PD CONTROLLER BASED UNMANNED SHIP NAVIGATION
DOI:
https://doi.org/10.29121/granthaalayah.v5.i4RACEEE.2017.3337Keywords:
PD Controller, Tracking EfficiencyAbstract [English]
For the improvement of the performance of track keeping of unmanned water vehicle numerous ship models, wave disturbances models and distinct control algorithms has been proposed. Researchers are using variants of PID controllers, Adaptive controllers and Predictive controllers for accurate trajectory control. Being simpler in nature still PID controller are popular in control domain. In this paper we have implemented PD based controller for trajectory control of unmanned vehicle considering all standard models of sea disturbances. Analysis of result obtained using PD control proved that path tracking is more accurate than open loop controller in terms of computation time, complexity and fuel consumption.
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Dr. S.A.Hariprasad, Pampapathi, Vijay Singh, Dr. Krishna, Sharath. K, T.D. Shashikala.(2012). “A DESIGN APPROACH TO RUDDER CONTROLLER.” International Journal of Computer Science and technology, 3(3), 23-29.
Dr. S.A.Hariprasad, Dr.M. Krishna, Cdr.Vijay Singh, Pampapathi, Sanjay.N, Arunlal.K.S.(2013). “PERFORMANCE ANALYSIS OF SHIP TRACKING USING PID/PREDICTIVE CONTROLLER.” International Journal of Science Engineering and Advance Technology, 1(1), 1-3.
Ching-Yaw Tzeng, Ju-Fen Chen. (1999). “FUNDAMENTAL PROPERTIES OF LINEAR SHIP STERRING DYNAMIC MODELS.” Journal of Marine Science and Technology, 7(2), 79-88.
Thor I. Fossen. “GUIDANCE AND CONTROL OF OCEAN VEHICLES.” John Wiley and Sons LTD (1994).
Ke Feng, Ning Wang, Dan Liu, MengJooEr.(2016).“ADAPTIVE FUZZY TRAJECTORY CONTROL OF UNMANNED SURFACE VEHICLES WITH UNKNOWN DYNAMICS.” International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS), 1-6. DOI: https://doi.org/10.1109/ICCSS.2016.7586478
Yan-Cheng Liu, Si-Yuan Liu, Ning Wang. (2016). “FULLY-TUNED NEURAL NETWORK BASED ROBUST ADAPTIVE TRACKING CONTROL OF UNMANNED UNDERWATER VEHIVLE WITH THRUSTER DYNAMICS.” International Journal of Neurocomputing, 2-28. http://dx.doi.org/10.1016/j.neucom.2016.02.042. DOI: https://doi.org/10.1016/j.neucom.2016.02.042
Ning Wang, Jing-chao Sun, MengJoo, Yan-Cheng Liu. (2015). “A NOVEL LEARNING CONTROL FRAMEWORK OF UNMANNED SURFACE VEHICLES.” IEEE Transcation on Cybernetics, 1-12. 10.1109/TCYB.2015.2423635
Chang-Zhong Pan, Xu-Zhi Lai, Simon X. Yang, Min Wu. (2013). “AN EFFICIENT NEURAL NETWORK APPROACH TO TRACKING CONTROL OF AN AUTONOMOUS SURFACE VEHICLE WITH UNKNOWN DYNAMICS.” Expert System with Applications, 40, 1629-1635.
A.PedroAguiar, Joao P. hespanha.(2007).“TRAJECTORY-TRACKING AND PATH-FOLLOWING OF UNDERACTUATED AUTONOMOUS VEHICLES WITH PARAMETRIC MODELING UNCERTAINITY.” IEEE Transaction on Automatic Control, 52(8), 1362-1379. 10.1109/TAC.2007.902731. DOI: https://doi.org/10.1109/TAC.2007.902731
KhacDuc Do. (2011). “GLOBAL ROBUST AND ADAPTIVE OUTPUT FEEDBACK DYNAMIC POSITIONING OF SURFACE SHIPS.” J. Marine Sci. Appl. 10, 325-332. 10.1007/s11804-0111076-z DOI: https://doi.org/10.1007/s11804-011-1076-z
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