中国机械工程2012，Vol.23Issue(22)：2732-2738,7.DOI:10.3969/j.issn.1004-132X.2012.22.017
基于粒子群优化的神经网络自适应控制算法
Neural Network Adaptive Control Algorithm Modified by PSO
摘要
Abstract
As in some situations the control objects are nonlinear or variable,the traditional PID control can not meet the requirements and the PID parameters need to be constantly adjusted by empirical knowledge. A new neural network adaptive control algorithm modified by PSO was proposed herein. It consisted of the traditional PID,BP neural network and the PSO global optimization algorithm which was used to optimize the initial weights of BP neural network. The optimized BP neural network was then used to adjust PID parameters on - line. Variation operation was introduced to the optimization process and the comprehensive influence on PSO and BP introduced by the choice of the activation function gain and the number of hidden layers was considered. The algorithm can improve the problem more effectively that neural network goes easily into the local minimum value and has slow convergence speed. Simulation results show that the proposed method has greatly improved in accuracy and real - time performance.关键词
PSO算法/BP神经网络/PID控制/自适应控制Key words
particle swarm optimization(PSO) algorithm/ BP neural network/ PID control/ adaptive control分类
信息技术与安全科学引用本文复制引用
徐胜男,周祖德,艾青松,刘泉..基于粒子群优化的神经网络自适应控制算法[J].中国机械工程,2012,23(22):2732-2738,7.基金项目
国家自然科学基金资助项目(50905133) （50905133）
湖北省自然科学基金重大国际合作交流项目(2009BFA006) （2009BFA006）
