工矿自动化2011，Vol.37Issue(5)：59-62,4.DOI:CNKI:32-1627/TP.20110428.1943.012
基于改进的PSO算法的球磨机PID神经网络控制系统
PID Neural Network Control System of Ball Mill Based on Modified PSO Algorithm
摘要
Abstract
Ball mill system is a complex multivariable system, which has characteristics of strong coupling, nonlinearity, large delay and slow time-varying, so it is difficult to build its precise mathematical model and achieve satisfying control effect with conventional control strategy. In view of the problem, the paper proposed a multivariable PID neural network control strategy which is independent of the mathematical model of controlled object based on analysis of dynamic characteristics of the ball mill system. In order to improve the performance of controller further, a modified PSO algorithm was used to off-line optimize training of initial value of weights of PID neural network, and the weights were adjusted by BP algorithm on-line, so as to avoid that network falls into the local minimum, and ensure the system cannot overshoot and shake greatly. The simulation results showed that the strategy can guarantee robustness and adaptability of the control system of ball mill in a large range, and solve problems of coupling and time-varying, which has good coupling mechanism and control quality.关键词
球磨机/制粉系统/PID神经网络/多变量系统/解耦/改进粒子群优化算法Key words
ball mill/ pulverizing system/ PID neural network/ multivariable system/ decoupling/ modified particle swarm optimization algorithm分类
矿业与冶金引用本文复制引用
孙杰,韩艳,段勇,崔宝侠..基于改进的PSO算法的球磨机PID神经网络控制系统[J].工矿自动化,2011,37(5):59-62,4.基金项目
国家自然科学基金资助项目(60905054) （60905054）
