Design of Hybrid Neural Controller for Nonlinear MIMO System Based on NARMA-L2 Model

References

• K. Uçak, and GÖ Günel, “Generalized self-tuning regulator based on online support vector regression,” Neural. Comput. Appl., Vol. 28, no. 1, pp. 775–801, 2017.
   Google Scholar
• M. M. Arefi, and M. R. Jahed-Motlagh, “Observer-based adaptive neural control of uncertain MIMO nonlinear systems with unknown control direction,” Int. J. Adapt Control Signal Process., Vol. 27, no. 9, pp. 741–754, 2013.
   Web of Science ®Google Scholar
• R.-E. Precup, and H. Hellendoorn, “A survey on industrial applications of fuzzy control,” Comput. Ind., Vol. 62, no. 3, pp. 213–226, 2011.
   Web of Science ®Google Scholar
• R. Kumar, S. Srivastava, and J. R. P. Gupta, “Comparative study of neural networks for control of nonlinear dynamical systems with lyapunov stability-based adaptive learning rates,” Arab. J. Sci. Eng., Vol. 43, no. no.6, pp. 2971–2993, 2018.
   Web of Science ®Google Scholar
• O. I. Abiodun, A. Jantan, A. E. Omolara, K. V. Dada, N. A. Mohamed, and H. Arshad, “State-of-the-art in artificial neural network applications: A survey,” Heliyon, Vol. 4, no. 11, 2018, e00938.
   PubMed Web of Science ®Google Scholar
• Z. Wang, C. Hu, Y. Zhu, et al., “Neural network learning adaptive robust control of an industrial linear motor-driven stage with disturbance rejection ability,” IEEE Trans. Ind. Inform, Vol. 13, no. 5, pp. 2172–2183, 2017.
   Web of Science ®Google Scholar
• K. Uçak, and G. Öke Günel, “A novel adaptive NARMA-L2 controller based on online support vector regression for nonlinear systems,” Neural Process. Lett., Vol. 44, no. 3, pp. 857–886, 2016.
   Web of Science ®Google Scholar
• K. S. Narendra, and S. Mukhopadhyay, “Adaptive control using neural networks and approximate models,” IEEE Trans. Neural Netw., Vol. 8, no. 3, pp. 475–485, 1997.
   PubMed Web of Science ®Google Scholar
• O. De Jesus, A. Pukrittayakamee, and M. T. Hagan. “A comparison of neural network control algorithms,” In: Proceedings of the International Joint Conference on Neural Networks IJCNN’01, (Cat. No.01CH37222), 2001, vol.1, pp. 521–526.
   Google Scholar
• Y. Jiang, D. Necsulescu, and J. and Sasiadek, “Robotic unmanned aerial vehicle trajectory tracking control,” IFAC Proc, Vol. 39, no. 15, pp. 97–102, 2006.
   Google Scholar
• M. George, and K. P. Basu, “NARMA-L2 controlled variable frequency three-phase induction motor drive,” Eur. J. Sci. Res., Vol. 70, no. 1, pp. 98–111, 2012.
   Google Scholar
• S. K. Valluru, M. Singh, and N. Kumar, “Implementation of NARMA-L2 Neuro controller for speed regulation of series connected DC motor,” IEEE 5th India International Conference on Power Electronics (IICPE), Delhi, India, pp. 1–7, 2012. doi: 10.1109/IICPE.2012.6450518.
   Google Scholar
• G. P. Reddy, P. R. Krishna, and C. Swetha, “A stable artificial neural network based NARMA-L2 control of a bioreactor with input multiplicities,” In Proceedings of the World Congress on Engineering and Computer Science, Vol. 2, pp. 978–988, 2013.
   Google Scholar
• J. Kananai, and R. Chancharoen, “Stiff PD and narma L2 synergy control for a nonlinear mechanical system,” Eur. J. Sci. Res, Vol. 77, no. 3, pp. 350–361, 2012.
   Google Scholar
• F. Fourati, S. Baklouti, and H. Moalla, “NARMA-L2 neural control of a bioreactor,” 4th International Conference on Systems and Control (ICSC), Sousse, Tunisia, pp. 504–509, 2015, doi: 10.1109/ICoSC.2015.7153307.
   Google Scholar
• A. T. Humod, A. H. Almukhtar, H. B. Ahmed,”Direct torque control for Permanent Magnet Synchronous Motor based on NARMA-L2 controller,” Eng. & Tech. J., Part (A), vol. 34, no.3, pp. 464–482, 2016.
   Google Scholar
• I. A. Kheioon, B. S. Munahi, and A. H. Abdulaali, “A New fuzzy-NARMA L2 controller design for active suspension system,” Basrah J. Eng Sci, Vol. 17, no. 2, pp. 43–50, 2017.
   Google Scholar
• Y. Al-Dunainawi, M. F. Abbod, and A. Jizany, “A new MIMO ANFIS-PSO based NARMA-L2 controller for nonlinear dynamic systems,” Eng. Appl. Artif. Intell., Vol. 62, pp. 265–275, 2017.
   Web of Science ®Google Scholar
• A. S. Al-Araji, and S. J. E. Al-Zangana, “Design of New hybrid neural controller for nonlinear CSTR system based on identification,” J. Eng, Vol. 25, no. 4, pp. 70–89, 2019.
   Google Scholar
• S. Abderrahmene, C. Mohammed, K. Abderrahmane, and H. Rachida, “Neural network NARMA-L2 control of a Twin Rotor MIMO System,” International Conference on Advanced Electrical Engineering (ICAEE), Algiers, Algeria, pp. 1–6, 2019, doi: 10.1109/ICAEE47123.2019.9015131.
   Google Scholar
• R. Celikel, “Speed control of BLDC using NARMA-L2 controller in single link manipulator,” Balkan J. Elect and Comp Eng, Vol. 7, no. 2, pp. 143–148, 2019.
   Google Scholar
• E. Petlenkov, “Neural networks-based identification and control of nonlinear systems: ANARX model-based approach,” Ph.D. dissertation, Department of Computer Control, Tallinn University Of Tecnology, Estonia, 2007.
   Google Scholar
• S. Purwar, I. N. Kar, and A. N. Jha, “Nonlinear system identification using neural networks,” IETE. J. Res., Vol. 53, no. 1, pp. 35–42, 2007.
   Web of Science ®Google Scholar
• K. El Hamidi, M. Mjahed, A. El Kari, and H. Ayad, “Adaptive control using neural networks and approximate models for nonlinear dynamic systems,” Model Simulat Eng, Vol. 2020, pp. 13, 2020., Article ID 8642915
   Web of Science ®Google Scholar
• H. J. Asl, G. Oriolo, and H. Bolandi, “An adaptive scheme for image-based visual servoing of an underactuated UAV,” Int. J. Robot. Autom, Vol. 29, no. 1, pp. 92–104, 2014.
   Web of Science ®Google Scholar
• X. Shao, N. Liu, Z. Wang, W. Zhang, and W. Yang, “Neuroadaptive integral robust control of visual quadrotor for tracking a moving object,” Mech. Syst. Signal Pr., Vol. 136, pp. 106513, 2020.
   Web of Science ®Google Scholar
• M. Zare, F. Pazooki, and S. Etemadi Haghighi, “Quadrotor UAV position and altitude tracking using an optimized fuzzy-sliding mode control,” IETE. J. Res., 1–15, 2020. doi: 10.1080/03772063.2020.1793694.
   Web of Science ®Google Scholar
• K. El Hamidi, M. Mjahed, A. El Kari, and H. Ayad, “Neural network and fuzzy-logic-based self-tuning PID control for quadcopter path tracking,” Stud. Inf. Control, Vol. 28, no. 4, pp. 401–412, 2019.
   Web of Science ®Google Scholar