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Wen, Guo-Xing; Liu, Yan-Jun; Tong, Shao-Cheng; Li, Xiao-Li

Adaptive neural output feedback control of nonlinear discrete-time systems. (English) Zbl 1235.93110

Nonlinear Dyn. 65, No. 1-2, 65-75 (2011).
Summary: Based on the backstepping technique, an adaptive Neural Network (NN) based output feedback controller is proposed to achieve a desired tracking performance for a class of discrete-time nonlinear systems, which are represented in non-strict-feedback form. The neural networks are utilized to approximate unknown functions in the systems. The adaptive output feedback controller needs only to adjust less adaptive parameters, therefore it is clear that the proposed approach can reduce on-line computation burden. It is proven that all the signals in the closed-loop system are Semi-Global Uniformly Ultimately Bounded (SGUUB) and the tracking error converges to a small neighborhood of zero by choosing the design parameters appropriately. A simulation example is used to verify the effectiveness of the proposed approach.

Cited in 7 Documents

MSC:

93B52 Feedback control
93C55 Discrete-time control/observation systems
93C10 Nonlinear systems in control theory
93C40 Adaptive control/observation systems

Keywords:

adaptive control; uncertainty; neural control; nonlinear discrete-time systems
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References:

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