Reconfigurable Fault Tolerant Control of a Boeing 747 using Subspace Predictive Control

This paper presents a fault tolerant control (FTC) system for a Boeing 747. This FTC system is based on a combination of predictive control and subspace identification called subspace predictive control (SPC). In SPC, the mathematical model used by conventional predictive controllers to predict the future output, is replaced by a subspace predictor. Since this subspace predictor is continuously adapted in a closed-loop setting based on past input-output data, it can naturally adapt to the system after a fault has occurred. This property is very useful for FTC since a model of the post-fault system is not required. A novel feature of the presented SPC algorithm is how the predictor is recursively updated in a computationally efficient way. Furthermore, for optimal performance the SPC algorithm is implemented with different settings for different fault types. These settings include the control surfaces that are used for a certain fault type. The fault types are determined by a multiple model fault classification scheme. The proposed FTC system is evaluated on a detailed model of a Boeing 747, which is used in Action Group 16 on FTC of the European GARTEUR project.

[1]  Biao Huang,et al.  A data driven subspace approach to predictive controller design , 2001 .

[2]  Chingiz Hajiyev,et al.  Sensor Fault Detection and Isolation in Flight Control Systems Based on Innovation Approach , 2003 .

[3]  Jan M. Maciejowski,et al.  Predictive control : with constraints , 2002 .

[4]  Michel Verhaegen,et al.  Recursive subspace identification of linear and non-linear Wiener state-space models , 2000, Autom..

[5]  Youmin Zhang,et al.  Detection and diagnosis of sensor and actuator failures using IMM estimator , 1998 .

[6]  Michel Gevers,et al.  SPC: Subspace Predictive Control , 1999 .

[7]  Chingiz Hajiyev,et al.  Fault diagnosis and reconfiguration in flight control systems , 2003 .

[8]  Meir Pachter,et al.  Fault tolerant flight control , 2003 .

[9]  Jong-Yeob Shin,et al.  Performance analysis on fault tolerant control system , 2006, IEEE Transactions on Control Systems Technology.

[10]  Bart De Moor,et al.  Closed-loop model-free subspace-based LQG-design , 1999 .

[11]  Van Der Linden DASMAT-Delft University Aircraft Simulation Model and Analysis tool , 1996 .

[12]  Patrick Dewilde,et al.  Subspace model identification Part 1. The output-error state-space model identification class of algorithms , 1992 .

[13]  Jonathan P. How,et al.  Subspace based direct adaptive ℋ︁∞ control , 2001 .

[14]  Bart De Moor,et al.  Combined Deterministic-Stochastic Identification , 1996 .

[15]  Magnus Jansson A NEW SUBSPACE IDENTIFICATION METHOD FOR OPEN AND CLOSED LOOP DATA , 2005 .

[16]  Gary J. Balas,et al.  Application of H ∞ Fault Detection and Isolation to a Boeing 747-100/200 Aircraft , 2002 .

[17]  Mario G. Perhinschi,et al.  Online Parameter Estimation Techniques Comparison Within a Fault Tolerant Flight Control System , 2002 .

[18]  Lennart Ljung,et al.  Subspace identification from closed loop data , 1996, Signal Process..

[19]  Hafid Smaili,et al.  A Simulation Benchmark for Integrated Fault Tolerant Flight Control Evaluation , 2006 .

[20]  Andres Marcos,et al.  Linear parameter-varying detection filter design for a Boeing 747-100/200 aircraft , 2005 .

[21]  G. Balas,et al.  Development of linear-parameter-varying models for aircraft , 2004 .

[22]  Andres Marcos,et al.  Application of H-infinity Fault Detection and Isolation to a Boeing 747-100/200 Aircraft , 2002 .

[23]  Alessandro Chiuso,et al.  The role of vector autoregressive modeling in predictor-based subspace identification , 2007, Autom..

[24]  Michel Verhaegen,et al.  Model Weight Estimation for FDI Using Convex Fault Models , 2006 .