基于遗传算法的非高斯系统随机分布控制

传统的控制理论并未考虑工业过程中的一些不确定因素，而这些不确定因素在生产过程中对系统的能源损耗和精度都有很大的影响.为了解决上述问题，本文研究一种基于数据的非高斯随机分布系统优化控制策略，该策略采用核密度估计（KDE）方法完全基于输出数据估计输出概率密度函数（PDF），根据控制目标建立性能指标函数，采用遗传算法优化性能指标函数，实现输出PDF对目标PDF的跟踪.以磨矿系统为模型进行仿真，采用PDF表征粒度分布（PSD）.仿真结果表明，基于遗传算法的非高斯随机分布最优控制算法能有效地实现随机分布控制系统的控制目标，为实际工业生产提供参考.

Genetic algorithm-based stochastic distribution control for non-Gaussian systems

Traditional control theory does not consider some uncertain factors in the industrial process,which has a significant impact on energy loss and system accuracy in production process.This paper considers the data-based optimization control strategies for non-Gaussian stochastic systems.Kernel density estimation was used to estimate the output probability density functions (PDFs) on the basis of collected output data.Firstly,the performance index function was established based on the control objectives.Secondly,the performance index function was optimized by a genetic algorithm.The simulation takes the grinding system as a model and uses PDFs to characterize the particle size distribution.Simulation results show that the genetic algorithm-based stochastic distribution control for non-Gaussian systems can effectively achieve the control target of the stochastically distributed control system and provides reference for practical industrial production.