Abstract
Flexible joint manipulators are extensively used in several industries and precise control of their nonlinear dynamics has proven to be a challenging task. In this work, we want to compare two intelligent controllers by proposing two Takagi-Sugeno-Kang Neuro-Fuzzy Approaches (Type-1 and Type-2) to control a flexible joint. For both controllers, The inverse models are found using identification techniques, then they are put in series as inverse controllers to control the flexible joint in an online structure. Interval weights are trained by gradient descent approaches using backpropagation algorithms. Results reveal that, without any knowledge about the dynamic of the robot, the methods can control the flexible joint which is highly unstable. As illustrated in result section, One level more fuzziness of Type-2 in compare to type-1 fuzzy controllers helps this controller to more effectively deals with information from a knowledge base. The proposed models can effectively handle uncertainties arising from friction and other structural nonlinearities.
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Siciliano, B.: Control in robotics: open problems and future directions. In: IEEE International Conference on Control Applications, vol. 1, no. 1, pp. 81–85 (1998)
Khorasani, K.: Adaptive control of FJR. IEEE J. Robot. Autom. 8(2), 250–267 (1992)
Long, Z., Yuan, Y., Xu, Y., Du, S.: High-accuracy positioning of lathe servo system using fuzzy controllers based on variable universe of discourse. Int. J. Smart Sens. Intell. Syst. 7(3), 1114–1133 (2014)
Ho, W.H., Chou, J.H.: Design of optimal controllers for Takagi-Sugeno fuzzy-model-based systems. IEEE Trans. Syst. Man Cybern. A 37(3), 329–339 (2007)
Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning. Inf. Sci. 8(3), 199–249 (1975)
Hagras, H.: A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots. IEEE Trans. Fuzzy Syst. 12(4), 524–539 (2004)
Liang, Q., Mendel, J.M.: Interval T2 fuzzy logic systems: theory and design. IEEE Trans. Fuzzy Syst. 8(5), 535–550 (2000)
Karnik, N.N., Mendel, J.M., Liang, Q.: Type-2 fuzzy logic systems. IEEE Trans. Fuzzy Syst. 7(6), 643–658 (1999)
Wang, C.H., Cheng, C.S., Lee, T.T.: Dynamical optimal training for interval T2 fuzzy neural network (T2NFN). IEEE Trans. Syst. Man Cybern. 1462–1477 (2004)
Abiyev, R.H., Kaynak, O., Alshanableh, T., Mamedov, M.: A T2 neuro-fuzzy system based on clustering and gradient techniques applied to system identification and channel equalization. Appl. Soft Comput. 11, 1396–1406 (2011)
Lin, C.T., Pal, N.R., Wu, S.L., Liu, Y.T., Lin, Y.Y.: An interval T2 neural fuzzy system for online system identification and feature elimination. IEEE Trans. Neural Netw. Learn. Syst. 26(7), 1442–1455 (2015)
Castro, J.R., Castillo, O., Melin, P., Rodriguez-Diaz, A.: A hybrid learning algorithm for a class of interval T2 fuzzy neural networks. J. Inf. Sci. 179, 2175–2193 (2009)
Martinez, R., Castillo, O., Aguilar, L.T.: Optimization of interval T2 fuzzy logic controllers for a perturbed autonomous wheeled mobile robot using genetic algorithms. J. Inf. Sci. 179, 2158–2174 (2009)
Olivas, F., Valdez, F., Castillo, O., Melin, P.: Dynamic parameter adaptation in particle swarm optimization using interval T2 fuzzy logic. J. Soft Comput. 20(3), 1057–1070 (2016)
Moodi, H., Farrokhi, M.: Robust observer design for Sugeno systems with incremental quadratic nonlinearity in the consequent. Int. J. Appl. Math. Comput. Sci. 23(4), 711–723 (2013)
Khosravi, A., Nahavandi, S., Creighton, D., Srinivasan, D.: Interval Type-2 fuzzy logic systems for load forecasting. IEEE Trans. Power Syst. 27(3), 1274–1282 (2012)
Hagras, H.A.: A hierarchical T2 fuzzy logic control architecture for autonomous mobile robots. IEEE Trans. Fuzzy Syst. 12(4), 524–539 (2014)
Lin, T.C.: Based on interval T2 fuzzy-neural network direct adaptive sliding mode control for SISO nonlinear systems. Commun. Nonlinear Sci. Numer. Simul. 15(12), 4084–4099 (2010)
Clavo-Rolle, J.L., Fontelna-Romero, O., Perez-Sanchez, B., Guijarro-Berdinas, B.: Adaptive inverse control using an online learning algorithm for neural networks. Informatica 25(3), 401–414 (2014)
Li, C.H.D., Yi, J.Q., Yu, Y., Zhao, D.B.: Inverse control of cable-driven parallel mechanism using T2 fuzzy neural network. Acta Automatica Sinica 36(3), 459–464 (2010)
Kadhim, H.H.: Self-learning of ANFIS inverse control using iterative learning technique. Int. J. Comput. Appl. 21(8), 24–29 (2011)
Sang, X., Liu, X.: An analytical solution to the TOPSIS model with interval T2 fuzzy sets. Soft Comput. 20, 1213–1230 (2015)
Akbari, M.E., Badamchizadeh, M.A., Poor, M.A.: Implementation of a fuzzy TSK controller for a flexible joint robot. J. Discret. Dyn. Nat. Soc. 2012 (2012)
Akbari, M.E., Alizadeh, G., Khanmohammadi, S., Hassanzadeh, I., Mirzaei, M., Badamchizadeh, M.A.: Design and implementation of a nonlinear \(H_{\infty }\) tracking controller for high elastic joint robot with compensated friction. Int. J. Eng. Sci. Technol. 2, 7691–7702 (2010)
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Jokandan, A.S., Khosravi, A., Nahavandi, S. (2019). Performance Comparison of Type-1 and Type-2 Neuro-Fuzzy Controllers for a Flexible Joint Manipulator. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Lecture Notes in Computer Science(), vol 11953. Springer, Cham. https://doi.org/10.1007/978-3-030-36708-4_51
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DOI: https://doi.org/10.1007/978-3-030-36708-4_51
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