基于混沌-神经网络模型最优控制及应用

由于非线性混沌时间序列内部确定的规律性，其重构相空间具有高精度短期预测性．为此，为了实现非线性、大时滞系统的自适应控制，文章根据具有混沌特性非线性、大时滞系统的时间序列重构相空问，计算相空间饱和嵌入维数、最大Lyapunov指数和系统的可预报尺度，并以此为指导，建立神经网络预测模型对系统作高精度的短期预测；在此基础上，通过反馈校正，将校正误差和控制增量引入性能函数寻优得最优控制决策，实现了对非线性、大时滞系统高精度的自适应预测控制．将该控制决策应用在锅炉过热汽温控制中，仿真表明该控制的有效性、快速性和鲁棒性．

Optimal control and application based on chaos-neural network model

Because of chaotic time series internal certain regularity, their reconstructing chaotic attractors phase space can be used for high precision short-term forecast. Therefore, in order to realize adaptive control of a nonlinear big-lagged system, the phase space is reconstructed, its saturated embedded dimension, the maximal Lyapunov exponent and the forecast measure are calculated by the nonlinear big-lagged system time series in this paper. After that, a neural-network model is constructed, which can make high precision short-term forecast for the system. On the basis of this, an optimal controller is designed by a feedback rectification term and the control input error is introduced into a performance function, and then a high precision adaptive forecast control is realized to the nonlinear big-lagged system. The controller is applied to the overheat steam temperature system of drum boiler. The validity, the high-speed and the robustness of the control system are demonstrated by simulation results.