A hybrid fault diagnosis and recovery for a team of unmanned vehicles

In this paper, a hybrid fault detection, isolation, and recovery (FDIR) methodology is developed for a team of unmanned vehicles which takes advantage of the cooperative nature of the system to accomplish the desired mission in presence of failures. The proposed methodology is hybrid and consists of a low level (agent level) and a high level (team level) FDIR. The high level FDIR is formulated in the discrete-event system (DES) supervisory control framework, whereas the low level FDIR uses the classical control techniques. By properly integrating the two FDIR components, a larger class of faults can be detected and isolated when compared to results in the literature. A reconfiguration strategy is also designed so that the team is recovered from faults. Simulation results are provided to elucidate the efficacy of the proposed approach.

[1]  Luigi Villani,et al.  Fault Diagnosis and Fault Tolerance for Mechatronic Systems: Recent Advances , 2003 .

[2]  Randal W. Beard,et al.  A coordination architecture for spacecraft formation control , 2001, IEEE Trans. Control. Syst. Technol..

[3]  J.D. Boskovic,et al.  Retrofit reconfigurable flight control in the presence of control effector damage , 2005, Proceedings of the 2005, American Control Conference, 2005..

[4]  Michèle Lavagna,et al.  SMART-FDIR: Use of Artificial Intelligence in the Implementation of a Satellite FDIR , 2003 .

[5]  Amir G. Aghdam,et al.  Supervisory control of switching control systems , 2006, CDC.

[6]  Shahin Hashtrudi-Zad,et al.  A hybrid architecture for diagnosis in hybrid systems with applications to spacecraft propulsion system , 2007, 2007 IEEE International Conference on Systems, Man and Cybernetics.

[7]  S. Hashtrudi Zad,et al.  Fault recovery in control systems: a modular discrete-event approach * , 2004, (ICEEE). 1st International Conference on Electrical and Electronics Engineering, 2004..

[8]  Mark A. Peot,et al.  Planning sensing actions for UAVs in urban domains , 2005, Security + Defence.

[9]  S. Monckton,et al.  Multi-unmanned vehicle systems (nUxVs) at Defence R&D Canada , 2006, SPIE Defense + Commercial Sensing.

[10]  Alberto L. Sangiovanni-Vincentelli,et al.  Design of Observers for Hybrid Systems , 2002, HSCC.

[11]  Jason L. Speyer,et al.  A Vehicle Health Monitoring System Evaluated Experimentally on a Passenger Vehicle , 2005, CDC 2005.

[12]  William S. Levine,et al.  Handbook Of Networked And Embedded Control Systems , 2007 .

[13]  Keith Worden Fault Diagnosis and Fault Tolerance for Mechatronic Systems: Recent Advances , 2005 .

[14]  Timothy W. McLain,et al.  Cooperative forest fire surveillance using a team of small unmanned air vehicles , 2006, Int. J. Syst. Sci..

[15]  A. S. Barry,et al.  Ground surveillance radar for perimeter intrusion detection , 2000, 19th DASC. 19th Digital Avionics Systems Conference. Proceedings (Cat. No.00CH37126).

[16]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part I: Quantitative model-based methods , 2003, Comput. Chem. Eng..

[17]  V. Cocquempot,et al.  Fault detection and isolation for hybrid systems using structured parity residuals , 2004, 2004 5th Asian Control Conference (IEEE Cat. No.04EX904).

[18]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part II: Qualitative models and search strategies , 2003, Comput. Chem. Eng..

[19]  Roy S. Smith,et al.  Control of Deep-Space Formation-Flying Spacecraft; Relative Sensing and Switched Information , 2005 .