Self-adaptive Intelligent Algorithms for Regulating Elastic Coupled Multi-motor System Exposed to Variable Loading | ||||
| Menoufia Journal of Electronic Engineering Research | ||||
| Article 10, Volume 25, Issue 2, July 2016, Page 283-304 | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/mjeer.2016.64120 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| Essam A. El-Araby; Mohammad A. El-Bardini; Nabila M. El-Rabaie | ||||
| Menoufia University, Faculty of Electronic Engineering, Egypt | ||||
| Abstract | ||||
| This paper deals with a well-known type of MIMO electromechanical systems used in industry, which is the multi-motor elastic coupled system (ECMMS). This system is characterized by its complexity, nonlinearity, oscillatory behavior and associated mechanical vibration. The oscillatory behavior of this system is coming from the multiple elastic coupling which causes tortional oscillations and in some cases, tortional resonance which leads to various damages in the system. In literature, classic multi-loop control scheme has been applied to this type of systems. In this paper, a decentralized structure of control system is proposed for controlling the ECMMS. The core controllers of the proposed decentralized control system are self-adaptive local controllers. Two different adaptive algorithms are adopted practically and given comparable results. The main target of the proposed algorithms is to attenuate the effect of mechanical oscillations resulted in the ECMMS effectively. An experimental prototype of ECMMS is used to provide related experimental data. Moreover, stability analysis and convergence criterion based on Lyapunov stability theory is presented in the paper. | ||||
| References | ||||
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This paper deals with a well-known type of MIMO electromechanical systems used in industry, which is the multi-motor elastic coupled system (ECMMS). This system is characterized by its complexity, nonlinearity, oscillatory behavior and associated mechanical vibration. The oscillatory behavior of this system is coming from the multiple elastic coupling which causes tortional oscillations and in some cases, tortional resonance which leads to various damages in the system. In literature, classic multi-loop control scheme has been applied to this type of systems. In this paper, a decentralized structure of control system is proposed for controlling the ECMMS. The core controllers of the proposed decentralized control system are self-adaptive local controllers. Two different adaptive algorithms are adopted practically and given comparable results. The main target of the proposed algorithms is to attenuate the effect of mechanical oscillations resulted in the ECMMS effectively. An experimental prototype of ECMMS is used to provide related experimental data. Moreover, stability analysis and convergence criterion based on Lyapunov stability theory is presented in the paper. 0px; "> [11] Chen, Q. Z.; Meng, G.;Zeng, S. S.: On the algorithms of adaptive neural network-based speed control of switched reluctance machines. Journal of Shanghai Jiaotong University. (Science), 15, pp. 484-491, 2010. [12] Madady, Ali. : An extended PID type iterative learning control, International Journal of Control, Automation and Systems 11.3, pp 470-481 (2013). [13] Ouyang, P. R., V. Pano, and T. Dam.: PID contour tracking control in position domain, Industrial Electronics (ISIE), 2012 IEEE International Symposium on. IEEE, 2012. [14] Zhuo, Wang, Jiang Yanyan, and Wang SHichao.: The application of feedforward PID control in water level control system, World Automation Congress. 2012. [15] Skoczowski, Stanislaw, et al.: A method for improving the robustness of PID control, Industrial Electronics, IEEE Transactions on 52.6, pp. 1669- 1676, 2005. [16] Yu, C. C.; Liu, B. D.: A back-propagation algorithm with adaptive learning rate and momentum coefficient. In Neural Networks, 2002. IJCNN'02. Proceedings of the 2002 International Joint Conference. IEEE. 2, pp. 1218- 1223, 2002. [17] Dietterich, T. G.: Ensemble methods in machine learning. In Multiple classifier systems. Springer Berlin Heidelberg. pp. 1-15 (2000). [18] Mazumdar, J.; Harley, R. G.: Recurrent neural networks trained with backpropagation through time algorithm to estimate nonlinear load harmonic currents. Industrial Electronics, IEEE Transactions. 55, pp.3484-3491, 2008. [19] Sun, Y. J.; Zhang, S.; Miao, C. X.; Li, J. M.: Improved BP neural network for transformer fault diagnosis. Journal of China University of Mining and Technology. 17, pp. 138-142, 2007. [20] M. Polycarpou and P. Ioannou: Learning and Convergence Analysis of Neural- Type Structured Net4works. IEEE Transactions on Neural Networks, Vol. 3, No. 1, PP. 39-50, 1992. [21] C. C. Ku and K. Y. Lee: Diagonal Recurrent Neural Networks for Dynamics Systems Control. IEEE Transactions on Neural Networks, Vol. 6, PP. 144- 156, 1995. [22] N. M. El-Rabie, M. A. El-Bardini, E. A. Gomah, “Adaptive Decentralized Controller for Regulating an Elastic Coupled Multi-Motor System”, is accepted by the committee of the 5th computer science on-line conference (2016), and will be published in the proceedings of “ Advances in Intelligent Systems and Computing”, Springerlink. [23] ARDUINO & GENUINO PRODUCTS > Arduino MEGA 2560 & Genuino MEGA 2560: https://www.arduino.cc/en/Main/ArduinoBoardMega 2560. Last update, 2016. | ||||
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