转向系统背压加载的广义预测控制  

Generalized Predictive Control of Steering System Back Pressure

作　　者：江小霞[1] 叶挺[1] 林少芬[1] 吴兵[1] 

机构地区：[1]集美大学轮机工程学院,厦门361021

出　　处：《北京工业大学学报》2012年第12期1766-1771,1874,共7页Journal of Beijing University of Technology

基　　金：福建省科技重大专题(2008HZ0002-1);财政部科技部科技人员服务企业行动项目(财教[2009]365)

摘　　要：针对转向系统背压加载的时变、非线性、多变量耦合等过程特性,研究了一种基于最小二乘支持向量机(least square support vector machine,LS-SVM)的广义预测控制算法.采用LS-SVM辨识方法对系统进行建模,并用粒子群算法对LS-SVM的参数进行寻优,为控制器的设计奠定基础;对于时变的特点,采用基于在线LS-SVM的广义预测控制混合算法,实时修改模型参数.转向系统背压加载的控制实验结果表明,基于LS-SVM的广义预测控制混合算法是有效的,能准确地跟踪设定的加载压力,对扰动有较强的鲁棒性,具有实际工程应用价值.To deal with the time-variation, nonlinearity and multivariable couping characteristics in the process of steering system back pressure, the generalized predictive control strategy based on least square support vector machine （LS-SVM） was proposed. The LS-SVM model is established to identify the SSBP. Particle swarm optimization is proposed to get better value of normalizing parameter and kernel parameter. With time-variable inertia, an adaptive direct generalized predictive control method based on online LS-SVM is adopted, which revises the parameters for the model in time. Results of the actual application for the control of SSBP demonstrate that the proposed adaptive direct generalized predictive control method based on online LS-SVM is effective, the performance of loading pressure tracking is good, and the robustness to the disturbance is strong. The method can be applied to some control fields like the process of SSBP.

关 键 词：转向系统 背压加载 最小二乘支持向量机(LS—SVM) 广义预测控制 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]