Cooperative actuator fault accommodation in formation flight of unmanned vehicles using absolute measurements

In this study, the problem of cooperative fault accommodation in formation flight of unmanned vehicles represented by linear time-invariant models that are subject to loss-of-effectiveness actuator faults is investigated through a hierarchical framework. Three hierarchical levels are envisaged, namely a low-level fault recovery (LLFR), a formation-level fault recovery and a high-level supervisor. In the LLFR module, a recovery controller is designed by using an estimate of the actuator fault. A performance monitoring module is then introduced at the high-level to identify a ‘partially low-level recovered’ vehicle because of inaccuracy in the fault severity estimate that results in violating the ‘error specification’ of the formation mission. The high-level supervisor then activates the formation-level fault recovery module to compensate for the resulting performance degradations of the partially low-level (LL) recovered vehicle at the expense of other healthy vehicles. The fault is accommodated by reconfiguring the formation structure through the novel notion of the weighted absolute measurement formation digraph, activating a robust controller for the partially LL recovered vehicle, and imposing a constraint on the desired input signals. Numerical simulations for a formation flight of five satellites in the planetary orbital environment are presented to confirm the validity and effectiveness of the proposed analytical work.

[1]  Ming Liu,et al.  H/sub 2/and hαrobust autopilot synthesis for longitudinal f light of a special unmanned aerial vehicle: a comparative study , 2008 .

[2]  Stephen P. Boyd,et al.  Linear Matrix Inequalities in Systems and Control Theory , 1994 .

[3]  K. Khorasani,et al.  A distributed Kalman filter for actuator fault estimation of deep space formation flying satellites , 2009, 2009 3rd Annual IEEE Systems Conference.

[4]  Brian D. O. Anderson,et al.  UAV Formation Control: Theory and Application , 2008, Recent Advances in Learning and Control.

[5]  K. Lau,et al.  AN INNOVATIVE DEEP SPACE APPLICATION OF GPS TECHNOLOGY FOR FORMATION FLYING SPACECRAFT , 1996 .

[6]  Rodney Teo,et al.  Decentralized overlapping control of a formation of unmanned aerial vehicles , 2004, Autom..

[7]  Mo Jamshidi,et al.  Intelligent control of UAVs for consensus-based and network controlled applications , 2011 .

[8]  Leang-San Shieh,et al.  Actuator fault detection and performance recovery with Kalman filter-based adaptive observer , 2007, Int. J. Gen. Syst..

[9]  Charles D. Brown Elements of Spacecraft Design , 2002 .

[10]  J. Karl Hedrick,et al.  Linear Tracking for a Fixed-Wing UAV Using Nonlinear Model Predictive Control , 2009, IEEE Transactions on Control Systems Technology.

[11]  Alexandra Moutinho,et al.  Hover Control of an UAV With Backstepping Design Including Input Saturations , 2008, IEEE Transactions on Control Systems Technology.

[12]  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..

[13]  Mehrdad Saif,et al.  Observer-Based Fault Diagnosis of Satellite Systems Subject to Time-Varying Thruster Faults , 2007 .

[14]  Christopher Edwards,et al.  Robust decentralized actuator fault detection and estimation for large-scale systems using a sliding mode observer , 2008, Int. J. Control.

[15]  Khashayar Khorasani,et al.  Multi-agent methodology for distributed and cooperative supervisory estimation subject to unreliable information , 2011 .

[16]  Mehrdad Saif,et al.  An iterative learning observer for fault detection and accommodation in nonlinear time‐delay systems , 2006 .

[17]  Khashayar Khorasani,et al.  A Hybrid Fault Detection and Isolation Strategy for a Network of Unmanned Vehicles in Presence of Large Environmental Disturbances , 2010, IEEE Transactions on Control Systems Technology.

[18]  Chris R. Fuller,et al.  Control Reconfiguration Based on Hierarchical Fault Detection and Identification for Unmanned Underwater Vehicles , 2003 .

[19]  Khashayar Khorasani,et al.  Cooperative actuator fault accommodation in formation flight of unmanned vehicles using relative measurements , 2011, Int. J. Control.

[20]  Khashayar Khorasani,et al.  Actuator Fault Detection and Isolation for a Network of Unmanned Vehicles , 2009, IEEE Transactions on Automatic Control.

[21]  Steven X. Ding,et al.  Actuator fault robust estimation and fault-tolerant control for a class of nonlinear descriptor systems , 2007, Autom..

[22]  Mehrdad Saif,et al.  Actuator fault isolation and estimation for uncertain nonlinear systems , 2005, 2005 IEEE International Conference on Systems, Man and Cybernetics.

[23]  Petros A. Ioannou,et al.  Adaptive LQ Control With Anti-Windup Augmentation to Optimize UAV Performance in Autonomous Soaring Applications , 2008, IEEE Transactions on Control Systems Technology.

[24]  Kevin L. Moore,et al.  High-Order and Model Reference Consensus Algorithms in Cooperative Control of MultiVehicle Systems , 2007 .

[25]  Nader Meskin,et al.  Hybrid fault detection and isolation strategy for non-linear systems in the presence of large environmental disturbances , 2010 .

[26]  P. Khargonekar,et al.  An algebraic Riccati equation approach to H ∞ optimization , 1988 .

[27]  Richard M. Murray,et al.  Recent Research in Cooperative Control of Multivehicle Systems , 2007 .

[28]  M. Staroswiecki,et al.  Fault estimation in nonlinear uncertain systems using robust/sliding-mode observers , 2004 .

[29]  Khashayar Khorasani,et al.  Cooperative fault accommodation in formation flying satellites , 2008, 2008 IEEE International Conference on Control Applications.

[30]  Sarangapani Jagannathan,et al.  Output Feedback Control of a Quadrotor UAV Using Neural Networks , 2010, IEEE Transactions on Neural Networks.