Adaptive Intelligent Inverse Control Of Nonlinear Systems With Regard To Sensor Noise And Parameter Uncertainty (Magnetic Ball Levitation System Case Study)

[1]Zuqiang Long, Yan Yuan, Yuebing Xu, Shehui Du, “High-accuracy positioning of lathe servo system using fuzzy controllers based on variable universe of discourse”, International Journal on Smart Sensing and Intelligent Systems, Vol. 7, No. 3, pp. 1114-1133, 2014. Search in Google Scholar

[2]Zhonghu Yuan, Wenwu Hua and Xiaowei Han, “Zigbee transmission power dynamic adjustment system based on fuzzy control”, International Journal on Smart Sensing and Intelligent Systems, Vol. 7, No. 4, pp. 1736-1752, 2014. Search in Google Scholar

[3]Xing Haihua, Yu Xianchuan, Hu Dan1 and Dai Sha, “Sensitivity analysis of hierarchical hybrid fuzzy neural network”, International Journal on Smart Sensing and Intelligent Systems, Vol. 8, No. 3, pp. 1837-1854, 2015. Search in Google Scholar

[4]J.R. Castro, O. Castillo, L.G. Martínez, “Interval Type-2 Fuzzy Logic Toolbox”, Engineering Letters, EL_15_1_14, pp. 1-15, 2007. Search in Google Scholar

[5]L.A. Zadeh, “The concept of a linguistic variable and its application to approximate reasoning”, Parts 1, 2, and 3. Information Sciences 8, 9, 199–249, 301-357, pp. 43-80, 1975.10.1016/0020-0255(75)90036-5 Search in Google Scholar

[6]Xiuzhi Sang, Xinwang Liu, “An analytical solution to the TOPSIS model with interval type-2 fuzzy sets”, Soft Computing, 2015.10.1007/s00500-014-1584-2 Search in Google Scholar

[7]E. Hosseinzadeh, H. Hassanpour, M. Arefi, “A weighted goal programming approach to fuzzy linear regression with crisp inputs and type-2 fuzzy outputs”, Soft Computing, Vol. 19, No. 5, pp. 1143-1151, 2015. Search in Google Scholar

[8]S.O. Olatunji, A. Selamat, A.R.A. Azeez, “Modeling permeability and PVT properties of oil and gas reservoir using hybrid model based on type-2 fuzzy logic systems”, Neurocomputing, Vol. 157, pp. 125–142, 2015.10.1016/j.neucom.2015.01.027 Search in Google Scholar

[9]A.C. Aras, O. Kaynak, “Interval Type-2 Fuzzy Neural System Based Control with Recursive Fuzzy C-Means Clustering”, International Journal of Fuzzy Systems, Vol. 16, No. 3, pp. 317-326, 2014 Search in Google Scholar

[0]C.M. Lin, Y.M. Chen, C.S. Hsueh, “A Self-Organizing Interval Type-2 Fuzzy Neural Network for Radar Emitter Identification”, International Journal of Fuzzy Systems, vol. 16, No. 1, pp. 20-30, 2014. Search in Google Scholar

[1]Tufan Kumbasar, “A simple design method for interval type-2 fuzzy pid controllers”, Soft Computing, Vol. 18, No. 7, pp. 1293-1304, 2014. Search in Google Scholar

[2]O. Castillo, P. Melin, “Type-2 Fuzzy Logic: Theory and Applications”, Springer-Verlag Berlin Heidelberg 2008.10.1007/978-3-540-76284-3 Search in Google Scholar

[3]J. Tavoosi, A. Shamsi Jokandan and M.A. Daneshwar, “A new method for position control of a 2-DOF robot arm using neuro–fuzzy controller”, Indian Journal of Science and Technology, Vol. 5, No.3, 2012.10.17485/ijst/2012/v5i3.10 Search in Google Scholar

[4]J. Tavoosi, M. Alaei, B. Jahani, M.A. Daneshwar, “A novel intelligent control system design for water bath temperature control”, Australian Journal of Basic and Applied Sciences, Vol. 5, No. 12, pp. 1879-1885, 2011. Search in Google Scholar

[5]J. Tavoosi, M.A. Badamchizadeh, “A class of type-2 fuzzy neural networks for nonlinear dynamical system identification”, Neural Computing & Application, Vol. 23, No. 3, 2013.10.1007/s00521-012-0981-7 Search in Google Scholar

[6]J.R. Castro, O. Castillo, P. Melin, A. Rodríguez-Díaz, “A hybrid learning algorithm for a class of interval type-2 fuzzy neural networks”, journal of Information Sciences, Vol. 179, pp. 2175-2193, 2009. Search in Google Scholar

[7]R.H. Abiyev, O. Kaynak, T. Alshanableh, F. Mamedov, “A type-2 neuro-fuzzy system based on clustering and gradient techniques applied to system identification and channel equalization”, Applied Soft Computing, Vol. 11, pp. 1396–1406, 2011. Search in Google Scholar

[8]R. Martínez, O. Castillo, L.T. Aguilar, “Optimization of interval type-2 fuzzy logic controllers for a perturbed autonomous wheeled mobile robot using genetic algorithms”, Information Sciences, Vol. 179, pp. 2158–2174, 2009. Search in Google Scholar

[9]Frumen Olivas, Fevrier Valdez, Oscar Castillo, Patricia Melin, “Dynamic parameter adaptation in particle swarm optimization using interval type-2 fuzzy logic”, Soft Computing, 2014.10.1109/NORBERT.2014.6893881 Search in Google Scholar

[0]D. Bhattacharya, A. Konar, P. Das, “Secondary factor induced stock index time-series prediction using Self-Adaptive Interval Type-2 Fuzzy Sets”, Neurocomputing, Vol. 171, pp. 551–568, 1 January 2016.10.1016/j.neucom.2015.06.073 Search in Google Scholar

[1]F.J. Lin, P.H. Shieh, Y.C. Hung, “An intelligent control for linear ultrasonic motor using interval type-2 fuzzy neural network”, IET Electric Power Applications, Vol. 2, No. 1,pp. 32– 41, 2008.10.1049/iet-epa:20070060 Search in Google Scholar

[2]A. Mohammadzadeh, S. Ghaemi, “Synchronization of chaotic systems and identification of nonlinear systems by using recurrent hierarchical type-2 fuzzy neural networks”, ISA Transactions, Vol. 58, pp. 318–329, September 2015.10.1016/j.isatra.2015.03.01625933686 Search in Google Scholar

[3]T.C. Lin, “Based on interval type-2 fuzzy-neural network direct adaptive sliding mode control for SISO nonlinear systems”, Commun Nonlinear Sci Numer Simulat, Vol. 15, pp. 4084-4099, 2010. Search in Google Scholar

[4]C. Hwang, F.C.H. Rhee, “Uncertain fuzzy clustering: interval type-2 fuzzy approach to C-means”, IEEE Trans. on Fuzzy Systems, Vol. 15, No. 1, pp. 107–120, 2007.10.1109/TFUZZ.2006.889763 Search in Google Scholar

[5]H.A. Hagras,”A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots”, IEEE Trans. on Fuzzy Systems, Vol. 12, No. 4, pp. 524–539, 2004.10.1109/TFUZZ.2004.832538 Search in Google Scholar

[6]J.L. Clavo-Rolle, O. Fontelna-Romero, B. Perez-Sanchez, B. Guijarro-Berdinas, “Adaptive inverse control using an online learning algorithm for neural networks”, INFORMATICA, Vol. 25, No. 3, pp. 401–414, 2014.10.15388/Informatica.2014.20 Search in Google Scholar

[7]C.H.D. Li, J.Q. Yi, Y. Yu, D.B. Zhao, “Inverse control of cable-driven parallel mechanism using type-2 fuzzy neural network”, Acta Automatica Sinica, Vol. 36, No. 3, pp. 459-464, 2010.10.3724/SP.J.1004.2010.00459 Search in Google Scholar

[8]H.H. Kadhim, “Self-learning of ANFIS inverse control using iterative learning technique”, International Journal of Computer Applications, Vol. 21, No. 8, pp. 24-29, 2011.10.5120/2532-3450 Search in Google Scholar

[9]C.F. Juang, J.S. Chen, “Water bath temperature control by a recurrent fuzzy controller and Its FPGA implementation”, IEEE Trans. Industrial Electronics, Vol. 53, No. 3, pp. 941949, 2006. Search in Google Scholar

[0]Y. Satoh, H. Nakamura, H. Katayama, H. Nishitani, “Adaptive inverse optimal control of a magnetic levitation system”, Adaptive Control, pp.307-322, 2009.10.5772/6512 Search in Google Scholar

[1]J. J. Raygoza-Panduro, S. Ortega-Cisneros, J. Rivera, A. de la Mora, “Design of a mathematical unit in FPGA for the implementation of the control of a magnetic levitation system”, International Journal of Reconfigurable Computing, pp. 1-9, 2008.10.1155/2008/634306 Search in Google Scholar

[2]A. Kumar, M. Kumar Panda, V. Kumar, “Design and implementation of interval type-2 single input fuzzy logic controller for magnetic levitation system” Proceedings of ICAdC, AISC 174, pp. 833–840, 2013.10.1007/978-81-322-0740-5_99 Search in Google Scholar

[3]T. T. Salim, V.M. Karsli, “Control of single axis magnetic levitation system using fuzzy logic control”, International Journal of Advanced Computer Science and Applications, Vol. 4, No. 11, 2013.10.14569/IJACSA.2013.041111 Search in Google Scholar

[4]A. C. Unni, A. Junghare, “Fuzzy logic controller and LQR for magnetic levitation system”, International Journal of Recent Technology and Engineering (IJRTE),Vol. 3, No.1, 2014. Search in Google Scholar

[5]K. Niveedha, P. Hari Prasath, P.N. Shivakumaran, B. Karthikeyan, “Performance assessment and design of an intelligent fuzzy logic controller for magnetic ball levitation system”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 2, No. 8, 2013. Search in Google Scholar

[6]H.A. Elreesh, B. Hamed, “FPGA fuzzy controller design for magnetic ball levitation”, I.J. Intelligent Systems and Applications, Vol. 10, pp. 72-81, 2012.10.5815/ijisa.2012.10.08 Search in Google Scholar

[7]S. Liu, C.Y. Su, Z. Li, “Robust adaptive inverse control of a class of nonlinear systems with Prandtl-Ishlinskii hysteresis model”, IEEE Trans. on Automatic Control, Vol. 59, No. 8, 2014.10.1109/TAC.2014.2298732 Search in Google Scholar

[8]O. Nelles “Nonlinear System Identification”, Springer-Verlag Berlin Heidelberg 2001.10.1007/978-3-662-04323-3 Search in Google Scholar

[9]H. Moodi, M. Farrokhi, “Robust observer design for Sugeno systems with incremental quadratic nonlinearity in the consequent”, International Journal of Applied Mathematics and Computer Science, Vol. 23, No. 4, 2013.10.2478/amcs-2013-0053 Search in Google Scholar

[0]R. Abiyev, F. Mamedov, T. Al-shanableh, “Nonlinear Neuro-fuzzy Network for Channel Equalization”, Analysis and Design of Intelligent Systems using Soft Computing Techniques, pp. 327-336, 2007.10.1007/978-3-540-72432-2_33 Search in Google Scholar

[1]T. Dereli, A. Baykasoglu, K. Altun, A. Durmusoglu, I. B. Turksen, “Industrial applications of type-2 fuzzy sets and systems: A concise review”, Computers in Industry, Vol. 62, pp.125–137, 2011.10.1016/j.compind.2010.10.006 Search in Google Scholar

[2]W.H. Roger Jeng, C.Y. Yeh, S.J. Lee, “General type-2 fuzzy neural network with hybrid learning for function approximation”, IEEE International Conference on Fuzzy Systems, Korea, 2009. Search in Google Scholar

[3]N. Karnik, J. Mendel, Q. Liang, “Type-2 Fuzzy Logic Systems”, IEEE Transactionon Fuzzy Systems, Vol. 7, No. 6, pp. 643-658, 1999.10.1109/91.811231 Search in Google Scholar

[4]M. Singh, S. Srivastava, M. Hanmandlu, J.R.P. Gupta, “Type-2 fuzzy wavelet networks (T2FWN) for system identification using fuzzy differential and Lyapunov stability algorithm”, Applied Soft Computing, Vol. 9, pp. 977–989, 2009.10.1016/j.asoc.2008.03.017 Search in Google Scholar

[5]Y. Chu, K. Glover, “Bounds of the induced norm and model reduction errors for systems with repeated scalar nonlinearities”, IEEE Trans. Automatic Control. Vol.44,No. 2, pp.471483, 1999. Search in Google Scholar

[6]E. D. Sontag “Recurrent neural networks: Some systems-theoretic aspects” in Dealing with Complexity: A Neural Network Approach, M. Karny, K. Warwick, and V. Kurkova, Eds. London, U.K.: Springer-Verlag, 1997. Search in Google Scholar

[7]N. Barabanov, D. Prokhorov, “Stability analysis of discrete time recurrent neural networks,” IEEE Trans. Neural Networks, Vol. 13, pp.292–303, 2002.10.1109/72.99141618244432 Search in Google Scholar

[8]Q. Han, A. Xue, S. Liu, and X. Yu, “Robust absolute stability criteria for uncertain Lur’e systems of neutral type,” International Journal of Robust and Nonlinear Control, Vol. 18, pp. 278-295, 2008.10.1002/rnc.1219 Search in Google Scholar

[9]V. M. Becerra, F. R. Garces, S. J. Nasuto and W. Holderbaum, “An efficient parameterization of dynamic neural networks for nonlinear system identification,” IEEE Trans. Neural Networks, Vol. 16, No. 4, pp. 983-988, 2005.10.1109/TNN.2005.84984416121739 Search in Google Scholar

[0]J. Li, Y. Jinshou, “Nonlinear Hybrid Adaptive Inverse Control Using Neural Fuzzy System and Its Application To CSTR Systems”, Proceedings of the 4th World Congress on Intelligent Control and Automation, Shanghai, P.R.China, 2002. Search in Google Scholar