Development and application of sliding mode LPV fault reconstruction schemes for the ADDSAFE Benchmark

Abstract This paper describes the development and the evaluation of a robust sliding mode observer fault detection scheme applied to an aircraft benchmark problem as part of the ADDSAFE project. The ADDSAFE benchmark problem which is considered in this paper is the yaw rate sensor fault scenario. A robust sliding mode sensor fault reconstruction scheme based on an LPV model is presented, where the fault reconstruction signal is obtained from the so-called equivalent output error injection signal associated with the observer. The development process includes implementing the design using AIRBUS׳s the so-called SAO library which allows the automatic generation of flight certifiable code which can be implemented on the actual flight control computer. The proposed scheme has been subjected to various tests and evaluations on the Functional Engineering Simulator conducted by the industrial partners associated with the ADDSAFE project. These were designed to cover a wide range of the flight envelope, specific challenging manoeuvres and realistic fault types. The detection and isolation logic together with a statistical assessment of the FDD schemes are also presented. Simulation results from various levels of FDD developments (from tuning, testing and industrial evaluation) show consistently good results and fast detection times.

[1]  Philippe Goupil,et al.  A High Fidelity AIRBUS Benchmark for System Fault Detection and Isolation and Flight Control Law Clearance , 2013 .

[2]  Gary J. Balas,et al.  Linear, parameter‐varying control and its application to a turbofan engine , 2002 .

[3]  Claudio De Persis,et al.  Proceedings of the 41st IEEE Conference on Decision and Control , 2002 .

[4]  Daniel Ossmann,et al.  Diagnosis of actuator faults using LPV-gain scheduling techniques , 2011 .

[5]  Alessandro Casavola,et al.  A fault-detection, filter-design method for linear parameter-varying systems , 2007 .

[6]  David Henry,et al.  A method for designing fault diagnosis filters for LPV polytopic systems , 2008 .

[7]  L. Fridman,et al.  High-Order Sliding-Mode Observation of Linear Systems with Unknown Inputs , 2008 .

[8]  A. Levant.  Robust exact differentiation via sliding mode technique , 1998 .

[9]  Alexandre Falcoz,et al.  Structured H∞/H_ LPV filters for fault diagnosis: Some new results , 2009 .

[10]  Christopher Edwards,et al.  Sliding mode observers for robust detection and reconstruction of actuator and sensor faults , 2003 .

[11]  Masayuki Sato.  Filter design for LPV systems using quadratically parameter-dependent Lyapunov functions , 2006, Autom..

[12]  Hafid Smaili,et al.  RECOVER: A Benchmark for Integrated Fault Tolerant Flight Control Evaluation , 2010 .

[13]  Philippe Goupil,et al.  AIRBUS state of the art and practices on FDI and FTC in flight control system , 2011 .

[14]  Xiukun Wei,et al.  Mixed H2212;/H∞ fault detection observer design for LPV systems , 2008, 2008 47th IEEE Conference on Decision and Control.

[15]  Kenzo Nonami,et al.  Sliding mode control with gain scheduled hyperplane for LPV plant , 1999 .

[16]  Andres Marcos,et al.  Advanced Diagnosis for Sustainable Flight Guidance and Control: The European ADDSAFE Project , 2011 .

[17]  Leonid M. Fridman,et al.  Interval estimation for LPV systems applying high order sliding mode techniques , 2012, Autom..

[18]  Kenzo Nonami,et al.  Sliding mode control with time-varying hyperplane for AMB systems , 1998 .

[19]  Halim Alwi,et al.  Validation of Sliding Mode Observer FDI Schemes on the ADDSAFE Functional Engineering Simulator , 2012 .

[20]  Halim Alwi,et al.  Actuator and Sensor Fault Reconstruction Using an LPV Sliding Mode Observer , 2010 .

[21]  Halim Alwi,et al.  Robust actuator fault reconstruction for LPV systems using sliding mode observers , 2010, 49th IEEE Conference on Decision and Control (CDC).

[22]  J. Bokor,et al.  Failure detection for quasi LPV systems , 2002, Proceedings of the 41st IEEE Conference on Decision and Control, 2002..

[23]  Harald Pfifer,et al.  Generation of Optimal Linear Parametric Models for LFT-Based Robust Stability Analysis and Control Design , 2011, IEEE Trans. Control. Syst. Technol..

[24]  Vadim I. Utkin,et al.  Sliding Modes in Control and Optimization , 1992, Communications and Control Engineering Series.

[25]  Yuri B. Shtessel,et al.  HOSM Observer for a Class of Non-Minimum Phase Causal Nonlinear MIMO Systems , 2010, IEEE Transactions on Automatic Control.

[26]  Halim Alwi,et al.  Second Order Sliding Mode Observers for the ADDSAFE Benchmark Problem , 2012 .

[27]  Sarah K. Spurgeon,et al.  Sliding Mode Observers for Fault Detection , 1997 .

[28]  Christopher Edwards,et al.  A multivariable super-twisting sliding mode approach , 2014, Autom..

[29]  Edoardo Mosca,et al.  Robust fault detection and isolation for LPV systems under a sensitivity constraint , 2009 .

[30]  Christopher Edwards,et al.  Sliding mode control : theory and applications , 1998 .

[31]  Pierre Apkarian,et al.  Self-scheduled H∞ control of linear parameter-varying systems: a design example , 1995, Autom..

[32]  Christopher Edwards,et al.  Robust Fault Reconstruction in Uncertain Linear Systems Using Multiple Sliding Mode Observers in Cascade , 2010, IEEE Transactions on Automatic Control.

[33]  Andres Marcos,et al.  Industrial benchmarking and evaluation of ADDSAFE FDD designs , 2012 .

[34]  Halim Alwi,et al.  Robust fault reconstruction for linear parameter varying systems using sliding mode observers , 2014 .

[35]  Halim Alwi,et al.  Fault Detection and Fault-Tolerant Control Using Sliding Modes , 2011 .

[36]  J. Bokor,et al.  DETECTION FILTER DESIGN FOR LPV SYSTEMS – A GEOMETRIC APPROACH , 2002 .

[37]  Halim Alwi,et al.  Sliding mode estimation schemes for incipient sensor faults , 2009, Autom..

[38]  Michel Verhaegen,et al.  Mixed H − / H ∞ Fault Detection Observer Design for LPV Systems , 2008 .

[39]  Sarah K. Spurgeon,et al.  Sliding mode observers for fault detection and isolation , 2000, Autom..