Dynamic Recurrent Neural Network Based Indirect Adaptive Control for Nonlinear Systems | ||
| Menoufia Journal of Electronic Engineering Research | ||
| Article 6, Volume 26, Issue 1, January 2017, Pages 79-98 | ||
| Document Type: Original Article | ||
| DOI: 10.21608/mjeer.2017.63423 | ||
| Authors | ||
| Mohamed A. Hussien; Tarek A. Mahmoud; Mohamed I. Mahmoud | ||
| Dept. of Industrial Electronics and Control Eng., Faculty of Electronic Engineering, Menoufia University, Egypt | ||
| Abstract | ||
| In this study, an indirect adaptive controller based on dynamic recurrent neural network (DRNN) is developed in the form of the internal model control structure. This control method includes two learning phases, i.e., off-line and on-line learning. In the offline learning, the DRNN is learned by the epoch wise back propagation through time (BPTT) method to represent the forward dynamics of the system to be controlled. In the online phase, the DRNN is used as the internal model of the controlled system and its parameters can be trained by the Truncated BPTT to cope with the possible change in the system dynamics. Hence, the mathematical inversion of the DRNN internal model is computed online to act as the forward controller. Finally, the controller is then obtained by cascading this inverse model with a robust filter and a linear compensator to improve the closed loop performance. The proposed method is applied to a continuous stirred tank reactor (CSTR) with time varying behavior to evaluate the performance of controller. | ||
| References | ||
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to; -webkit-text-stroke-width: 0px; ">[1] S. Kalogirou, “Artificial intelligence for the modeling and control of combustion processes: A Review” Progress in Energy and Combustion Science, vol. 29, pp. 515–566,2003. [2] D. C. Psichogios and L. H. Ungar, “Direct and indirect model-based control using artificial neural networks,” Ind. Eng. Chem. Res., vol. 30, pp. 25–64, 1991. [3] Cabrera, J., Narendra, K., “Issues in the application of neural networks for tracking based on inverse control, ”IEEE Transaction on Automatic Control, vol. 44, pp. 2007–2027, 1999. [4] Hussain, M.A., Kershenbau, L.S., “Implementation of neural networkbased inverse-model control strategies on an exothermic reactor,”ScienceAsia, vol. 27, pp. 41–50, 2001. [5] Awais, M.,“Application of internal model control methods to industrial combustion”. Applied Soft Computing, vol. 5, pp. 223–233, 2005. [6] Chidrawar, S., Patre, B., “Implementation of neural network for internal model control and adaptive control,”Proceedings of the International Conference on Computer and Communication Engineering, Kuala Lumpur, Malaysia, 2008. [7] Hua Deng, Zhen Xu, Han-Xiong Li." A novel neural internal model control for multi-input multi-output nonlinear discrete-time processes”.Journal of Process Control, vol. 19, 1392–1400, 2009 px; -webkit-text-size-adjust: auto; -w[8] J. Suykens, “Nonlinear modeling and support vector machines,” IEEE Instrumentation and Measurement Technology Conference, (Budapest Hungary), 21-23 May 2001. [9] J. Suykens, J. Vandewalle, and B. D. Moor, “Optimal control by least squares support vector machines,” Neural Networks, vol. 14, pp. 23–35, 2001. [10] L. Bin, S. Hong-ye, and C. Jian , “New predictive control algorithms based on least squares support machines,” Journal of Zhejiang University Science, vol. 6 , no. 5, pp. 440–446, 2005. [11] L. Li-Juan, S. Hong-Ye, and C. Jian, “Generalized predictive control with online least squares support machines,” ActaAutomaticaSinica, vol. 33, no. 11, pp. 1182–1188, 2007. [12] W. Yao-Nan and Y. Xiao-Fang, “SVM approximate-based internal model control strategy,” ActaAutomaticaSinica, vol. 34, no. 2, pp. 696–702,2008. [13] Tarek. A. Mahmoud, “Multi resolution wavelet least squares support vector machine network for nonlinear system modeling,” Proceedings of 15th International Conference in Automation and Robotics MMAR2010, Miedzyzdroje, Poland, 23-26 August 2010. [14] Tarek. A. Mahmoud, “Adaptive control scheme based on the least squares support vector machine network,” International Journal of Applied Mathematics and Computer Science, vol. 21, no. 4, pp. 685–696, 2011. [15] Tarek A. Mahmoud, Lamiaa M. Elshenawy“ Direct Adaptive Control Based on LS-SVM Inverse Model For Nonlinear Systems ,” Proceedings of 19th International Conference in Automation and Robotics MMAR2014, Miedzyzdroje, Poland, 2- 5 September 2014. [16] Chun-Fei H. , Chin-Min L. , Ang-Bung T., and Chao-Ming C., “Adaptive control for MIMO uncertain nonlinear systems using recurrent wavelet neural network,” International Journal of Neural Systems vol. 22, no.1, 2012. [17] Williams R. J., Zisper D., “An efficient gradient-based algorithm for online training of recurrent networks”. Neural Computation, vol .2, no. 4, pp: 490–50, 1990 [18] B. A. Pearlmutter, “Gradient calculations for dynamic recurrent neural networks: A survey,” IEEE Trans. Neural Networks, vol. 6, no. 5, pp. 1212–1227, 1995. [19] Zamarreno J. M., Vega P., "State-Space Neural Network, Properties and Application", Neural Networks, vol. 11, pp. 1099 - 1112, 1998. [20] Ku CC and Lee KY, “Diagonal recurrent development neural networks for dynamic systems control,”IEEE Transactions on Neural Networks vol. 6 , No .1, pp. 144–156, 1995. [21] Levin A. and Narendra K., “Control of nonlinear dynamical systems using neural networks. ii. observability, identification, and control,” IEEE Transactions on Neural Networks vol. 7, no. 1, pp. 30–42, 1996. | ||
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