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

A review of convex approaches for control, observation and safety of linear parameter varying and Takagi-Sugeno systems

This paper provides a review about the concept of convex systems based on Takagi-Sugeno, linear parameter varying (LPV) and quasi-LPV modeling. These paradigms are capable of hiding the nonlinearities by means of an equivalent description which uses a set of linear models interpolated by appropriately defined weighing functions. Convex systems have become very popular since they allow applying extended linear techniques based on linear matrix inequalities (LMIs) to complex nonlinear systems. This survey aims at providing the reader with a significant overview of the existing LMI-based techniques for convex systems in the fields of control, observation and safety. Firstly, a detailed review of stability, feedback, tracking and model predictive control (MPC) convex controllers is considered. Secondly, the problem of state estimation is addressed through the design of proportional, proportional-integral, unknown input and descriptor observers. Finally, safety of convex systems is discussed by describing popular techniques for fault diagnosis and fault tolerant control (FTC).Peer ReviewedPostprint (published version

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

Flight Evaluation of an LPV Sliding Mode Observer for Sensor FTC

This brief develops a sliding mode sensor fault-tolerant control scheme for a class of linear parameter varying (LPV) systems. It incorporates a sliding mode observer that reconstructs the unknown sensor faults based on only the system inputs and outputs. The reconstructed sensor faults are used to compensate for the corrupted sensor measurements before they are used in the feedback controller. Provided accurate fault estimates can be computed; near nominal control performance can be retained without any controller reconfiguration. Furthermore, the closed-loop stability of the fault-tolerant control (FTC) scheme, involving both a sliding mode controller and a sliding mode observer, is rigorously analyzed. The proposed scheme is validated using the Japan Aerospace Exploration Agency’s Multipurpose Aviation Laboratory (MuPAL- α ) research aircraft. These flight tests represent the first validation tests of a sliding mode sensor FTC scheme on a full-scale aircraft

On the synthesis of an integrated active LPV FTC scheme using sliding modes

This is the final version. Available on open access from Elsevier via the DOI in this recordThis paper proposes an integrated fault tolerant control scheme for a class of systems, modelled in a linear parameter-varying (LPV) framework and subject to sensor faults. The gain in the LPV sliding mode observer (SMO) and the gain in the LPV static feedback controller are synthesized simultaneously to optimize the performance of the closed-loop system in an L2 sense. In the proposed scheme, the sensor faults are reconstructed by the SMO and these estimates are subsequently used to compensate the corrupted sensor measurements before they are used by the feedback controller. To address the synthesis problem, an iterative algorithm is proposed based on a diagonalization of the closed-loop Lyapunov matrix at each iteration. As a result the NP-hard, non-convex linear parameter-varying bilinear matrix inequality (LPV/BMI) associated with the Bounded Real Lemma formulation, is simplified into a tractable convex LPV/LMI problem. A benchmark scenario, involving the loss of the angle of attack sensor in a civil aircraft, is used as a case study to demonstrate the effectiveness of the scheme.European Commissio

A hybrid automata approach for monitoring the patient in the loop in artificial pancreas systems

The use of automated insulin delivery systems has become a reality for people with type 1 diabetes (T1D), with several hybrid systems already on the market. One of the particularities of this technology is that the patient is in the loop. People with T1D are the plant to control and also a plant operator, because they may have to provide information to the control loop. The most immediate information provided by patients that affects performance and safety are the announcement of meals and exercise. Therefore, to ensure safety and performance, the human factor impact needs to be addressed by designing fault monitoring strategies. In this paper, a monitoring system is developed to diagnose potential patient modes and faults. The monitoring system is based on the residual generation of a bank of observers. To that aim, a linear parameter varying (LPV) polytopic representation of the system is adopted and a bank of Kalman filters is designed using linear matrix inequalities (LMI). The system uncertainty is propagated using a zonotopic-set representation, which allows determining confidence bounds for each of the observer outputs and residuals. For the detection of modes, a hybrid automaton model is generated and diagnosis is performed by interpreting the events and transitions within the automaton. The developed system is tested in simulation, showing the potential benefits of using the proposed approach for artificial pancreas systems.Peer ReviewedPostprint (published version

Flight evaluation of an LPV sliding mode observer for sensor FTC

This brief develops a sliding mode sensor fault-tolerant control scheme for a class of linear parameter varying (LPV) systems. It incorporates a sliding mode observer that reconstructs the unknown sensor faults based on only the system inputs and outputs. The reconstructed sensor faults are used to compensate for the corrupted sensor measurements before they are used in the feedback controller. Provided accurate fault estimates can be computed; near nominal control performance can be retained without any controller reconfiguration. Furthermore, the closed-loop stability of the fault-tolerant control (FTC) scheme, involving both a sliding mode controller and a sliding mode observer, is rigorously analyzed. The proposed scheme is validated using the Japan Aerospace Exploration Agency's Multipurpose Aviation Laboratory (MuPAL-α) research aircraft. These flight tests represent the first validation tests of a sliding mode sensor FTC scheme on a full-scale aircraft

Fault Diagnosis and Fault Tolerant Control of Wind Turbines: An Overview

Wind turbines are playing an increasingly important role in renewable power generation. Their complex and large-scale structure, however, and operation in remote locations with harsh environmental conditions and highly variable stochastic loads make fault occurrence inevitable. Early detection and location of faults are vital for maintaining a high degree of availability and reducing maintenance costs. Hence, the deployment of algorithms capable of continuously monitoring and diagnosing potential faults and mitigating their effects before they evolve into failures is crucial. Fault diagnosis and fault tolerant control designs have been the subject of intensive research in the past decades. Significant progress has been made and several methods and control algorithms have been proposed in the literature. This paper provides an overview of the most recent fault diagnosis and fault tolerant control techniques for wind turbines. Following a brief discussion of the typical faults, the most commonly used model-based, data-driven and signal-based approaches are discussed. Passive and active fault tolerant control approaches are also highlighted and relevant publications are discussed. Future development tendencies in fault diagnosis and fault tolerant control of wind turbines are also briefly stated. The paper is written in a tutorial manner to provide a comprehensive overview of this research topic

Flight Data Validation of an Icing Accretion Estimation Scheme using Super-twisting Observers

This paper develops a generalised multivariable super-twisting observer for a class of nonlinear systems in which the unmeasured variables linked to the known state dependent matrix function appear multiplicatively. A sufficient condition is given to guarantee that the reconstruction errors associated with the unmeasurable variables converge to zero in finite time. This approach is then used to address the aircraft icing accretion estimation problem despite unreliable sensor measurement. The efficacy of the approach has been evaluated via real flight data recorded under natural icing conditions. Results show that the observer has the capability to estimate the change of the drag coefficient induced by icing accretion and to reconstruct the unreliable pitch rate sensor measurement simultaneously