Two-Neural-Network for Partially Unknown Nonlinear Systems

摘要

If the unknown nonlinear system is not in a controllable canonical form, then 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-network's 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.