Suman K. Ghosh and Prasanta Sarkar
[1] R.H. Middleton and G.C. Goodwin, Improved finite word length characteristics in digital control using delta operators, IEEE Transactions on Automatic Control, 31(11), 1986, 1015–1021. [2] R.H. Middleton and G.C. Goodwin, Digital control and estimation: a unified approach (New Jersey: Prentice Hall, 1990). [3] K.S. Narendra and K. Parthasarathy, Identification and control of dynamical systems using neural networks, IEEE Transactions on Neural Networks, 1, 1990, 4–27. [4] C.F. Chuan and H.K. Khalil, Adaptive control of a class of nonlinear discrete-time systems using neural networks, IEEE Transactions on Automatic Control, 40, 1995, 791–801. [5] S. Jagannathan and F.L. Lewis, Multilayer discrete-time neural-net controller with guaranteed performance, IEEE Transactions on Neural Networks, 7(1), 1996, 107–130. [6] K.S. Narendra and S. Mukhopadhyay, Adaptive control using neural networks and approximate models, IEEE Transactions on Neural Networks, 8(3), 1997, 475–485. [7] J.B.D. Cabrera and K.S. Narendra, Issues in the application of neural networks for tracking based on inverse control, IEEE Transactions on Automatic Control, 44(11), 1999, 2007–2027. [8] G.L. Plett, Adaptive inverse control of linear and nonlinear systems using dynamic neural networks, IEEE Transactions on Neural Networks, 14(2), 2003, 360–376. [9] H.X. Li and H. Deng, An approximate internal model-based neural control for unknown nonlinear discrete processes, IEEE Transactions on Neural Networks, 17(3), 2006, 659–670. [10] M. Sakamoto, T. Matsushita, Y. Mizukami, and K. Tanaka, Model reference adaptive control using delta-operator with neural network for pneumatic servo system, SICE Annual Conference, Osaka, Aug. 5–7, 2002, 771–776. [11] S.S. Ge, G.Y. Li, and T.H. Lee, Adaptive NN control for a class of strict-feedback discrete-time nonlinear systems, Automatica, 39(5), 2003, 807–819. [12] S.S. Ge, C. Yang, and T.H. Lee, Adaptive predicative control using neural network for a class of pure-feedback systems in discrete time, IEEE Transactions on Neural Networks, 19(9), 2008, 1599–1614. [13] J. Vance and S. Jagannathan, Discrete-time neural network output feedback control of nonlinear discrete-time systems in non-strict form, Automatica, 44(4), 2008, 1020–1027. [14] H. Deng, H.X. Li, and Y.H. Wu, Feedback-linearization-based neural adaptive control for unknown nonaffine nonlinear discrete-time systems, IEEE Transactions on Neural Networks, 19(9), 2008, 1615–1625. [15] W. Chen, Adaptive NN control for discrete-time pure-feedback systems with unknown control direction under amplitude and rate actuator constraints, ISA Transaction, 48(3), 2009, 304–311. [16] C.G. Yang, S.S. Ge, and T.H. Lee, Adaptive control of a class of discrete-time MIMO nonlinear systems with uncertain coupling, International Journal of Control, 83(10), 2010, 2120–2133. [17] N. Dong and Z. Chen, A novel data based control method based upon neural network and simultaneous perturbation stochastic approximation, Nonlinear Dynamics, 67, 2012, 957–963. [18] G.X. Wen, Y.J. Liu, and C.L. Philip Chen, Direct adaptive robust NN control for a class of discrete-time nonlinear strict-feedback SISO systems, Neural Computer and Application, 21(6), 2012, 1423–1431. [19] Z. Xu, Q. Song, and D. Wang, Recurrent neural tracking control based on multivariable robust adaptive gradient-descent training algorithm, Neural Computer and Application, 21, 2012, 1745–1755. [20] Y.J. Liu, C.L. Philip Chen, G.X. Wen, and S. Tong, Adaptive neural output feedback tracking control for a class of uncertain discrete-time nonlinear systems, IEEE Transactions on Neural Networks, 22(7), 2011, 1162–1167. [21] Y. Cui, Y.J. Liu, and D.J. Li, Robust adaptive NN control for a class of uncertain discrete-time nonlinear MIMO systems, Neural Computer and Application, 22(3–4), 2013, 747–754. [22] P. Sarkar, Reduced order modeling and controller design in delta domain, Ph.d. Thesis, Department of Electrical Engineering, Indian Institute of Technology, Kharagpur, 2001. [23] S. Haykin, Neural networks, a comprehensive foundation, second ed. (Upper Saddle River, New Jersey, USA: Pearson Education, Inc., 1998). [24] R. Hecht-Nielsen, Theory of the back-propagation neural network, Proc. Int. Joint Conf. on Neural Networks, 1989, I-593–605. [25] K. Hornik, M. Stinchcombe, and H. White, Multilayer feedforward networks are universal approximators, Neural Networks, 2, 1989, 359–366.
Important Links:
Go Back