Please use this identifier to cite or link to this item: https://doi.org/10.1109/TNN.2008.2003290
Title: Output feedback NN control for two classes of discrete-time systems with unknown control directions in a unified approach
Authors: Yang, C.
Ge, S.S. 
Xiang, C. 
Chai, T.
Lee, T.H. 
Keywords: Discrete Nussbaum gain
Discrete-time system
Neural networks (NNs)
Nonlinear autoregressive moving average with exogenous inputs (NARMAX) systems
Pure-feedback system
Unknown control directions
Issue Date: 2008
Source: Yang, C., Ge, S.S., Xiang, C., Chai, T., Lee, T.H. (2008). Output feedback NN control for two classes of discrete-time systems with unknown control directions in a unified approach. IEEE Transactions on Neural Networks 19 (11) : 1873-1886. ScholarBank@NUS Repository. https://doi.org/10.1109/TNN.2008.2003290
Abstract: In this paper, output feedback adaptive neural network (NN) controls are investigated for two classes of nonlinear discrete-time systems with unknown control directions: 1) nonlinear pure-feedback systems and 2) nonlinear autoregressive moving average with exogenous inputs (NARMAX) systems. To overcome the noncausal problem, which has been known to be a major obstacle in the discrete-time control design, both systems are transformed to a predictor for output feedback control design. Implicit function theorem is used to overcome the difficulty of the nonaffine appearance of the control input. The problem of lacking a priori knowledge on the control directions is solved by using discrete Nussbaum gain. The high-order neural network (HONN) is employed to approximate the unknown control. The closed-loop system achieves semiglobal uniformly-ultimately-bounded (SGUUB) stability and the output tracking error is made within a neighborhood around zero. Simulation results are presented to demonstrate the effectiveness of the proposed control. © 2008 IEEE.
Source Title: IEEE Transactions on Neural Networks
URI: http://scholarbank.nus.edu.sg/handle/10635/51009
ISSN: 10459227
DOI: 10.1109/TNN.2008.2003290
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