LPV Observer Research Articles

Detection of Sensor Faults with or without Disturbance Using Analytical Redundancy Methods: An Application to Orifice Flowmeter.

Sensors and transducers play a vital role in the productivity of any industry. A sensor that is frequently used in industries to monitor flow is an orifice flowmeter. In certain instances, faults can occur in the flowmeter, hindering the operation of other dependent systems. Hence, the present study determines the occurrence of faults in the flowmeter with a model-based approach. To do this, the model of the system is developed from the transient data obtained from computational fluid dynamics. This second-order transfer function is further used for the development of linear-parameter-varying observers, which generates the residue for fault detection. With or without disturbance, the suggested method is capable of effectively isolating drift, open-circuit, and short-circuit defects in the orifice flowmeter. The outcomes of the LPV observer are compared with those of a neural network. The open- and short-circuit faults are traced within 1 s, whereas the minimum time duration for the detection of a drift fault is 5.2 s and the maximum time is 20 s for different combinations of threshold and slope.

LPV Control and Virtual-Sensor-Based Fault Tolerant Strategies for a Three-Axis Gimbal System.

This paper deals with the LPV control of a three-axis gimbal including fault-tolerant capabilities. First, the derivation of an analytical model for the considered system based on the robotics Serial-Link (SL) theory is derived. Then, a series of simplifications that allow obtaining a quasi-LPV model for the considered gimbal is proposed. Gain scheduling LPV controllers with PID structure are designed using pole placement by means of linear matrix inequalities (LMIs). Moreover, exploiting the sensor redundancy available in the gimbal, a virtual-sensor-based fault tolerant control (FTC) strategy is proposed. This virtual sensor uses a Recursive Least Square (RLS) estimation algorithm and an LPV observer for fault detection and estimation. Finally, the proposed LPV control scheme including the virtual sensor strategy is tested in simulation in several scenarios.

Fault estimation for automotive Electro-Rheological dampers: LPV-based observer approach

This paper presents an LPV method for Fault Estimation, considering Electro-Rheological dampers of automotive suspension systems. Faults upon the dampers are modelled as Loss of Effectiveness multiplicative factors, which are estimated with the proposed LPV observer. This framework is based upon an LPV extended-state observer, whose gain is derived from the mixed H2/H∞ norm minimization. The method is discussed through simulation, and validation tests are realized on a real vehicle test-bench in order to demonstrate the truthfulness and capability of the framework to identify faults on Electro-Rheological dampers.

LPV Force Observer Design and Experimental Validation from a Dynamical Semi-Active ER Damper Model

Abstract This paper presents an LPV damping force observer of Electro Rheological (ER) dampers for a real automotive suspension system, taking the dynamic characteristic of damper into account. First, an extended nonlinear quarter-car model is considered, where the time constant representing the damper dynamics is varying according to the control level. This is rewritten as an LPV model which is used to design an LPV observer. The objective of the LPV observer is to minimize the effects of bounded unknown input disturbances (unknown road profile and measurement noises) on the state estimation errors through an H∞ criterion, while the damper nonlinearity is bounded using a Lipschitz condition. Two low-cost accelerometers (the sprung mass and the unsprung mass accelerations) are used as inputs for the proposed methodology only. To experimentally assess the proposed approach, the observer is implemented on the 1/5-scaled real vehicle-INOVE testbench of GIPSA-lab. Experiments shows the ability of the observer to estimate the damper force in real-time, face to unknown inputs disturbance and sensor noises.

Fault detection of a PEMFC system based on delayed LPV observer

This work deals with the fault estimation of actuator fault for a polymer electrolyte membrane fuel cell (PEMFC) using descriptor approach. The main contribution of this paper is to consider unknown inputs and time delay in the output. Delay-dependent sufficient conditions for the design of an unknown input observer with time delays are given in terms of linear matrix inequalities (LMIs). For that purpose, an unknown input observer (UIO) for delayed LPV model is designed. Firstly, by considering the faults as auxiliary states, we obtain an augmented LPV system. An algorithm scheme is developed in order to compute the UIO proposed for the augmented system with actuator faults, secondly. Basing on the Lyapunov theory, the convergence and the stability of the UIO are analyzed. Finally and in order to apply the proposed fault diagnosis approach, the time delay is related to the mass flow oxygen in the anode of a PEMFC and actuator faults such as the compressor outlet mass flow rate and the return manifold outlet orifice are designed. Simulation results prove the effectiveness of this approach.

Interval observer for LPV systems: Application to vehicle lateral dynamics

This paper presents a new method for guaranteed and robust estimation of sideslip angle and lateral tire forces with consideration of cornering stiffness variations resulting from changes in tire/road and driving conditions. An interval LPV observer with both measurable and unmeasurable time-varying parameters is proposed. The longitudinal velocity is treated as the online measured time-varying parameter and the cornering stiffness at front and rear tires are assumed to be unknown but bounded with a priori known bounds. The obtained results are no more punctual values but a set of acceptable values. The simulation is based on experimental data in order to prove the effectiveness of the proposed observers.

Robust sensor fault estimation for descriptor-LPV systems with unmeasurable gain scheduling functions: Application to an anaerobic bioreactor

Abstract This paper addresses the design of a state estimation and sensor fault detection, isolation and fault estimation observer for descriptor-linear parameter varying (D-LPV) systems. In contrast to where the scheduling functions depend on some measurable time varying state, the proposed method considers the scheduling function depending on an unmeasurable state vector. In order to isolate, detect and estimate sensor faults, an augmented system is constructed by considering faults to be auxiliary state vectors. An unknown input LPV observer is designed to estimate simultaneously system states and faults. Sufficient conditions to guarantee stability and robustness against the uncertainty provided by the unmeasurable scheduling functions and the influence of disturbances are synthesized via a linear matrix inequality (LMI) formulation by considering H∞ and Lyapunov approaches. The performances of the proposed method are illustrated through the application to an anaerobic bioreactor model.

Robust sensor fault detection and isolation of an anerarobic bioreactor modeled as a descriptor-LPV system

This work is dedicated to the design of a robust fault detection system for an anaerobic bioreactor modeled as a Descriptor Linear Parameter Varying system (D-LPV) system with unmeasurable gain scheduling functionss. The main goals are: first, an accurate D-LPV representation of the nonlinear model is obtained by considering the sector nonlinearity modeling approach. Second, a LPV observer is designed to estimate the bioprocess states variables. The observer considers the gains scheduling functions depending on estimated states due to the lack of a biomass mass sensor. Third, in oder to perform fault detection and isolation, by mean of residual generation, a bank of observers is designed. For each observer, sufficient conditions to guarantee asymptotic stability and robustness against disturbance are given by a set of feasible Linear Matrix Inequalities (LMIs). Finally, some simulations in fault-free and faulty operations are considered on the bioreactor system.

Adaptive Vibration Control System for MR Damper Faults

Several methods have been proposed to estimate the force of a semiactive damper, particularly of amagnetorheologicaldamper because of its importance in automotive and civil engineering. Usually, all models have been proposed assuming experimental data in nominal operating conditions and some of them are estimated for control purposes. Because dampers are prone to fail, fault estimation is useful to design adaptive vibration controllers to accommodate the malfunction in the suspension system. This paper deals with the diagnosis and estimation of faults in an automotivemagnetorheologicaldamper. A robust LPV observer is proposed to estimate the lack of force caused by a damper leakage in a vehicle corner. Once the faulty damper is isolated in the vehicle and the fault is estimated, anAdaptive Vibration Control Systemis proposed to reduce the fault effect using compensation forces from the remaining healthy dampers. To fulfill the semiactive damper constraints in the fault adaptation, an LPV controller is designed for vehicle comfort and road holding. Simulation results show that the fault observer has good performance with robustness to noise and road disturbances and the proposed AVCS improves the comfort up to 24% with respect to a controlled suspension without fault tolerance features.

Fault Diagnosis Based on Robust Observer for Descriptor-LPV Systems with Unmeasurable Scheduling Functions

This paper design a method for fault detection and isolation based on observers for systems modelled as Descriptor-Linear Parameter Varying (D-LPV) with Unmeasurable Scheduling Functions (USF). The first contribution of the paper is deal with the USF problem by transforming the into an uncertain D-LPV system with an estimated scheduling parameter. As a second contribution, a robust LPV observer is designed with H∞ performance to supply a robust state estimation against uncertainties provided by USF. Sufficient conditions to guarantee the robustness and convergence are obtained via linear matrix inequalities (LMIs). Finally, an observer bank based on robust H∞ observers is used to generate residuals and perform sensor fault detection and isolation. A numerical example demonstrates the effectiveness of our methods.

Fault Detection and Isolation of a Real PEM Fuel Cell using Interval LPV Observers and the support from the National Science and Technology Council (CONACYT) Mexico.

The purpose of this paper is to design a Fault Detection and Isolation (FDI) system based on interval LPV observers with application to a real fuel cell. The interval LPV observer is used to generate an adaptive threshold to enhance the robustness of the fault detection test. Real field data and system identification techniques are used to identify the nominal model as well as its uncertainty. Since fuel cells are highly non-linear systems when operating in their whole range of operation, a LPV model is used. Finally, fault isolation is based on an algorithm that uses the residual fault sensitivity. Several fault scenarios are used to show the performance of the proposed approach.

LPV observer design for PEM fuel cell system: Application to fault detection

In this paper, the modelling of an energy generation system based on polymer electrolyte membrane fuel cell (PEMFC) system through a parameter varying approach (LPV model), that takes in to account model parameter variation with the operating point, is presented. This model has been obtained through a Jacobian linearization of the PEMFC non-linear dynamic model that was previously calibrated using real data from lab. In order to illustrate the use of the LPV model obtained its application to model-based fault detection is used. For this purposes a set of common fault scenarios, which could appear during a normal PEMFC operation, is used as case study.

State Estimation for Polytopic LPV Descriptor Systems: Application to Fault Diagnosis

In this paper, the design of a polytopic Unknown Input Observer (UIO) for polytopic Linear Varying Parameter (LPV) descriptor systems is investigated. The method presented is based on the representation of affine LPV descriptor systems where parameters evolve in a hypercube domain. The considered polytopic UIO is able to estimate the states of the system in spite of the presence of unknown inputs. This approach is also applied to actuator fault detection, isolation and estimation. Stability conditions of such observer are expressed in terms of Linear Matrix Inequalities (LMI). An example illustrates the effectiveness and performances of such polytopic LPV observer.

STATE OBSERVATION OF NONLINEAR AND UNCERTAIN SYSTEMS: APPLICATION TO INDUCTION MOTORS

This paper deals with a study of performances and characteristics of four advanced state observers: the high gain observer (exponential observer), the multimodel observer, the sliding mode observer and the linear parameter variation observer (LPV observer). The main characteristic of the first observer lies in its calibration. Thus, the gain of the exponential observer does not use any kind of transformation and it is obtained from an algebraic Lyapunov equation. In addition, this observer requires the choice of a single parameter to calculate its gain. For the multimodel observer, it is based on the fusion of linear observers which are built around different operating points and the use of the validity notion. The most distinguished feature of the sliding mode observer is the ability to result in robust control. Indeed, its correction term is based on discontinuous functions. The last observation approach is the LPV observer. In this approach, we consider that the process is defined by a linear system with polytopic representation and linear parameter variation. Thus, the observer, designed in the same structure, is based on the notion of quadratic detectability and the use of LMI 1 tools to calculate its observation gain. The performances of these observers are highlighted through the estimation of state variables of an induction machine and specially the rotor flux with the stator current and voltage measurements. Numerical simulation study has confirmed the availability and the interesting results of the nonlinear and robust state observer approaches.