Bounded State Reconstruction Error for LPV Systems With Estimated Parameters

International audienceThis note deals with the state reconstruction of a class of discrete-time systems with time-varying parameters. While usually the parameters are assumed to be online available and exactly known, the special and realistic situation when the parameters are known with a finite accuracy is considered. The main objective of the note is to show that, despite of the resulting mismatch between the true system and the model, the state reconstruction error boundedness can be guaranteed and an explicit bound can be derived. The proof is based upon the concept of input-to-state stability

Fault estimation and active fault tolerant control for linear parameter varying descriptor systems

Starting with the baseline controller design, this paper proposes an integrated approach of active fault tolerant control based on proportional derivative extended state observer (PDESO) for linear parameter varying descriptor systems. The PDESO can simultaneously provide the estimates of the system states, sensor faults, and actuator faults. The L₂ robust performance of the closed-loop system to bounded exogenous disturbance and bounded uncertainty is achieved by a two-step design procedure adapted from the traditional observer-based controller design. Furthermore, an LMI pole-placement region and the L₂ robustness performance are combined into a multiobjective formulation by suitably combing the appropriate LMI descriptions. A parameter-varying system example is given to illustrate the design procedure and the validity of the proposed integrated design approach

Fault detection in uncertain LPV systems with imperfect scheduling parameter using sliding mode observers

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record.This paper presents a sliding mode fault detection scheme for linear parameter varying (LPV) systems with uncertain or imperfectly measured scheduling parameters. In the majority of LPV systems, it is assumed that the scheduling parameters are exactly known. In reality due to noise or possibly faulty sensors, it is sometimes impossible to have accurate knowledge of the scheduling parameters and a design based on the assumption of perfect knowledge of the scheduling parameters cannot be guaranteed to work well in this situation. This paper proposes a sliding mode observer scheme to reconstruct actuator and sensor faults in a situation where the scheduling parameters are imperfectly known. The efficacy of the approach is demonstrated on simulation data taken from the nonlinear RECONFIGURE benchmark model.This work is supported by the EU-FP7 Grant (FP7-AAT-2012-314544

Sensor Fault Estimation Using LPV Sliding Mode Observers with Erroneous Scheduling Parameters

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.This paper proposes a linear parameter-varying sliding mode observer for the purpose of simultaneously estimating the system states and reconstructing sensor faults. Furthermore, some of the measured scheduling parameters are also assumed to be unreliable, and the corresponding values used in the observer are adapted to maintain the performance level of the observer. The adaptive algorithm is driven by the ‘equivalent output error injection’ signal associated with the reduced-order sliding motion. Sufficient conditions are given to ensure asymptotic stability of the state estimation error system, ensuring both the state estimation errors and the estimation errors associated with the scheduling parameters converge to zero. The efficacy of the scheme has been evaluated based upon an industrial high-fidelity aircraft benchmark scenario involving a simultaneous total loss of airspeed and angle of attack measurements

Sensor redundancy based FDI using an LPV sliding mode observer

This is the author accepted manuscript. The final version is available from IET via the DOI in this record.In this paper, a linear parameter varying (LPV) sliding mode sensor fault detection and isolation (FDI) scheme is proposed wherein knowledge of the measurement redundancy is utilised to achieve FDI in multiple channels simultaneously. Such a situation is common in some state-of-the-art aircraft fault diagnosis systems where information is generally/mainly measured based on triplex redundancy. The scheme proposed in this paper is based on an LPV sliding mode observer and exploits the so-called equivalent output error injection signal to create estimates of the measurement faults. In the case of sensor measurement redundancy, and where there exists a fault free (but unknown) sensor amongst the set of measurements, the fault reconstruction performance of the observer can be improved by isolating and using the output error injection signal associated with the fault free redundant sensor. Simulation results using the RECONFIGURE benchmark model demonstrate the effectiveness of the schemeThis work is supported by the EU Grant (FP7-AAT-2012-314544): RECONFIGUR

Robust fault tolerant control of induction motor system

Research into fault tolerant control (FTC, a set of techniques that are developed to increase plant availability and reduce the risk of safety hazards) for induction motors is motivated by practical concerns including the need for enhanced reliability, improved maintenance operations and reduced cost. Its aim is to prevent that simple faults develop into serious failure. Although, the subject of induction motor control is well known, the main topics in the literature are concerned with scalar and vector control and structural stability. However, induction machines experience various fault scenarios and to meet the above requirements FTC strategies based on existing or more advanced control methods become desirable. Some earlier studies on FTC have addressed particular problems of 3-phase sensor current/voltage FTC, torque FTC, etc. However, the development of these methods lacks a more general understanding of the overall problem of FTC for an induction motor based on a true fault classification of possible fault types.In order to develop a more general approach to FTC for induction motors, i.e. not just designing specific control approaches for individual induction motor fault scenarios, this thesis has carried out a systematic research on induction motor systems considering the various faults that can typically be present, having either “additive” fault or “multiplicative” effects on the system dynamics, according to whether the faults are sensor or actuator (additive fault) types or component or motor faults (multiplicative fault) types.To achieve the required objectives, an active approach to FTC is used, making use of fault estimation (FE, an approach that determine the magnitude of a fault signal online) and fault compensation. This approach of FTC/FE considers an integration of the electrical and mechanical dynamics, initially using adaptive and/or sliding mode observers, Linear Parameter Varying (LPV, in which nonlinear systems are locally decomposed into several linear systems scheduled by varying parameters) and then using back-stepping control combined with observer/estimation methods for handling certain forms of nonlinearity.In conclusion, the thesis proposed an integrated research of induction motor FTC/FE with the consideration of different types of faults and different types of uncertainties, and validated the approaches through simulations and experiments

Actuator fault diagnosis of singular delayed LPV systems with inexact measured parameters via PI unknown input observer

© 2017 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 study, actuator fault diagnosis of singular delayed linear parameter varying (SDLPV) systems is considered. The considered system has a time-varying state delay and its matrices are dependent on some parameters that are measurable online. It is assumed that the measured parameters are inexact due to the existence of noise in real situations. The system with inexact measured parameters is converted to an uncertain system. Actuator fault diagnosis is carried out based on fault size estimation. For this purpose, the system is transformed to a polytopic representation and then a polytopic proportional integral unknown input observer (PI-UIO) is designed. The proposed observer provides simultaneous state and actuator fault estimation while attenuating, in the H8H8 sense, the effects of input disturbance, output noise and the uncertainty caused by inexact measured parameters. The design procedure of PI-UIO is formulated as a convex optimisation problem with a set of Linear Matrix Inequality (LMI) constraints in the vertices of the parameter domain, guaranteeing robust exponential convergence of the PI-UIO. The efficiency of the proposed method is illustrated with an electrical circuit example modelled as an SDLPV system.Peer ReviewedPostprint (author's final draft

Robust inversion based fault estimation for discrete-time LPV systems

The article presents a state-space based Fault Diagnosis (FD) method for discrete-time, affine Linear Parameter Varying (LPV) systems. The goal of the technical note is to develop a robust and dynamic inversion based technique for systems with parameter varying representations when an additive, exogenous disturbance signal perturbs the system. After applying geometric concepts for explicit fault inversion, a robust strategy is proposed to attenuate the effect of the unknown disturbance input signal on the fault estimation error. The proposed robust observer is derived as a solution of off-line Linear Matrix Inequality (LMI) conditions. The technical note demonstrates the viability of the novel methodology through a numerical example