Identifikasi Pemodelan Matematis Robot Wall Following
This paper describes the process to obtain a mathematical model of a wall following robot. A mathematical modeling is carried out as an effort in determining Proportional, Integral, and Derivative (PID) controller parameters using analytic tuning. In this paper, the model approach used is Auto Regressive Exogenous (ARX). The ARX model is a model used to show the effect of control and disturbance on the output of the plant. The result of this research is a mathematical model of wall following robot which is then used to obtain PID controller parameters.
Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L.D., Monfort, M., Muller, U., Zhang, J. and Zhang, X., “End to end learning for self-driving cars”. arXiv preprint arXiv:1604.07316., 2016.
Kelly, A., Nagy, B., Stager, D. and Unnikrishnan, R., “Field and service applications-An infrastructure-free automated guided vehicle based on computer vision-An Effort to Make an Industrial Robot Vehicle that Can Operate without Supporting Infrastructure”. IEEE Robotics & Automation Magazine, vol.14, no. 3, hal 24-34, 2007.
Fahmizal, Surriani, A., Budianto, M. and Arrofiq, M.,. “Altitude control of quadrotor using fuzzy self tuning PID controller”. IEEE 5th International Conference on Instrumentation, Control, and Automation (ICA), hal. 67-72, Agustus, 2017.
Fahmizal, Chen TS, Chi SW, Kuo CH. “Fuzzy controller based subsumption behavior architecture for autonomous robotic wheelchair”. IEEE International Conference on Advanced Robotics and Intelligent Systems (ARIS), hal. 158-163, Mei, 2013.
Yata T, Kleeman L, Yuta SI. “Wall following using angle information measured by a single ultrasonic transducer”. IEEE International Conference on Robotics and Automation, vol. 2, hal. 1590-1596, Mei, 1998.
Fahmizal, Kuo CH. “Development of a fuzzy logic wall following controller for steering mobile robots”. IEEE International Conference on Fuzzy Theory and Its Applications (iFUZZY), hal. 7-12, Desember 2013.
Jung, I.K., Hong, K.B., Hong, S.K. and Hong, S.C., “Path planning of mobile robot using neural network”. IEEE International Symposium on Industrial Electronics, vol. 3, hal. 979-983, 1999.
L. Ljung, “Prediction error estimation methods”, Circ. Syst. Signal Process, vol. 21, hal. 11-21, 2002.
G. H. Shakouri and H. R. Radmanesh, “Identification of a continuous time nonlinear state space model for the extenal power system dynamic equivalent by neural network,” International Journal of Electrical Power & Energy Systems, vol. 31, hal. 334-344, 2009.
L. A. Zadeh, “On the Identification Problem,” IEEE Transactions On Circuit Theory, vol. 3, no 4, hal.277-281,1956.
L. Ljung, System Identification Theory for the User, 2nd ed.,
PrenticeHall, CA: Linkoping University Sweden, 1999.
Fahmizal, Setyawan, G., Arrofiq, M. and Mayub, A., “Logika Fuzzy pada Robot Inverted Pendulum Beroda Dua”. Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 4, no. 4, hal.244-252, 2017.
H. J. Palanth, S. Lacy, J. B. Hoagg and D. S. Bernstein, “Subspacebased Identification for Linear and Nonlinear Systems,” IEEE International Conference on American Control Conference, hal. 2320-2334, 2005.
Ling, T.G., Rahmat, M.F., Husain, A.R. and Ghazali, R., “System identification of electro-hydraulic actuator servo system”. IEEE 4th International Conference On Mechatronics (ICOM), hal. 17, Mei, 2011.
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