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

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

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

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

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

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

Unknown input observer approaches to robust fault diagnosis

This thesis focuses on the development of the model-based fault detection and isolation /fault detection and diagnosis (FDI/FDD) techniques using the unknown input observer (UIO) methodology. Using the UI de-coupling philosophy to tackle the robustness issue, a set of novel fault estimation (FE)-oriented UIO approaches are developed based on the classical residual generation-oriented UIO approach considering the time derivative characteristics of various faults. The main developments proposed are:- Implement the residual-based UIO design on a high fidelity commercial aircraft benchmark model to detect and isolate the elevator sensor runaway fault. The FDI design performance is validated using a functional engineering simulation (FES) system environment provided through the activity of an EU FP7 project Advanced Fault Diagnosis for Safer Flight Guidance and Control (ADDSAFE).- Propose a linear time-invariant (LTI) model-based robust fast adaptive fault estimator (RFAFE) with UI de-coupling to estimate the aircraft elevator oscillatory faults considered as actuator faults.- Propose a UI-proportional integral observer (UI-PIO) to estimate actuator multiplicative faults based on an LTI model with UI de-coupling and with added H∞ optimisation to reduce the effects of the sensor noise. This is applied to an example on a hydraulic leakage fault (multiplicative fault) in a wind turbine pitch actuator system, assuming that thefirst derivative of the fault is zero. - Develop an UI–proportional multiple integral observer (UI-PMIO) to estimate the system states and faults simultaneously with the UI acting on the system states. The UI-PMIO leads to a relaxed condition of requiring that the first time derivative of the fault is zero instead of requiring that the finite time fault derivative is zero or bounded. - Propose a novel actuator fault and state estimation methodology, the UI–proportional multiple integral and derivative observer (UI-PMIDO), inspired by both of the RFAFE and UI-PMIO designs. This leads to an observer with the comprehensive feature of estimating faults with bounded finite time derivatives and ensuring fast FE tracking response.- Extend the UI-PMIDO theory based on LTI modelling to a linear parameter varying (LPV) model approach for FE design. A nonlinear two-link manipulator example is used to illustrate the power of this method

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