Two-Neural-Network for Partially Unknown Nonlinear Systems
If the unknown nonlinear system is not in a controllable canonical form, men the derivative of the tracking error is unknown. The controller design for the system will be complex. In this paper, we propose two- neural-network structure (TNNS) to learn the unknown nonlinear coefficient of control systems. For the convenience of control, the structure of the two-neural-network is divided into two parts. Each part is a neural-network. Beginning, two neural-networks have the same structure. In the actual control process, one neural-networks output is used to approximate the unknown nonlinear coefficient, and the other neural-network is learning. In control processing, the actions of two neural-networks can be exchanged (this is decided by the switching line). Stability analysis of this control law is given in the paper, and simulation results show that it is useful.
Zheng Pei Shuwei Cheng Keyun Qin
Department of Applied Mathematics, Southwest Jiaotong University. Chengdu 610031 Sichuan, P. R. China
国际会议
上海
英文
8-9
2003-11-09（万方平台首次上网日期，不代表论文的发表时间）