Real-Time Trajectory Tracking Of Mobile Robots Based On Sliding Mode Control Using Irbfnns

References

• I. Kolmanovsky and N. H. McClamroch. “Developments in nonholonomic control problems.” IEEE Control systems Magazine, Vol. 15:pp. 20–36, 1995.
   Google Scholar
• Y. Kanayama, Y. Kimura, E Miyazaki, and T. Noguchi, “A stable tracking control method for an autonomous mobile robot.” in Proc. Int. IEEE Conf. Robotics and Automation, pp. 384–389, 1990.
   Google Scholar
• D. H. Kim and T. H. Oh. “Tracking control of atwo-wheeled mobile robot using input-output linearization.” Control Engineering Practice, vo1.7: pp. 369–373, 1999.
   Google Scholar
• Z. P. Jiang and H. Nijmeijer. “Tracking control of mobile robots: a case study in backstepping.” Auotomatica, 33(7): pp. 1393–1399, 1997.
   Google Scholar
• F. Pourboghrat and M. P. Karlsson. “Adaptive control of dynamic mobile robots with nonholonomic constraints.” Computers and Electrical Engineering, vol. 28:pp. 241–253, 2002.
   Google Scholar
• T. Fukao, H. Nakagawa, and N. Adachi. “Adaptive tracking control of a nonholonomic mobile robot.” IEEE Trans. Robotics and Automation, 16(5); pp. 609–615, 2000.
   Google Scholar
• R. Fierro and F. L. Lewis. “Control of a nonholonomic mobile robot using neural networks.” IEEE Trans. Neural Networks, 9(4): pp. 589–600, 1998.
   Google Scholar
• S. X. Yang and T. Hu. “An efficient neural network approach to real-time control of a mobile robot with unknown dynamics.” Differential Equations and Dynamical Systems, 10(1–2): pp. 151–168, 2002.
   Google Scholar
• M. L. Corradini, G. Ippoliti, and S. Longhi. “Neural networks based control of mobile robots: development and experimental validation.” Journal of Robotics Systems, 20(10):pp.587–600, 2003.
   Google Scholar
• J. M. Yang and J. H. Kim. “Sliding mode control for trajectory tracking of nonholonomic wheeled mobile robots.” IEEE Trans. Robotics and Automation, 15(3): pp. 578–587, 1999.
   Google Scholar
• S. J. Huang, K. S. Huang, and K. C. Chiou. “Development and application of a novel radial basis function sliding mode controller.” Mechatronics, 13: pp.313–329, 2003.
   Google Scholar
• J. J. Slotine and W. Li. Applied Nonlinear Control. Enlewood Cliffs: Prentice Hall, ch.7, 1991.
   Google Scholar
• R. J. Wai. “Tracking control based on neural network strategy for robot manipulator.” Neurocomputing, vo151: pp. 425–445, 2003.
   Google Scholar
• L. K. Wong, F. H. F. Leung, and P. K S. Tam. “A chattering elimination algorithm for sliding mode control of uncertain non-linear systems.” Mechatronics, vol. 8: pp. 765–775, 1998.
   Google Scholar
• T. Poggio and F. Girosi. “Regularization algorithm for learning that are equivalent to multilayer networks.” Science, 247:pp. 978–982, 1990.
   Google Scholar
• F. C. Chen and H. K Khali. “Adaptive control of non-linear systems using neural networks.”Int J Control, 55(6):pp. 1299–1317, 1992.
   Google Scholar
• C. C. Liu and F. C. Chen. “Adaptive control of non-linear continuous time systems using neural networks general relative degree and MIMO case.” Int J Control, 58(2): pp. 317335, 1993.
   Google Scholar
• S. Lu and T. Briar. “Robust non-linear system identification using neural-network models.” IEEE Trans Neural networks, 9(3):pp. 407–429, 1998.
   Google Scholar
• R M. Sanner and J. J. Slotine. “Gaussian network for direct adaptive control.” IEEE Trans Neural Networks, 3:pp. 837–863, 1992.
   Google Scholar
• A. Saha and J. D. Keeler. “Algorithms for better representation and faster learning in radial basis function network.” in Advances in Neural Information Processing Systems 2, D. S. Touretzky, ed. San Mateo, CA: Morgan Kaufmann: pp. 482–489, 1990.
   Google Scholar
• S. Chen, C. E N. Cowan, and P. N. Grant. “Orthogonal Least Squares Learning Algorithms for Radial Basis Function Networks.” IEEE Trans. Neural Networks, 2:pp. 302–309, 191.
   Google Scholar
• J. Moody and C. Darken. “Fast Learning in Networks of Locally-Tuned Processing Units.” Neural Computation, l:pp. 281–294, 1989.
   Google Scholar
• M. A Kubat. “Hyperrectangle-Based Method that Creates RBF Networks.” in Radial Basis Function Networks 1, R. J. Howlett and L. C. Jain, Eds., Heidelberg: Physica-Verlag, pp. 32–50, 2001.
   Google Scholar
• C. J. Platt. “A Resource allocating network for function interpolation.” Neural Computation, 4(4): pp. 473–493, 1991.
   Google Scholar
• V. Kadirkamanathan and M. Niranjan. “A Function Estimation Approach to Sequential Learning with Neural Networks.” Neural Computation, 5:pp. 954–975, 1993.
   Google Scholar
• G. Yang, J. Lu, and Z. Y. Liu. “A New Sequential Learning Algorithm for RBF Neural Networks.” Science of China-Series E, 47:pp. 447–460, 2004.
   Google Scholar