Sensor-fault tolerance using robust MPC with set-based state estimation and active fault isolation

In this paper, a sensor fault-tolerant control (FTC) scheme using robust model predictive control (MPC) and set theoretic fault detection and isolation (FDI) is proposed. The MPC controller is used to both robustly control the plant and actively guarantee fault isolation (FI). In this scheme, fault detection (FD) is passive by interval observers, while fault isolation (FI) is active by MPC. The advantage of the proposed approach consists in using MPC to actively decouple the effect of sensor faults on the outputs such that one output component only corresponds to one sensor fault in terms of FI, which can utilize the feature of sensor faults for FI. A numerical example is used to illustrate the effectiveness of the proposed scheme.Postprint (author’s final draft

Set-valued observer-based active fault-tolerant model predictive control

This paper proposes an integrated actuator and sensor active fault-tolerant model predictive control scheme. In this scheme, fault detection is implemented by using a set-valued observer, fault isolation (FI) is performed by set manipulations, and fault-tolerant control is carried out through the design of a robust model predictive control law. In this paper, a set-valued observer is used to passively complete the fault detection task, while FI is actively performed by making use of the constraint-handling capability of robust model predictive control. The set-valued observer is chosen to implement fault detection and isolation (FDI) because of its simple mathematical structure that is not affected by the type of faults such as sensor, actuator, and system-structural faults. This means that only one set-valued observer is needed to monitor all considered actuator and sensor statuses (health and fault) and to carry out the fault detection and isolation task instead of using a bank of observers (each observer matching a health/fault status). Furthermore, in the proposed scheme, the advantage of robust model predictive control is that it can effectively deal with system constraints, disturbances, and noises and allow to implement an active FI strategy, which can improve FI sensitivity when compared with the passive FI methods. Finally, a case study based on the well-known two-tank system is used to illustrate the effectiveness of the proposed fault-tolerant model predictive control scheme.Peer ReviewedPostprint (author's final draft

Actuator fault-tolerant control based on set separation

In this paper, an actuator fault-tolerant control (FTC) strategy based on set separation is presented. The proposed scheme employs a standard configuration consisting of a bank of observers which match the different fault situations that can occur in the plant. Each of these observers has an associated estimation error with a distinctive behaviour when a estimator matches the current fault situation of the plant. With this information from each observer, a fault diagnosis and isolation (FDI) module is able to reconfigure the control loop by selecting the appropriate stabilising controller from a bank of precomputed control laws, each of them related to one of the considered fault models. The control law consists of a reference feedforward term and a feedback gain multiplying the state estimate provided by the matching observer. A novel feature of the proposed scheme resides in the decision criteria of the FDI, which is based on the computation of sets towards which the output estimation errors related to each fault scenario and for each control configuration converge. Conditions for the design of the FDI module and for fault tolerant closed-loop stability are given, and the effectiveness of the approach is illustrated by means of a numerical examplePostprint (published version

The INOVE ANR 2010 Blan 0308 project: Integrated approach for observation and control of vehicle dynamics

International audienceThis paper presents the INOVE "Integrated approach for observation and control of vehicle dynamics" project. The aim and organization of the project are described and we present some recent results on the proposed integrated approach to design new methodologies for the improvement of the vehicle dynamical behaviour

An annotated bibligraphy of multisensor integration

technical reportIn this paper we give an annotated bibliography of the multisensor integration literature

Observer-based sensor fault detectability: about robust positive invariance approach and residual sensitivity

© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/This paper considers detectability of deviation of sensors from their nominal behavior for a class of linear time-invariant discrete-time systems in the presence of bounded additive uncertainties. Detectable sensor faults using interval observers are analyzed considering two distinct approaches: invariant-sets and classical fault-sensitivity method. It can be inferred from this analysis that both approaches derive distinct formulations for minimum detectable fault magnitude, though qualitatively similar. The core difference lies in the method of construction of the invariant set offline in the former method and the reachable approximation of the convergence set using forward iterative techniques in the latter. This paper also contributes in giving a formulation for minimum fault magnitudes with invariant sets using an observer-based approach. Finally, an illustrative example is used to compare both approaches.Peer ReviewedPostprint (author's final draft

Fault detection and isolation using viability theory and interval observers

This paper proposes the use of interval observers and viability theory in fault detection and isolation (FDI). Viability theory develops mathematical and algorithmic methods for investigating the viability constraints characterisation of dynamic evolutions of complex systems under uncertainty. These methods can be used for checking the consistency between observed and predicted behaviour by using simple sets that approximate the exact set of possible behaviour (in the parameter or state space). In this paper, FDI is based on checking for an inconsistency between the measured and predicted behaviours using viability theory concepts and sets. Finally, an example is provided in order to show the usefulness of the proposed approachPeer ReviewedPostprint (author's final draft

Model-based H2/H1 control design of integrated vehicle tracking systems

The paper presents the design of an integrated heavy vehicle system which consists of the driveline, the brake, the suspension and the steering components. The purpose of the integration is to create an adaptive cruise control which is able to keep the distance from the preceding vehicle and track the path on roads. The vehicle model consists of the dynamics of the sprung mass and the unsprung masses and handles the effects of road and wind disturbances. The design of the adaptive cruise-control system is based on the H2/H∞ control method. The operation of the controlled system is illustrated through simulation examples

Diagnosis and fault-tolerant control using set-based methods

[EN]: The fault-tolerant capability is an important performance specification for technical systems. Examples showing its importance are some catastrophes in civil aviation. According to official investigations, some air accidents due to failures are technically avoidable if the pilots can take right measures. But, relying on the skill and experience of the pilots, it cannot be guaranteed that reliable flight decisions are always made. Instead, if fault-tolerant strategies can be included in the decision-making procedure, it will be very useful for safer flight. Fault-tolerant control is generally classified into passive and active fault-tolerant control. Passive fault-tolerant control relies on the robustness of the controller, which can only provide limited fault-tolerant ability, while active fault-tolerant control turns to a fault detection and isolation module to obtain fault information and then to actively take actions to tolerate the effect of faults. Generally, active fault-tolerant control has more powerful fault-tolerant ability than passive fault-tolerant control. In this dissertation, one focuses on active fault-tolerant control, which for this case considers model predictive control and set-based fault detection and isolation. Model predictive control is a successful advanced control strategy in process industry and has been widely used for processes such as chemistry and water treatment, because of its ability to deal with multivariable constrained systems. However, the performance of model predictive control has deep dependence on model accuracy. Realistically, it is impossible to avoid the effect of modelling errors, disturbances, noises and faults, which always result in model mismatch. Comparatively, model mismatch induced by faults is possible to be effectively handled by suitable fault-tolerant strategies. The objective of this dissertation is to endow model predictive control with faulttolerant ability to improve its effectiveness. In order to reach this objective, set-based fault detection and isolation methods are used in the proposed fault-tolerant schemes. The important advantage of set-based fault detection and isolation is that it can make robust fault detection and isolation decisions, which is the key for taking right fault-tolerant measures. This dissertation includes four parts. The first part introduces this research, presents the state of the art and gives an introduction of used research tools. The second part proposes setbased fault detection and isolation for actuator and sensor faults, which is involved in interval observers and invariant sets. In the second part, the relationship between interval observers and invariant sets is firstly investigated. Then, actuator and sensor faults are separately coped with depending on their own features. The third part focuses on actuator and sensor fault-tolerant model predictive control, where the control strategy is robust model predictive control. The last part draws some conclusions, summarizes this research and gives clues for the future work.[ES]: La capacidad de los sistemas para tolerar fallos es una importante especificación de desempeño para la mayoría de sistemas. Ejemplos que muestran su importancia son algunas catástrofes en aviación civil. De acuerdo a investigaciones oficiales, algunos incidentes aéreos son técnicamente evitables si los pilotos pudiesen tomar las medidas adecuadas. Aun así, basándose en las habilidades y experiencia de los pilotos, no se puede garantizar que decisiones de vuelo confiables serán siempre posible de tomar. En cambio, si estrategias de tolerancia a fallos se pudieran incluir en el proceso de toma de decisión, los vuelos serían mucho más seguros. El control tolerante a fallos es generalmente clasificado en control pasivo y activo. El control pasivo se basa en la robustez del controlador, el cual sólo provee una habilidad limitada de tolerancia a fallos, mientras que el control tolerante a fallos de tipo activo se convierte en un modulo de detección y aislamiento de fallos que permite obtener información de éstos, y luego, activamente, tomar acciones para tolerar el efecto de dichos fallos. Así pues, el control activo generalmente tiene habilidades más fuertes de tolerancia a fallos. Esta tesis se enfoca en control tolerante a fallos activo, para lo cual considera el control predictivo basado en modelos y la detección y aislamiento de fallos basados en conjuntos. El control predictivo basado en modelos es una estrategia de control exitosa en la industria de procesos y ha sido ampliamente utilizada para procesos químicos y tratamiento de aguas, debido a su habilidad de tratar con sistemas multivariables con restricciones. A pesar de esto, el desempeño del control predictivo basado en modelos tiene una profunda dependencia de la precisión del modelo del sistema. Siendo realistas, es imposible evitar el efecto de errores de modelado, perturbaciones, ruidos y fallos, que siempre llevan a diferencias entre el modelo y el sistema real. Comparativamente, el error de modelo inducido por los fallos es posible de ser manejado efectivamente por estrategias adecuadas de control tolerante a fallos. Con el fin de alcanzar este objetivo, métodos de detección y aislamiento de fallos basados en conjuntos son utilizados en los esquemas de tolerancia a fallos propuestos en esta tesis. La ventaja importante de estas técnicas de detección y aislamiento de fallos basadas en conjuntos es que puede tomar decisiones robustas de detección y aislamiento, lo cual es clave para tomar medidas acertadas de tolerancia a fallos. Esta tesis esta dividida en cuatro partes. La primera parte es introductoria, presenta el estado del arte y hace una introducción a las herramientas de investigación utilizadas. La segunda parte expone la detección y aislamiento de fallos en actuadores y/o sensores, basándose en teoría de conjuntos, a partir de observadores de intervalo, y conjuntos invariantes. La tercera parte se enfoca en el control predictivo robusto (con enfoques basados tanto en tubos robustos como en min-max) con tolerancia a fallos en actuadores y/o sensores. La cuarta parte presenta algunas conclusiones, hace un resume de esta investigación y da algunas ideas para trabajos futuros.I want to thank the projects/grants that partially finance my research. They are the Spanish research projects CICYT SHERECS DPI-2011-26243 and WATMAN (DPI2009-13744) of the Science and Technology Ministry, the DGR of Generalitat de Catalunya (SAC group Ref. 2009/SGR/1491) and the contract i-Sense (FP7-ICT-2009-6-270428) by the European Commission.Peer Reviewe

Diagnosis and fault-tolerant control using set-based methods

The fault-tolerant capability is an important performance specification for most of technical systems. The examples showing its importance are some catastrophes in civil aviation. According to some official investigations, some air incidents are technically avoidable if the pilots can take right measures. But, relying on the skill and experience of the pilots, it cannot be guaranteed that reliable flight decisions are always made. Instead, if fault-tolerant strategies can be included in the decision-making procedure, it will be very useful for safer flight. Fault-tolerant control is generally classified into passive and active fault-tolerant control. Passive fault-tolerant control relies on robustness of controller, which can only provide limited fault-tolerant ability, while active fault-tolerant control turns to a fault detection and isolation module to obtain fault information and then actively take actions to tolerate the effect of faults. Thus, active fault-tolerant control generally has stronger fault-tolerant ability. In this dissertation, one focuses on active fault-tolerant control, which for this case considers model predictive control and set-based fault detection and isolation. Model predictive control is a successful advanced control strategy in process industry and has been widely used for processes such as chemistry and water treatment, because of its ability to deal with multivariable constrained systems. However, the performance of model redictive control has deep dependence on system-model accuracy. Realistically, it is impossible to avoid the effect of modelling errors, disturbances, noises and faults, which always result in model mismatch. Comparatively, model mismatch induced by faults is possible to be effectively handled by suitable fault-tolerant strategies. The objective of this dissertation is to endow model predictive control with fault-tolerant ability to improve its effectiveness. In order to reach this objective, set-based fault detection and isolation methods are used in the proposed fault-tolerant schemes. The important advantage of set-based fault detection and isolation is that it can make robust fault detection and isolation decisions, which is key for taking right fault-tolerant measures. This dissertation includes four parts. The first part introduces this research, presents the state of the art and gives an introduction of used research tools. The second part proposes set-based fault detection and isolation for actuator or=and sensor faults, which are involved in interval observers, invariant sets and set-membership estimation. First, the relationship between interval observers and invariant sets is investigated. Then, actuator and sensor faults are separately coped with depending on their own features. The third part focuses on actuator or=and sensor fault-tolerant model predictive control, where the control strategy is robust model predictive control (tube-based and min-max approaches). The last part draws some conclusions, summarizes this research and gives clues for the further work.La capacidad de los sistemas para tolerar fallos es una importante especificación de desempeño para la mayoría de sistemas. Ejemplos que muestran su importancia son algunas catástrofes en aviación civil. De acuerdo a investigaciones oficiales, algunos incidentes aéreos son técnicamente evitables si los pilotos pudiesen tomar las medidas adecuadas. Aun así, basándose en las habilidades y experiencia de los pilotos, no se puede garantizar que decisiones de vuelo confiables serán siempre posible de tomar. En cambio, si estrategias de tolerancia a fallos se pudieran incluir en el proceso de toma de decisión, los vuelos serían mucho más seguros. El control tolerante a fallos es generalmente clasificado en control pasivo y activo. El control pasivo se basa en la robustez del controlador, el cual sólo provee una habilidad limitada de tolerancia a fallos, mientras que el control tolerante a fallos de tipo activo se convierte en un modulo de detección y aislamiento de fallos que permite obtener información de éstos, y luego, activamente, tomar acciones para tolerar el efecto de dichos fallos. Así pues, el control activo generalmente tiene habilidades más fuertes de tolerancia a fallos. Esta tesis se enfoca en control tolerante a fallos activo, para lo cual considera el control predictivo basado en modelos y la detección y aislamiento de fallos basados en conjuntos. El control predictivo basado en modelos es una estrategia de control exitosa en la industria de procesos y ha sido ampliamente utilizada para procesos químicos y tratamiento de aguas, debido a su habilidad de tratar con sistemas multivariables con restricciones. A pesar de esto, el desempeño del control predictivo basado en modelos tiene una profunda dependencia de la precisión del modelo del sistema. Siendo realistas, es imposible evitar el efecto de errores de modelado, perturbaciones, ruidos y fallos, que siempre llevan a diferencias entre el modelo y el sistema real. Comparativamente, el error de modelo inducido por los fallos es posible de ser manejado efectivamente por estrategias adecuadas de control tolerante a fallos. Con el fin de alcanzar este objetivo, métodos de detección y aislamiento de fallos basados en conjuntos son utilizados en los esquemas de tolerancia a fallos propuestos en esta tesis. La ventaja importante de estas técnicas de detección y aislamiento de fallos basadas en conjuntos es que puede tomar decisiones robustas de detección y aislamiento, lo cual es clave para tomar medidas acertadas de tolerancia a fallos. Esta tesis esta dividida en cuatro partes. La primera parte es introductoria, presenta el estado del arte y hace una introducción a las herramientas de investigación utilizadas. La segunda parte expone la detección y aislamiento de fallos en actuadores y/o sensores, basándose en teoría de conjuntos, a partir de observadores de intervalo, y conjuntos invariantes. La tercera parte se enfoca en el control predictivo robusto (con enfoques basados tanto en tubos robustos como en min-max) con tolerancia a fallos en actuadores y/o sensores. La cuarta parte presenta algunas conclusiones, hace un resumen de esta investigación y da algunas ideas para trabajos futuros