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Abstract and Applied Analysis
Volume 2012 (2012), Article ID 471281, 18 pages
Tracking Control Based on Recurrent Neural Networks for Nonlinear Systems with Multiple Inputs and Unknown Deadzone
1Centro Universitario de Ciencias Exactas e Ingenierías, Universidad de Guadalajara, Bulevar Marcelino García Barragán No. 1421, 44430 Guadalajara, JAL, Mexico
2Sección de Estudios de Posgrado e Investigación, ESIME UA-IPN, Avenida de las Granjas No. 682, Colonia Santa Catarina, 02250 Mexico City, DF, Mexico
Received 31 August 2012; Accepted 9 November 2012
Academic Editor: Wenchang Sun
Copyright © 2012 J. Humberto Pérez-Cruz et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citations to this Article [19 citations]
The following is the list of published articles that have cited the current article.
- Haitao Liu, and Tie Zhang, “Adaptive Neural Network Finite-Time Control for Uncertain Robotic Manipulators,” Journal of Intelligent & Robotic Systems, 2013.
- José de Jesús Rubio, “Evolving intelligent algorithms for the modelling of brain andeye signals,” Applied Soft Computing, 2013.
- Floriberto Ortiz Rodríguez, José Jesús Rubio, Carlos R. Mariaca Gaspar, Julio César Tovar, and Marco A. Moreno Armendáriz, “Hierarchical fuzzy CMAC control for nonlinear systems,” Neural Computing and Applications, 2013.
- José de Jesús Rubio, and J. Humberto Pérez-Cruz, “Evolving intelligent system for the modelling of nonlinear systems with dead-zone input,” Applied Soft Computing, 2013.
- Yuehjen E. Shao, Chia-Ding Hou, and Chih-Chou Chiu, “Hybrid Intelligent Modeling Schemes for Heart Disease Classification,” Applied Soft Computing, 2013.
- José de Jesús Rubio, Fidel Meléndez, and Maricela Figueroa, “An observer with controller to detect and reject disturbances,” International Journal of Control, pp. 1–13, 2013.
- José de Jesús Rubio, Zizilia Zamudio, Jaime Pacheco, and Dante Mújica Vargas, “Proportional Derivative Control with Inverse Dead-Zone for Pendulum Systems,” Mathematical Problems in Engineering, vol. 2013, pp. 1–9, 2013.
- Dante Mujica-Vargas, and Diana M. Vazquez, “Acquisition system and approximation of brain signals,” Iet Science Measurement & Technology, vol. 7, no. 4, pp. 232–239, 2013.
- Qun Dai, Zhongchen Ma, and QiongYu Xie, “A Two-Phased and Ensemble Scheme Integrated Backpropagation Algorithm,” Applied Soft Computing, 2014.
- Carlos Aguilar-Ibañez, Julio A. Mendoza-Mendoza, Miguel S. Suarez-Castanon, and Jorge Davila, “A nonlinear robust PI controller for an uncertain system,” International Journal of Control, pp. 1–9, 2014.
- Maher El'arbi, and Chokri Ben Amar, “Image authentication algorithm with recovery capabilities based on neural networks in the DCT domain,” Iet Image Processing, vol. 8, no. 11, pp. 619–626, 2014.
- Lei Yu, Shumin Fei, Lining Sun, Jun Huang, and Gang Yang, “Design of Robust Adaptive Neural Switching Controller for Robotic Manipulators with Uncertainty and Disturbances,” Journal of Intelligent & Robotic Systems, 2014.
- Yuehjen E. Shao, “Body Fat Percentage Prediction Using Intelligent Hybrid Approaches,” The Scientific World Journal, vol. 2014, pp. 1–8, 2014.
- S. Puga-Guzmán, J. Moreno-Valenzuela, and V. Santibáñez, “Adaptive Neural Network Motion Control of Manipulators with Experimental Evaluations,” The Scientific World Journal, vol. 2014, pp. 1–13, 2014.
- J. Humberto Pérez-Cruz, José de Jesús Rubio, Rodrigo Encinas, and Ricardo Balcazar, “Singularity-Free Neural Control for the Exponential Trajectory Tracking in Multiple-Input Uncertain Systems with Unknown Deadzone Nonlinearities,” The Scientific World Journal, vol. 2014, pp. 1–10, 2014.
- Lin-Lin Wang, Hong-Jian Wang, and Li-Xin Pan, “H-infinity control for path tracking of autonomous underwater vehicle motion,” Advances In Mechanical Engineering, vol. 7, no. 5, 2015.
- Changchun Cai, Bing Jiang, and Lihua Deng, “General Dynamic Equivalent Modeling of Microgrid Based on Physical Background,” Energies, vol. 8, no. 11, pp. 12929–12948, 2015.
- Shijoh Vellayikot, and M. V. Vaidyan, “ANN Approach for State Estimation of Hybrid Systems and Its Experimental Validation,” Mathematical Problems in Engineering, vol. 2015, pp. 1–13, 2015.
- Yuehjen E. Shao, and Ke-Shan Lin, “Change Point Determination for an Attribute Process Using an Artificial Neural Network-Based Approach,” Discrete Dynamics in Nature and Society, vol. 2015, pp. 1–7, 2015.