A New Type-2 Fuzzy Systems for Flexible-Joint Robot Arm Control

Document Type : Research Article

Author

Department of Electrical Engineering, Faculty of Engineering, Ilam University, Ilam, Iran

Abstract

 In this paper an adaptive neuro fuzzy inference system based on interval Gaussian type2 fuzzy sets in the antecedent part and Gaussian type-1 fuzzy sets as coefficients of linear combination of input variables in the consequent part is presented. The capability of the proposed method (we named ANFIS2) to function approximation and dynamical system identification is shown. The ANFIS2 structure is very similar to ANFIS, but in ANFIS2, a layer has been added for the purpose of type reduction. An adaptive learning rate based backpropagation with convergence guaranteed is used for parameter learning. Finally, the proposed ANFIS2 are used to control of a flexible joint robot arm that can be used in robot arm. Simulation results shows the proposed ANFIS2 with Gaussian type-1 fuzzy set as coefficients of linear combination of input variables in the consequent part has good performance and high accuracy but more training time. In the simulation, ANFIS2 is compared with conventional ANFIS. The results show that, in abrupt changes, the type-2 fuzzy system proof of efficiency and excellence to the type-1 fuzzy system.

Keywords

dor 20.1001.1.25882953.2019.51.2.2.3

Main Subjects


[1] J. Tavoosi, A. A. Suratgar, M. B. Menhaj, Nonlinear system identification based on a self-organizing type-2 fuzzy RBFN, Engineering Applications of Artificial Intelligence 54 (2016), 26-38.
[2] J. Tavoosi, M. Alaei, B. Jahani, Temperature Control of Water Bath by using Neuro-Fuzzy Controller, 5th Symposium on Advance in Science & Technology, 2011.
[3] 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 5 (12) 1879-1885, 2011.
[4] G.C. Calafiore, A subsystems characterization of the zero modes for flexible mechanical structures, in: Proceedings of 36th IEEE Conference On Decision and Control, San Diego, CA, 1997, pp. 1375–1380.
 [5] O. Castillo, P. Melin, Type-2 Fuzzy Logic: Theory and Applications, Springer-Verlag Berlin Heidelberg, 2008.
[6] S. Huang, M. Chen, Constructing optimized interval type-2 TSK neuro-fuzzy systems with noise reduction property by quantum inspired BFA, Neurocomputing 173 (2015) 1839-1850.
[7] J. Tavoosi, A. A. Suratgar, M. B. Menhaj, Stable ANFIS2 for Nonlinear System Identification, Neurocomputing 182 (2016) 235–246.
[8] R. Shahnazi, Observer-based adaptive interval type-2 fuzzy control of uncertain MIMO nonlinear systems with unknown asymmetric saturation actuators, Neurocomputing 171 (2016) 1053-1065.
[9] S.I. Han, J.M. Lee, Recurrent fuzzy neural network backstepping control for the prescribed output tracking performance of nonlinear dynamic systems, ISA Transactions 53 (2014) 33–43.
 [10] J Tavoosi, MA Badamchizadeh, S Ghaemi, Adaptive Inverse Control of Nonlinear Dynamical System Using Type-2 Fuzzy Neural Networks, Journal of Control 5 (2) (2011).
 [11] YP Asad, A Shamsi, H Ivani, J Tavoosi, Adaptive Intelligent INnverse Control of Nonlinear Systems with Regard to Sensor Noise and Parameter Uncertainty (Magnetic Ball Levitaion System Case Study), International Journal on Smart Sensing and Intelligent Systems 9 (1) 148-169.
 [12] T.C. Lin, C.H. Kuo, V.E. Balas, Real-time fuzzy system identification using uncertainty bounds, Neurocomputing 125 (2014) 195–216.
 [13] J. Soto, P. Melin, Genetic Optimization of Type-2 Fuzzy Integrators in Ensembles of ANFIS Models for Time Series Prediction, Recent Advances on Hybrid Approaches for Designing Intelligent Systems Studies in Computational Intelligence 547 (2014) 79–97.
 [14] H.M. Vaghefi, M.R. Sandgani, M. A. Shoorehdeli, Interval Type-2 Adaptive Network-based Fuzzy Inference System (ANFIS) with Type-2 non-singleton fuzzification, 13th Iranian Conference on Fuzzy Systems (2013) 1–6.
 [15] G.M. Mendez, D.L.A Hernandez, Interval Type-2 ANFIS. In: Innovations in Hybrid Intelligent Systems, Springer, Heidelberg (2007) 64–71.
 [16] S. Bhattacharyya, D. Basu, A. Konar, D.N. Tibarewala, Interval type-2 fuzzy logic based multiclass ANFIS algorithm for real-time EEG based movement control of a robot arm, Robotics and Autonomous Systems 68 (2015) 104–115.
 [17] J. Tavoosi, M.A. Badamchizadeh, A Class of Type-2 Fuzzy Neural Networks for Nonlinear Dynamical System Identification, Neural Computing & Application 23 (3) (2013) 707–717.
 [18] J Tavoosi, AA Suratgar, MB Menhaj, Stability analysis of recurrent type-2 TSK fuzzy systems with nonlinear consequent part, Neural Computing and Applications (2015) 1-10.
 [19] F. Jahangiri, A. Doustmohammadi, M.B. Menhaj, An adaptive wavelet differential neural networks based identifier and its stability analysis, Neurocomputing 77(2012)12–19.
[20] J Tavoosi, AA Suratgar, MB Menhaj, Stability Analysis of a Class of MIMO Recurrent Type-2 Fuzzy Systems, International Journal of Fuzzy Systems (2016) 1-14.
 [21] J. Tavoosi, A. Shamsi Jokandan, 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 5 (3) (2012) 2253-2257.
 [22] M.B.B. Sharifian, A. Mirlo, J. Tavoosi, M. Sabahi, Self-adaptive RBF neural network PID controller in linear elevator, International Conference on Electrical Machines and Systems (ICEMS), 2011.
 [23] Jafar Tavoosi, Majid Alaei, Behrouz Jahani, Neuro – Fuzzy Controller for Position Control of Robot Arm, 5th Symposium on Advances in Science and Technology (5thsastech), 2011.
 [24] M.B.B Sharifian Jafar Tavoosi, Ahad Mirloo, PMSM Position and Speed Estimation by Moving Horizon Estimation (MHE), ICEE19_207, 2011.
[25] 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 62 (2011) 125–137.
[26] J.R. Castro, O. Castillo, L.G. Martínez, an Interval Type-2 Fuzzy Logic Toolbox for Control Applications, IEEE International Conference on Fuzzy Systems, London (2007) 1–6.
 [27] C.J. Chen, S.M. Yang, Z.C. Wung, System Identification by Neuro-Fuzzy Model with Sugeno and Mamdani Fuzzy Rules, Journal of Aeronautics, Astronautics and Aviation, Series A 41 (4) (2009) 263 – 270.
 [28] Evren Gurkan, IIsmet Erkmen, Aydan M. Erkmen, Two-way fuzzy adaptive identification and control of a flexible-joint robot arm, Information Sciences 145 (2002) 13–43.
 [29] Xin Liu, Chenguang Yang, Zhiguang Chen, Min Wang, Chun-Yi Su, Neuro-adaptive observer based control of flexible joint robot, Neurocomputing 275 (2018) 73–82.
 [30] P.Nikdel, M.Hosseinpour, M.A.Badamchizadeh, M.A.Akbari, Improved Takagi–Sugeno fuzzy model-based control of flexible joint robot via Hybrid-Taguchi genetic algorithm, Engineering Applications of Artificial Intelligence33(2014)12–20.
 [31] Wei Yin, Lei Sun, Meng Wang, Jingtai Liu, Nonlinear state feedback position control for flexible joint robot with energy shaping, Robotics and Autonomous Systems 99 (2018) 121–134.