Passive Fault Tolerant Control of Piecewise Affine Systems Based on H Infinity Synthesis

International audienceIn this paper we design a passive fault tolerant controller against actuator faults for discretetime piecewise affine (PWA) systems. By using dissipativity theory and H analysis, fault tolerant state feedback controller design is expressed as a set of Linear Matrix Inequalities (LMIs). In the current paper, the PWA system switches not only due to the state but also due to the control input. The method is applied on a large scale livestock ventilation model

Fault-Tolerant Control Based on Virtual Actuator and Sensor for Discrete-Time Descriptor Systems

This article proposes a fault-tolerant control (FTC) strategy based on virtual actuator and sensor for discrete-time descriptor systems subject to actuator and sensor faults. The fault-tolerant closed-loop system, which includes the nominal controller and observer, as well as the virtual actuator and the virtual sensor, hides the effects of faults. When an observer-based state-feedback law is considered, the existence of algebraic loop may prevent the practical implementation due to the current algebraic states depending on the current control input, that affects also the implementation of the virtual actuator/sensor. To deal with this issue, an observer-based delayed feedback controller and a delayed virtual actuator are proposed for discrete-time descriptor systems. Furthermore, the satisfaction of the separation principle is shown, and an improved admissibility condition is developed for the design of the controller and virtual actuator/sensor. Finally, some simulation results including an electrical circuit are used to demonstrate the applicability of the proposed methods.acceptedVersio

Fault tolerant control of uncertain dynamical systems using interval virtual actuators

This is the peer reviewed version of the following article: Rotondo D, Cristofaro A, Johansen TA. Fault tolerant control of uncertain dynamical systems using interval virtual actuators. Int J Robust Nonlinear Control. 2018;28:611–624, which has been published in final form at https://doi.org/10.1002/rnc.3888. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.In this paper, a model reference fault tolerant control strategy based on a reconfiguration of the reference model, with the addition of a virtual actuator block, is presented for uncertain systems affected by disturbances and sensor noise. In particular, this paper (1) extends the reference model approach to the use of interval state observers, by considering an error feedback controller, which uses the estimated bounds for the error between the real state and the reference state, and (2) extends the virtual actuator approach to the use of interval observers, which means that the virtual actuator is added to the control loop to preserve the nonnegativity of the interval estimation errors and the boundedness of the involved signals, in spite of the fault occurrence. In both cases, the conditions to assure the desired operation of the control loop are provided in terms of linear matrix inequalities. An illustrative example is used to show the main characteristics of the proposed approach.Peer ReviewedPostprint (author's final draft

Fault tolerant control of uncertain dynamical systems using interval virtual actuators

This is the peer reviewed version of the following article: Rotondo D, Cristofaro A, Johansen TA. Fault tolerant control of uncertain dynamical systems using interval virtual actuators. Int J Robust Nonlinear Control. 2018;28:611–624, which has been published in final form at https://doi.org/10.1002/rnc.3888. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.In this paper, a model reference fault tolerant control strategy based on a reconfiguration of the reference model, with the addition of a virtual actuator block, is presented for uncertain systems affected by disturbances and sensor noise. In particular, this paper (1) extends the reference model approach to the use of interval state observers, by considering an error feedback controller, which uses the estimated bounds for the error between the real state and the reference state, and (2) extends the virtual actuator approach to the use of interval observers, which means that the virtual actuator is added to the control loop to preserve the nonnegativity of the interval estimation errors and the boundedness of the involved signals, in spite of the fault occurrence. In both cases, the conditions to assure the desired operation of the control loop are provided in terms of linear matrix inequalities. An illustrative example is used to show the main characteristics of the proposed approach.Peer ReviewedPostprint (author's final draft

Deep Learning-Based, Passive Fault Tolerant Control Facilitated by a Taxonomy of Cyber-Attack Effects

In the interest of improving the resilience of cyber-physical control systems to better operate in the presence of various cyber-attacks and/or faults, this dissertation presents a novel controller design based on deep-learning networks. This research lays out a controller design that does not rely on fault or cyber-attack detection. Being passive, the controller’s routine operating process is to take in data from the various components of the physical system, holistically assess the state of the physical system using deep-learning networks and decide the subsequent round of commands from the controller. This use of deep-learning methods in passive fault tolerant control (FTC) is unique in the research literature. The proposed controller is applied to both linear and nonlinear systems. Additionally, the application and testing are accomplished with both actuators and sensors being affected by attacks and /or faults

Data-driven techniques for the fault diagnosis of a wind turbine benchmark

This paper deals with the fault diagnosis of wind turbines and investigates viable solutions to the problem of earlier fault detection and isolation. The design of the fault indicator, i.e., the fault estimate, involves data-driven approaches, as they can represent effective tools for coping with poor analytical knowledge of the system dynamics, together with noise and disturbances. In particular, the proposed data-driven solutions rely on fuzzy systems and neural networks that are used to describe the strongly nonlinear relationships between measurement and faults. The chosen architectures rely on nonlinear autoregressive models with exogenous input, as they can represent the dynamic evolution of the system along time. The developed fault diagnosis schemes are tested by means of a high-fidelity benchmark model that simulates the normal and the faulty behaviour of a wind turbine. The achieved performances are also compared with those of other model-based strategies from the related literature. Finally, a Monte-Carlo analysis validates the robustness and the reliability of the proposed solutions against typical parameter uncertainties and disturbances.This paper deals with the fault diagnosis of wind turbines and investigates viable solutions to the problem of earlier fault detection and isolation. The design of the fault indicator, i.e., the fault estimate, involves data-driven approaches, as they can represent effective tools for coping with poor analytical knowledge of the system dynamics, together with noise and disturbances. In particular, the proposed data-driven solutions rely on fuzzy systems and neural networks that are used to describe the strongly nonlinear relationships between measurement and faults. The chosen architectures rely on nonlinear autoregressive models with exogenous input, as they can represent the dynamic evolution of the system along time. The developed fault diagnosis schemes are tested by means of a high-fidelity benchmark model that simulates the normal and the faulty behaviour of a wind turbine. The achieved performances are also compared with those of other model-based strategies from the related literature. Finally, a Monte-Carlo analysis validates the robustness and the reliability of the proposed solutions against typical parameter uncertainties and disturbances