An intelligent system for failure detection and control in an autonomous underwater vehicle

Autonomous underwater vehicles (AUVs) have been used extensively in deep sea research. Failure detection and control is an important issue in maintaining the stability of an AUV. In most AUVs, the vehicle resurfaces in the event of minor failures such as in the depth sensor, the inclinometer, etc. The paper proposes an intelligent system for failure detection and control in AUVs where the vehicle could continue exploration in case of minor failures in the sensors and control surfaces. The intelligent system, based on the model proposed in Patel and Ranganathan (1996), integrates the adaptability of an artificial neural network (ANN) and the inferencing ability of a fuzzy rule based expert system on a single VLSI chip. The associative function of the ANN is used to recognize and detect the failures by observing the various changing parameters of the dynamic vehicle. The inferencing ability of an expert system suggests ways to control the failure and indicates the subsequent status of the vehicle. The entire system could be used as a low level diagnoser in an overall control system for AUVs.

[1]  N. Ranganathan,et al.  A VLSI system architecture for real-time intelligent decision making , 1996, Proceedings of International Conference on Application Specific Systems, Architectures and Processors: ASAP '96.

[2]  Lotfi A. Zadeh,et al.  Outline of a New Approach to the Analysis of Complex Systems and Decision Processes , 1973, IEEE Trans. Syst. Man Cybern..

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

[4]  B.D. Appleby,et al.  Using learning techniques to accommodate unanticipated faults , 1993, IEEE Control Systems.

[5]  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.

[6]  X. Zheng.  Layered control of a practical AUV , 1992, Proceedings of the 1992 Symposium on Autonomous Underwater Vehicle Technology.

[7]  Asok Ray,et al.  A Redundancy Management Procedure for Fault Detection and Isolation , 1986 .

[8]  A. L. Kidd,et al.  Knowledge acquisition for expert systems: a practical handbook , 1987 .

[9]  Martin A. Abkowitz,et al.  Stability and motion control of ocean vehicles , 1969 .

[10]  Udo W. Pooch,et al.  FTMP: a protocol for distributed fault tolerant operating system services , 1992, Proceedings of the 1992 Symposium on Autonomous Underwater Vehicle Technology.

[11]  Rajni V. Patel,et al.  Quantitative measures of robustness for multivariable systems , 1980 .

[12]  S. E. Dunn,et al.  On-line damage detection for autonomous underwater vehicles , 1994, Proceedings of IEEE Symposium on Autonomous Underwater Vehicle Technology (AUV'94).

[13]  N. Ranganathan,et al.  IDUTC: an intelligent decision-making system for urban traffic-control applications , 2001, IEEE Trans. Veh. Technol..

[14]  A. J. Healey.  Towards an Automatic Health Monitor for Autonomous Underwater Vehicles using Parameter Identification , 1993, 1993 American Control Conference.

[15]  L. Yongkuan,et al.  AUV's trends over the world in the future decade , 1992, Proceedings of the 1992 Symposium on Autonomous Underwater Vehicle Technology.