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.
DOI
10.1109/TNN.2008.2003290
Publication Date
2008-11-05
Publication Title
IEEE Transactions on Neural Networks
Volume
19
Publisher
IEEE
ISSN
1045-9227
Embargo Period
2024-11-22
First Page
1873-1886
Last Page
1873-1886
Recommended Citation
Yang, C., Ge, S., Xiang, C., Chai, T., & Lee, T. (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, pp. 1873-1886-1873-1886. IEEE: Available at: https://doi.org/10.1109/TNN.2008.2003290
