摘要
针对一类非线性大时滞系统,提出了一种不需要精确已知系统的数学模型的无模型控制方法和一种改进的粒子群算法.该控制方法是在泛模型的基础上增加误差反馈修正项,将改进泛模型作为系统模型,根据二次型性能指标设计最优控制律,利用改进的粒子群算法优化控制律中的未知参数,粒子的取值范围通过对闭环系统的收敛性分析来确定.仿真研究表明,闭环控制系统的输出具有较好的响应速度和较小的跟踪误差的优点,证明了所提出方法的有效性.
A model-free control scheme and improved particle swarm optimization （IPSO） algorithm have been pro- posed for a class of nonlinear large time-delay systems, where the precise mathematical models of the controlled sys- tems do not need to be known. The universal model was improved by adding an error feedback correction term, thus, allowing the improved universal model to be used as a system model. On the basis of the improved universal model, the optimal control law was designed according to a quadratic type performance index. The unknown param- eters in the control law are optimized by IPSO and the range of particles was determined by analyzing the conver- gence of the closed-loop system. The simulation research shows that the outputted close-loop control system has the advantages of fast response speed and a small tracking error, proving the effectiveness of the proposed method.
出处
《智能系统学报》
CSCD
北大核心
2013年第3期254-260,共7页
CAAI Transactions on Intelligent Systems
基金
黑龙江省教育厅科学技术研究项目(11544036)
关键词
粒子群优化
非线性系统
无模型控制
大时滞
particle swarm optimization
nonlinear system
model-free control
large time-delay
