Control Reconfiguration Based on Hierarchical Fault Detection and Identification for Unmanned Underwater Vehicles

Unmanned underwater vehicles (UUVs) have been developed for various applications in ocean engineering. When failures occur to UUVs and result in abnormal operations, the only solution is to abort from the mission due to lack of fault tolerance. The purpose of this study is to investigate a method by which UUVs can continue to operate acceptably following failure occurrences. Based on a unique hierarchical fault detection and identification, this paper presents a control reconfiguration scheme with multiple sliding-mode controllers for each of the hypothesized failure modes from a discrete set [θ1, θ2,..., θ n ], which depict the failure status of actuators and sensors. The reconfigured control is a probability weighted average of all the elemental control signals. We apply this method to the steering subsystem of the Naval Postgraduate School (NPS) UUV with simulated rudder and/or sensor failures. The results show that both the heading angle and the steering track have been properly compensated.

[1]  Paul M. Frank,et al.  Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy: A survey and some new results , 1990, Autom..

[2]  Junku Yuh,et al.  Fault-tolerant System Design of an Autonomous Underwater Vehicle Odin: an Experimental Study , 1999, Int. J. Syst. Sci..

[3]  Casilda D. de Benito On-Board Real Time Failure Detection and Diagnosis of Automotive Systems , 1988, 1988 American Control Conference.

[4]  Mogens Blanke,et al.  Fault-tolerant control systems — A holistic view , 1997 .

[5]  C.D. Wickens,et al.  Ergonomic design for perspective flight-path displays , 1989, IEEE Control Systems Magazine.

[6]  Kevin M. Passino,et al.  Expert supervision of fuzzy learning systems for fault tolerant aircraft control , 1995 .

[7]  Lingli Ni,et al.  Fault-Tolerant Control of Unmanned Underwater Vehicles , 2001 .

[8]  A. J. Healey,et al.  A neural network approach to failure diagnostics for underwater vehicles , 1992, Proceedings of the 1992 Symposium on Autonomous Underwater Vehicle Technology.

[9]  Didier Theilliol,et al.  Fault-tolerant control in dynamic systems: application to a winding machine , 2000 .

[10]  H. E. Rauch,et al.  Autonomous control reconfiguration , 1995 .

[11]  K.M. Passino,et al.  Expert supervision of fuzzy learning systems with applications to reconfigurable control for aircraft , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.

[12]  Peter S. Maybeck,et al.  Reconfigurable flight control via multiple model adaptive control methods , 1991 .

[13]  Peter S. Maybeck,et al.  Multiple model adaptive controller for the STOL F-15 with sensor/actuator failures , 1989, Proceedings of the 28th IEEE Conference on Decision and Control,.

[14]  A. J. Healey,et al.  Multivariable sliding mode control for autonomous diving and steering of unmanned underwater vehicles , 1993 .

[15]  Glen Williams,et al.  Failure detection in an autonomous underwater vehicle , 1994, Proceedings of IEEE Symposium on Autonomous Underwater Vehicle Technology (AUV'94).

[16]  Peter S. Maybeck,et al.  Sensor/actuator failure detection in the Vista F-16 by multiple model adaptive estimation , 1995, IEEE Transactions on Aerospace and Electronic Systems.

[17]  Robert F. Stengel Intelligent failure-tolerant control , 1991 .

[18]  S. E. Dunn,et al.  Damage detection for autonomous underwater vehicles , 1993 .

[19]  A. J. Healey,et al.  Adaptive sliding mode control of autonomous underwater vehicles in the dive plane , 1990 .

[20]  Kevin M. Passino,et al.  Intelligent control for autonomous systems , 1995 .

[21]  R. J. Patton,et al.  Fault detection, supervision, and safety for technical processes 1997 (SAFEPROCESS '97) : a proceedings volume from the 3rd IFAC symposium, Kingston Upon Hull, UK, 26-28 August 1997 , 1998 .