Optimal hybrid fault recovery in a team of unmanned aerial vehicles

This paper introduces and develops an optimal hybrid fault recovery methodology for a team of unmanned vehicles by taking advantage of the cooperative nature of the team to accomplish the desired mission requirements in presence of faults/failures. The proposed methodology is developed in a hybrid framework that consists of a low-level (an agent level and a team level) and a high-level (discrete-event system level) fault diagnosis and recovery modules. A high-level fault recovery scheme is proposed within the discrete-event system (DES) supervisory control framework, whereas it is assumed that a low-level fault recovery designed based on classical control techniques is already available. The low-level recovery module employs information on the detected and estimated fault and modifies the controller parameters to recover the team from the faulty condition. By taking advantage of combinatorial optimization techniques, a novel reconfiguration strategy is proposed and developed at the high-level so that the faulty vehicles are recovered with minimum cost to the team. A case study is provided to illustrate and demonstrate the effectiveness of our proposed approach for the icing problem in unmanned aerial vehicles, which is a well-known structural problem in the aircraft industry.

[1]  John Valasek,et al.  [american institute of aeronautics and astronautics aiaa atmospheric flight mechanics conference and exhibit - keystone, colorado ()] aiaa atmospheric flight mechanics conference and exhibit - prediction of icing effects on the lateral/directional stability and control of light airplanes , 2006 .

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

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

[4]  M. R. Rao,et al.  Combinatorial Optimization , 1992, NATO ASI Series.

[5]  Michael B. Bragg,et al.  Effect of ice accretion on aircraft flight dynamics , 2000 .

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

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

[8]  J.P. Desai,et al.  On controlling aircraft formations , 2001, Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228).

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

[10]  A. Saboori,et al.  Fault recovery in discrete event systems , 2005, 2005 ICSC Congress on Computational Intelligence Methods and Applications.

[11]  C SIAMJ.,et al.  AN OPTIMAL CONTROL THEORY FOR DISCRETE EVENT SYSTEMS , 1998 .

[12]  Jean-Pierre Quadrat,et al.  11th International Conference on Analysis and Optimization of Systems Discrete Event Systems , 1994 .

[13]  K. Khorasani,et al.  Fault diagnosis and recovery from structural failures (icing) in unmanned aerial vehicles , 2009, 2009 3rd Annual IEEE Systems Conference.

[14]  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.

[15]  R. J. Shaw,et al.  Ice shapes and the resulting drag increase for a NACA 0012 airfoil , 1984 .

[16]  Jiawei Han,et al.  Adaptive Fastest Path Computation on a Road Network: A Traffic Mining Approach , 2007, VLDB.

[17]  John Valasek,et al.  Prediction of icing effects on the lateral/directional stability and control of light airplanes , 2012 .

[18]  Shigemasa Takai.  Supervisory Control of Discrete Event Systems , 2014 .

[19]  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..

[20]  Asok Ray,et al.  A language measure for performance evaluation of discrete-event supervisory control systems , 2004 .

[21]  Christos G. Cassandras,et al.  Introduction to Discrete Event Systems , 1999, The Kluwer International Series on Discrete Event Dynamic Systems.

[22]  Dan Ionescu.  Conditions for optimization of discrete event systems using temporal logic models , 1994 .

[23]  Khashayar Khorasani,et al.  A hybrid fault diagnosis and recovery for a team of unmanned vehicles , 2008, 2008 IEEE International Conference on System of Systems Engineering.

[24]  William B. Wright.  A Summary of Validation Results for LEWICE 2.0 , 1999 .

[25]  Wade W. Huebsch,et al.  Investigation of relative humidity and induced-vortex effects on aircraft icing , 2007 .