includes two types controllers. The first one is the longitudinal/lateral nonlinear Flatness controller. Based on the adequate choice of the flat outputs, the flatness proof of a 3DoF two wheels nonlinear vehicle model has been established. Then, the combined longitudinal and lateral vehicle control is designed. The algebraic estimation techniques have been used in order to have an accuracy estimation of the derivatives and filtering of the reference flat outputs. Such control strategy is developed in order to cope with coupled driving maneuvers like obstacle avoidance via steering control and stop-and-go control via braking or driving wheel torque. The second part of the proposed strategy consists of the LP V /H∞ suspension controller. This controller uses the lateral acceleration as a varying parameter to take into account the load transfers that affects directly the suspension system and therefore to achieve the desired performance. The coordination between the vehicle vertical and lateral dynamics is highlighted in this study, and the LP V /H∞ framework ensures a specific collaborative coordination between the suspension and the steering/braking controllers. Simulations on a complex full vehicle model have been validated using experimental data obtained on-board vehicle, with an identification procedure on a real Renault Mégane Coupé.

141739
17155
17/11/2017

Diagnostic de motifs de comportements dans les systèmes temporels

Y.PENCOLE, A.SUBIAS

DISCO

Manifestation avec acte : Modélisation des Systèmes Réactifs ( MSR ) 2017 du 15 novembre au 17 novembre 2017, Marseille (France), Novembre 2017, 14p. , N° 17155

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141592
16125
24/10/2017

Root cause analysis of actuator fault based on invertibility of interconnected system

M.ZHANG, Z.LI, M.CABASSUD, B.DAHHOU

LGC, GUIZHOU, DISCO

Revue Scientifique : International Journal of Modelling, Identification and Control, Vol.27, N°4, pp.256-270, Octobre 2017 , N° 16125

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Abstract

This paper addresses the problem of root cause analysis (RCA) of actuator fault. By considering an actuator as an individual dynamic subsystem connected with process dynamic subsystem in cascade, an interconnected system is then constituted. The fault detection and diagnosis (FDD) algorithm is carried out in actuator subsystem and aims at identifying the root causes of actuator faults. According to real plant, outputs of the actuator subsystem are assumed inaccessible and are reconstructed by measurements of the global system, thus providing a means of monitoring and diagnosis of the plant at both local and global level. A condition of invertibility of the interconnected system is first developed to guarantee that faults occurring in the actuator subsystem will affect the measured output of the global system distinguishably. For that, a necessary and sufficient condition is proposed to ensure invertibility of the interconnected system. Effectiveness of the proposed approach is demonstrated on an intensified HEX reactor.

141340
17373
18/10/2017

Alarm management via temporal pattern learning

J.VASQUEZ, A.SUBIAS, L.TRAVE-MASSUYES, F.JIMENEZ

DISCO, Univ. de Los Andes

Revue Scientifique : Engineering Applications of Artificial Intelligence, Vol.65, pp.506-516, Octobre 2017 , N° 17373

Lien : https://hal.laas.fr/hal-01611635

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Abstract

Industrial plant safety involves integrated management of all the factors that may cause accidents. Process alarm management can be formulated as a pattern recognition problem in which temporal patterns are used to characterize different typical situations, particularly at startup and shutdown stages. In this paper we propose a new approach of alarm management based on a diagnosis process. Assuming the alarms and the actions of the standard operating procedure as discrete events, the diagnosis step relies on situation recognition to provide the operators with relevant information on the failures inducing the alarm flows. The situation recognition is based on chronicle recognition where we propose to use the hybrid causal model of the system and simulations to generate the representative event sequences from which the chronicles are learned using the Heuristic Chronicle Discovery Algorithm Modified (HCDAM). An extension of this algorithm is presented in this article where the expertise knowledge is included as temporal restrictions which are a new input to HCDAM. An illustrative example in the field of petrochemical plants is presented.

141255
17411
13/10/2017

Chronicle based alarm management

J.VASQUEZ

DISCO

Doctorat : INSA de Toulouse, 13 Octobre 2017, 170p., Président: F.MUNOZ, Rapporteurs: C.V.ISAZA, M.LE GOC, Examinateurs: L.TRAVE-MASSUYES, Directeurs de thèse: A.SUBIAS, F.JIMENEZ , N° 17411

Lien : https://hal.laas.fr/tel-01660820

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Résumé

Ce travail de thèse a été réalisé dans le cadre d’une thèse en co-tutelle entre l’INSA, Toulouse, et l’Université des Andes, Colombie, avec un financement de Colciencias. Ce travail est motivé par la nécessité pour l’industrie de détecter des situations anormales pendant les phases de démarrage et d’arrêt des installations. La sécurité des installations industrielles implique une gestion intégrée de tous les facteurs et événements pouvant causer des accidents. La gestion des alarmes peut être formulée comme un problème de reconnaissance de motifs événementiels dans lequel des modèles temporels sont utilisés pour caractériser différentes situations typiques, en particulier pendant les phases de démarrage et d’arrêt. Dans cette thèse une nouvelle approche de gestion des alarmes basée sur un processus de diagnostic est proposée. En supposant que les alarmes et les actions du mode opératoire standard sont des événements discrets, l’étape de diagnostic repose sur la reconnaissance de situation pour fournir aux opérateurs des informations pertinentes sur les défaillances induisant le flux d’alarmes. La reconnaissance de situation est basée sur des chroniques qui caractérisent les situations d’interdit et qui sont apprises de manière automatique. Les chroniques sont apprises à partir de séquences d’événements représentatives obtenues par simulation et constituant l’entrée d’une version étendue de l’Algorithme de Découverte de Chroniques Heuristique Modifié (HCDAM). HCDAM a été étendu dans cette thèse pour prendre en compte des connaissances expertes sous la forme de restrictions temporelles spécifiques. Un modèles hybride causal du procédé est utilisé pour vérifier les séquences d’entrée et pour expliquer et donner du sens aux chroniques apprises. La méthodologie de gestion des alarmes basée sur des chroniques CBAM (comme Chronicle Based Alarm Management ) proposée dans cette thèse fusionne différentes techniques pour tenir compte de l’aspect hybride et des procédures opérationnelles standard des processus concernés. Comparée aux autres approches de gestion d’alarmes, cette approche se caractérise par l’utilisation de l’information sur les actions procédurales liées au comportement des variables continues dans un processus formel de diagnostic. Des informations spécifiques sont obtenues à chaque étape de la méthodologie CBAM qui se résume en trois étapes : 1. Étape 1 : Identification du type d’événement à partir des procédures d’exploitation standard et de l’évolution des variables continues, cette étape détermine l’ensemble des types d’événements pendant les phases de démarrage et d’arrêt. 2. Étape 2 : Génération de séquence d’événements à partir de l’expertise et d’une procédure d’abstraction événementielle, cette étape détermine la date d’apparition de chaque type d’événement pour la construction des séquences d’événements représentatives. Une séquence d’événements représentatifs est l’ensemble des types d’événements avec leurs dates d’occurrence qui peuvent être associées à un scénario spécifique du processus. Cette étape se conclut avec la vérification des séquences d’événements représentatives à l’aide du modèle causal hybride. 3. Étape 3 : Construction de la base de chroniques à partir des séquences d’événements représentatives et des restrictions temporelles dans chaque scénario, cette étape détermine la base de chroniques à l’aide de l’algorithme HCDAM. La methode proposée pour la gestion des alarmes est illustrée par deux cas d’etude representatifs du domaine pétrochimique.

Abstract

This thesis work was carried out in the framework of a co-tutelle between INSA, Toulouse, and the University of the Andes, Colombia, with financial support of Colciencias. This work is motivated by the need of the industry to detect abnormal situations in the plant startup and shutdown stages. Industrial plants involve integrated management of all the events that may cause accidents and translate into alarms. Process alarm management can be formulated as an event-based pattern recognition problem in which temporal patterns are used to characterize different typical situations, particularly at startup and shutdown stages. In this thesis, a new approach for alarm management based on a diagnosis process is proposed. Considering the alarms and the actions of the standard operating procedure as discrete events, the diagnosis step relies on situation recognition to provide the operators with relevant information about the failures inducing the alarm flow. The situation recognition is based on chronicles that characterize the situations of interest and are learned automatically. The chronicles are learned from representative event sequences obtained by simulation and given as input to an extended version of the Heuristic Chronicle Discovery Algorithm Modified (HCDAM). HCDAM has been extended in this thesis to account for expert knowledge in the form of specific temporal restrictions. A hybrid causal model of the process is used to verify the input event sequences and to explain and provide semantics to the learned chronicles. The Chronicle Based Alarm Management (CBAM) methodology proposed in this thesis involves different techniques to take the hybrid aspect and the standard operational procedures of the concerned processes into account. Compared to other approaches of alarm management, this approach uses information about the procedural actions related to the continuous variables behavior in a formal diagnosis process. Specific information is obtained in each step of the CBAM methodology, and it is summarized in three steps: 1. Step 1: Event type identification From the standard operating procedures and from the evolution of the continuous variables, this step determines the set of event types in startup and shutdown stages. 2. Step 2: Event sequence generation From the expertise and an event abstraction procedure this step determines the date of occurrence of each event type for constructing the representative event sequences. A representative event sequence is the set of event types with their dates of occurrence that can be associated to a specific scenario of the process. This step concludes verifying the representative event sequences using the hybrid causal graph. 3. Step 3: Chronicle database construction From the representative event sequences and temporal restrictions of each scenario, this step determines the chronicle database using the extended HCDAM algorithm. The proposed framework for alarm management is illustrated with two case studies representative of the petrochemical field.

141513
17408
05/10/2017

HPPN-based prognosis for hybrid systems

P.RIBOT, E.CHANTHERY, Q.GAUDEL

DISCO

Manifestation avec acte : Annual Conference of the Prognostics and Health Management Society ( PHM ) 2017 du 02 octobre au 05 octobre 2017, St Petersbourg (USA), Octobre 2017, 10p. , N° 17408

Lien : https://hal.archives-ouvertes.fr/hal-01579483

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Abstract

This paper presents a model-based prognosis method for hybrid systems i.e. that have both discrete and continuous behaviors. The current state of the hybrid system is estimated by a diagnosis process and the prognosis process uses this state estimation to predict the future states and to determine the end of life (EOL) or the remaining useful life (RUL) of the system. The Hybrid Particle Petri Nets (HPPN) formalism is used to model the hybrid system behavior and degradation. A HPPN-based diagnoser has already been defined to provide a current state estimation that takes uncertainty about the system model and observations into account. We propose to generate a prognoser from the HPPN model of the system. This prognoser is initialized and updated with the result of the HPPN-based diagnoser. It computes a distribution of beliefs over the future mode trajectories of the system and predicts the system RUL/EOL. The prognosis methodology is demonstrated on a three tanks example.

141456
13403
01/10/2017

Improved solutions for ill-conditioned problems involved in set-membership estimation for fault detection and isolation

L.RAVANBOD, C.JAUBERTHIE, N.VERDIERE, L.TRAVE-MASSUYES

Université du Havre, DISCO

Revue Scientifique : Journal of process control, Vol.58, pp.139-151, Octobre 2017 , N° 13403

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Abstract

Set-membership (SM) estimation implies that the computed solution sets are guaranteed to contain all the feasible estimates consistent with the bounds specified in the model. Two issues often involved in the solution of SM estimation problems and their application to engineering case studies are considered in this paper. The first one is the estimation of derivatives from noisy signals, which in a bounded uncertainty framework means obtaining an enclosure by lower and upper bounds. In this paper, we improve existing methods for enclosing derivatives using Higher-Order Sliding Modes (HOSM) differentiators combining filtering. Our approach turns the use of high order derivatives more efficiently especially when the signal to differentiate has slow dynamics. The second issue of interest is solving linear interval equation systems, which is often an ill-conditioned problem. This problem is reformulated as a Constraint Satisfaction Problem and solved by the combination of the constraint propagation Forward Backward algorithm and the SIVIA algorithm. The two proposed methods are tested on illustrative examples. The two methods are then used in a fault detection and isolation algorithm based on SM parameter estimation that is applied to detect abnormal parameter values in a biological case study.

140912
17154
29/09/2017

Diagnosis of supervision patterns on bounded labeled Petri nets by model checking

Y.PENCOLE, A.SUBIAS

DISCO

Manifestation avec acte : International Workshop on Principles of Diagnosis ( DX ) 2017 du 26 septembre au 29 septembre 2017, Brescia (Italie), Septembre 2017, 9p. , N° 17154

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141133
17153
29/09/2017

A learning algorithm for episodes

T;OBRY, A.SUBIAS, L.TRAVE-MASSUYES

DISCO

Manifestation avec acte : International Workshop on Principles of Diagnosis ( DX ) 2017 du 26 septembre au 29 septembre 2017, Brescia (Italie), Septembre 2017, 5p. , N° 17153

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141134
17327
26/09/2017

Chronicle modeling and analysis for diagnosis

Y.PENCOLE, A.SUBIAS

DISCO

Rapport LAAS N°17327, Septembre 2017, 33p.

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140996
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