Adaptive neural output feedback control of nonlinear discrete-time systems

作者：Wen Guo Xing^*; Liu Yan Jun; Tong Shao Cheng; Li Xiao Li

来源：Nonlinear Dynamics, 2011, 65(1-2): 65-75.

DOI：10.1007/s11071-010-9874-4

• Adaptive control
• Uncertainty
• Neural control
• Nonlinear discrete-time systems

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.

• 出版日期2011-7
• 单位北京科技大学; 辽宁工业大学