Modeling and Control of Nonlinear Discrete-time Systems Based on Compound Neural Networks

An adaptive inverse controller for nonliear discrete-time system is proposed in this paper. A compound neural network is constructed to identify the nonlinear system, which includes a linear part to approximate the nonlinear system and a recurrent neural network to minimize the difference between the linear model and the real nonlinear system. Because the current control input is not included in the input vector of recurrent neural network (RNN), the inverse control law can be calculated directly. This scheme can be used in real-time nonlinear single-input single-output (SISO) and multi-input multi-output (MIMO) system control with less computation work. Simulation studies have shown that this scheme is simple and affects good control accuracy and robustness.

一种改进的空空导弹三维变结构导引律

空空导弹采用变结构导引律攻击大机动目标时存在视线角速率易发散和变结构参数项不易确定等缺点。针对上述问题,在变结构控制理论的基础上,提出一种基于变指数趋近律的参数自适应变结构三维导引律,利用多输入多输出RBF神经网络对变结构参数项进行在线调整。仿真结果表明,该导引律能有效削弱变结构控制的抖振,进一步提高导引精度,具有很强的自适应能力和鲁棒性,满足空空导弹战术指标要求。