Pulse neural network-based adaptive iterative learning control for uncertain robots

被引：6
|
作者：
He, Xiongxiong [1 ]
Zhuang, Hualiang [1 ]
Zhang, Duan [1 ]
Qin, Zhenhua [1 ]
机构：
[1] Zhejiang Univ Technol, Coll Informat Engn, Hangzhou 310023, Zhejiang, Peoples R China
来源：
NEURAL COMPUTING & APPLICATIONS | 2013年 / 23卷 / 7-8期
关键词：
Iterative learning control; Pulse neural networks; Boundary layer; Adaptive control; Sliding mode variable structure control; NONLINEAR-SYSTEMS;
D O I：
10.1007/s00521-012-1157-1
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
An adaptive iterative learning control algorithm based on pulse neural network (PNN) is proposed for trajectory tracking of uncertain robot system. Sliding mode variable structure control is used to improve the robustness to disturbance and perturbation, and boundary layer is used to eliminate the chattering of sliding mode control. In the iterative domain, the unknown parameters are tuned and used for part of the controller. Running in parallel, the PNN can perform real-time state estimation for improving the system convergence. We analyze the stability and convergence of this algorithm by using the Lyapunove-like methodology. The simulation results show that the expected control purpose can be achieved using the proposed algorithm.
引用
收藏
页码：1885 / 1890
页数：6
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