ADAPTIVE IDENTIFICATION OF UNDERWATER GLIDER USING U-MODEL FOR DEPTH & PITCH CONTROL UNDER HYDRODYNAMIC DISTURBANCES
DOI:
https://doi.org/10.11113/jt.v74.4814Keywords:
Adaptive algorithm, RBFNN, stability, underwater roboticsAbstract
This paper presents an adaptive identification method based on recently developed control oriented model called U-model for online identification of underwater glider. It is indicated from obtained results that the proposed technique can accurately and adaptively model nonlinearity and dynamics of underwater glider even in presence of hydrodynamic disturbances. Since the proposed identification U-model scheme is control oriented in nature, hence it can be further utilized to synthesize a simple law for depth and pitch control of glider.
References
Mahmoudian, N. 2009. Efficient Motion Planning and Control for Underwater Gliders. PhD Dissertation, Faculty of the Virginia Polytechnic Institute and State University, Blacksburg Virginia.
Smallwood, D. A, and L. L. Whitcomb. 2003. Adaptive Identification of Dynamically Positioned Underwater Robotic Vehicles. IEEE Transactions on Control System Technology.
Hassanein, O., S. G. Anavatti, and T. Ray. 2012. Improved Fuzzy Neural Modelling for Underwater Vehicles. World Academy of Science, Engineering and Technology.
Hong, E. Y., T. K. Meng, and M. Chitre. 2013. Online System Identification of the Dynamics of an Autonomous Underwater Vehicle. IEEE International Underwater Technology Symposium, Tokyo, Japan.
Kato, N. 1995. Applications of Fuzzy Algorithm to Guidance of and Control of Underwater Vehicle. Underwater Robotic Vehicles: Design and Control, (TSI Press, Albuquerque).
Jeen-Shing, W. and C. S. G. Lee. 2003. Self-adaptive Recurrent Neuro-Fuzzy Control of an Autonomous Underwater Vehicle. IEEE Transactions Robotic Automation. 19: 283-295.
Antonelli, G., S. Chiaverini, and G. Fusco. 2007. A Fuzzy-logic Based Approach for Mobile Robot Path Tracking. IEEE Transactions on Fuzzy System. 15: 11-21.
Kodogiannis, V. S. 2006. Neuro-control of Unmanned Underwater Vehicles. International Journal of System Science. 37: 149-162.
Lewis, F. L., A. Yegildirek, and K. Liu. 1996. Multilayer Neural-net Robot Controller with Guaranteed Tracking Performance. IEEE Transactions on Neural Network. 7: 388-399.
Zhu. Q. M., and L. Z. Guo. 1991. A Pole Placement Controller for Nonlinear Dynamic Plants. Journal of Systems and Control Engineering. 216(I): 467-476.
Shafiq. M, and N. R. Butt. 2005. U-model Based Adaptive IMC for Nonlinear Dynamic Plants. 10th IEEE conference on Emerging Technologies and Factory Automation, Catania.
Shafiq. M, and M. Haseebuddin. 2004. Internal Model Control for Nonlinear Dynamic Plant using U-model. 12th Mediterranean conference on Control and Automation, Turkey.
Shafiq. M, and N. R. Butt. 2011. Utilizing Higher-order Neural Networks in U-model based Controllers for Stable nonlinear plants. International Journal of Control, Automation and Systems. 9(3): 489-496.
Chang. W., W. Wang and H. Jia. 2011. Radial Basis Functions Neural Network of Vary Learning Rate Based Stochastic U-Model. 2011 International Conference on Electrical and Control Engineering (ICECE). 278-281.
Ali. S. S. A., F. M. Al-Sunni., M. Shafiq., and B. Jamil. 2011. Umodel Based 2DoF Multi-variable IMC for Improved Input Disturbance Rejection: A Case Study on a 2-Link Robot Manipulator. International Journal of Advanced Robotic Systems 8(3): 166-175.
Ali. S. S. A., F. M. Al-Sunni., M. Shafiq., and B. Jamil. 2010. U-Model Based Learning Feedforward Control of MIMO Nonlinear Systems. Springer Journal of Electrical Engineering. 91(8): 405.
Ali. S. S. A., F. M. Al-Sunni., M. Shafiq., and B. Jamil. 2008. MIMO U-model Based Control: Real Time Tracking Control and Feedback Analysis via Small Gain Theorem. Transactions of Circuits & Systems. 7(7).
Ali. S. S. A. 2009. U-Model Based Control: Adaptive Control Approach for Multivariable Nonlinear Systems. LAP Lambert Academic Publishing.
Ali. S. S. A., M. Moinuddin., K. Raza. and S. H. Adil. 2014. An Adaptive Learning Rate for RBFNN Using Time-domain Feedback Analysis. Scientific World Journal.
Abbasi. I., S. S. A. Ali., M. Ovinis., and W. Naeem. 2014. Adaptive Identification of Underwater Glider Using U-Model for Depth and Pitch Control. The Fifth International Conference on Underwater System and Technology: Theory and Applications (USYS’14), Melaka, Malaysia. 145-150.
Hussain. N. A. A., M. R. Arshad. and R. M. Mokhtar. 2010. Modelling and Identification of an Underwater Glider. Proceedings of 2010 International Symposium on Robotics & Intelligent Sensors, Nagoya, Japan.
Noh, M. M., M. R. Arshad. and R. M. Mokhtar. Depth and Pitch Control of USM Underwater Glider: Performance Comparison PID vs LQR. Indian Journal of Geo-Marine Sciences. 40: 200-206.
Downloads
Published
Issue
Section
License
Copyright of articles that appear in Jurnal Teknologi belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions, or any other reproductions of similar nature.
