基于遗传算法的低压液压管路模型参数识别

Parameter identification of hydraulic low pressure pipeline transient model using genetic algorithms

为了合理预测伴随气泡和气穴的低压液压管路压力瞬态脉动,提出了用改进遗传算法对低压液压管路压力瞬态脉动模型进行参数辨识的新方法。给出了用来描述管路流动特性的瞬态脉动数学模型,建立了用来计算伴随气泡和气穴的液压管路瞬态下气泡体积和气穴体积的数学模型。构造了基于最小二乘法的适应度模型,探讨了遗传操作方式及算法终止准则,采用了算术交叉同线性逼近相结合的改进算术交叉算子进行交叉操作,给出了模型参数寻优的算法流程。实现了对低压液压管路压力瞬态脉动数学模型的参数识别,得到了参数优化后的低压液压管路压力瞬态脉动模型。仿真结果与实验数据的比较表明在低压液压管路瞬态模型中,用改进遗传算法来识别模型中的未知参数的方法是可行的、有效的。

In order to predict pressure transients accompanied with cavitation and gas bubbles inside low pressure hydraulic pipeline, a new method using improved genetic algorithms （GAs） in parameter identification of pressure transient models is presented in this paper. Pressure transient mathematical models are given to describe the flow behavior in the pipeline. The dynamic models of cavitation and gas bubbles are built to calculate the cavitation volume and gas bubbles volume during the transients. A fitness model based on the least-square errors for parameter optimization is built, the way of genetic operation and algorithm termination rule are discussed. Integrating arithmetic intercross with linear approach, an ameliorated arithmetic intercross operator is adopted to perform the cross operation. The GAs flow with parameter optimization of low pressure hydraulic pipeline pressure transient models is given. Making use of improved GAs, parameter identification of pressure transient mathematical models is carried out, simulation model with optimal parameters is obtained using improved GAs. Comparison between predicted results and experimental data shows that GAs is capable of estimating unknown parameters in hydraulic low pressure pipeline transient model.