Application of the Unscented Kalman Filter (UKF) Estimation Techniques for Fault Detection Diagnosis and Isolation (FDDI) in Attitude Control (AC) and Heating Ventilation Air Conditioning (HVAC) Systems

In this work we investigate two main applications of the detection and isolation of partial (soft) and total (hard) failures in the reaction wheel (RW) actuators of the satellite attitude control systems (ACS) and in the Heating Ventilation and Air Conditioning (HVAC) valve actuators respectively. The fault detection diagnosis and isolation (FDDI) is accomplished using a probabilistic approach based on the interactive multiple models (IMM) schemes embedded with Extended Kalman Filter (EKF) or Unscented Kalman Filter (UKF) estimation techniques. Towards this objective, the healthy modes of the ACS and HVAC systems under different operating conditions as well as a number of different fault scenarios including changes and anomalies in the temperature, power supply bus voltage, and unexpected current variations in the actuators of each axis of the satellite, or leakage, stuck-open and stuck-close fault modes in the HVAC actuator valves are considered. We describe and develop a bank of interacting multiple model Extended Kalman Filters (IMM_EKF) or Unscented Kalman Filters (IMM_UKF) to detect and isolate the above mentioned reaction wheel and valve failures in the ACS and HVAC systems. Also, it should be emphasized that the proposed IMM_EKF and IMM_UKF techniques are implemented based on high-fidelity highly nonlinear models of a commercial ITHACO RWA and discharge air temperature (DAT) cooling or heating coils. Compared to other fault detection diagnosis and isolation (FDDI) strategies developed in the control systems literature, the proposed FDDI schemes is shown, through extensive numerical simulations by using MATLAB and SIMULINK software packages, to be more accurate, less computationally demanding, and more robust with the potential of extending to a number of other engineering applications. Also, the proposed algorithms deal directly with the nonlinear dynamics of the system, the

[1]  Khashayar Khorasani,et al.  Fault Detection in Reaction Wheel of a Satellite Using Observer-Based Dynamic Neural Networks , 2005, ISNN.

[2]  N. Tudoroiu,et al.  Fault detection and diagnosis of valve actuators in HVAC systems , 2005, Proceedings of 2005 IEEE Conference on Control Applications, 2005. CCA 2005..

[3]  K. Khorasani,et al.  State estimation of the vinyl acetate reactor using unscented Kalman filters (UKF) , 2005, 2005 International Conference on Industrial Electronics and Control Applications.

[4]  Ludmila Mihaylova,et al.  AN INTERACTING MULTIPLE MODEL ALGORITHM FOR STOCHASTIC SYSTEMS CONTROL , 1999 .

[5]  Ludmila Mihaylova,et al.  Interacting Multiple Model Algorithms for Manoeuvring Ship Tracking Based On New Ship Models , 2000 .

[6]  Youmin Zhang,et al.  Detection and diagnosis of sensor and actuator failures using interacting multiple-model estimator , 1997, Proceedings of the 36th IEEE Conference on Decision and Control.

[7]  Robert M. Pap,et al.  Fault Diagnosis , 1990, Bayesian Networks in Fault Diagnosis.

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

[9]  N. Tudoroiu,et al.  Fault Detection and Diagnosis of Valve Actuators in Discharge Air Temperature (DAT) Systems, using Interactive Unscented Kalman Filter Estimation , 2006, 2006 IEEE International Symposium on Industrial Electronics.

[10]  Alberto Pigazo,et al.  Kalman Filter Recent Advances and Applications , 2009 .

[11]  E. Sobhani-Tehrani,et al.  Interactive Bank of Unscented Kalman Filters for Fault Detection and Isolation in Reaction Wheel Actuators of Satellite Attitude Control System , 2006, IECON 2006 - 32nd Annual Conference on IEEE Industrial Electronics.

[12]  Gregory L. Plett,et al.  Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 3. State and parameter estimation , 2004 .

[13]  Jr. J.J. LaViola,et al.  A comparison of unscented and extended Kalman filtering for estimating quaternion motion , 2003, Proceedings of the 2003 American Control Conference, 2003..

[14]  K. Khorasani,et al.  Satellite fault diagnosis using a bank of interacting Kalman filters , 2007, IEEE Transactions on Aerospace and Electronic Systems.

[15]  Jeffrey K. Uhlmann,et al.  New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.

[16]  Kumpati S. Narendra,et al.  Adaptive control using multiple models , 1997, IEEE Trans. Autom. Control..

[17]  Rudolph van der Merwe,et al.  The unscented Kalman filter for nonlinear estimation , 2000, Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373).

[18]  Paul M. Frank,et al.  Fault Diagnosis in Dynamic Systems , 1993, Robotics, Mechatronics and Manufacturing Systems.

[19]  Paul M. Frank,et al.  Fault diagnosis in dynamic systems: theory and application , 1989 .

[20]  D. Banabic,et al.  Recent advances and applications , 2004 .

[21]  Gregory L. Plett,et al.  Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs Part 1. Background , 2004 .