Direct adaptive NN control for a class of discrete-time nonlinear strict-feedback systems

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

来源：Neurocomputing, 2010, 73(13-15): 2498-2505.

DOI：10.1016/j.neucom.2010.06.001

• Neural networks
• Adaptive control
• Nonlinear systems

Based on the backstepping technique, a direct adaptive neural network control algorithm is proposed for a class of uncertain nonlinear discrete-time systems in the strict-feedback form. Neural networks are utilized to approximate unknown functions, and a stable adaptive neural backstepping controller is synthesized. It is proven that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) and the tracking error converges to a small neighborhood of zero by choosing the design parameters appropriately. Compared with the existing results for discrete-time systems, the proposed algorithm needs only less parameters to be adjusted online, therefore, it can reduce online computation burden. A simulation example is employed to illustrate the effectiveness of the proposed algorithm.

• 出版日期2010-8
• 单位辽宁工业大学