PSO-OSVRMPC在焦炉冷鼓系统中的应用  

Application of PSO-OSVRMPC in Coke Oven Cold Drum System

作　　者：沈聪 唐伟 张世峰 SHEN Cong;TANG Wei;ZHANG Shifeng(School of Electrical&Information Engineering,Anhui University of Technology,Maanshan 243032,China)

机构地区：[1]安徽工业大学电气与信息工程学院,安徽马鞍山243032

出　　处：《安徽工业大学学报（自然科学版）》2021年第4期407-413,共7页Journal of Anhui University of Technology（Natural Science）

基　　金：安徽省高校自然科学基金项目(KJ2018A0061)。

摘　　要：针对传统焦炉冷鼓系统控制复杂性高、非线性强、抗干扰能力弱以及无法使用数学模型精准拟合等问题,提出一种基于粒子群优化(PSO)算法的非线性在线支持向量回归(OSVR)的模型预测控制策略。利用OSVR的辨识方式建立预测控制模型,根据该辨识策略的在线学习能力进行模型的在线校正;通过PSO算法对系统的目标函数进行滚动优化,完成对系统的预测控制,且利用仿真实验对该控制策略进行验证。结果表明:该策略能够缩短焦炉冷鼓控制系统的响应时间,提高系统的运行效率;粒子群的优化策略具备较强的自我学习能力,提高了系统的抗干扰能力,增强了系统的鲁棒性。Aiming at the problems of high control complexity,strong nonlinearity,weak anti-interference ability and the inability to use mathematical models to accurately fit the traditional coke oven cold drum system,the strategy of model predictive control(MPC)based on particle swarm optimization(PSO)algorithm and nonlinear online support vector regression(OSVR)was proposed.The predictive controlmodel was established with the identification method of OSVR,and the model was corrected online according to the online learning ability of the identification strategy.The objective function of the system was optimized by PSO algorithm to complete the predictive control of the system,and the control strategy was verified by simulation experiments.The results show that this strategy can shorten the response time of coke oven cold drum control system and improve the operation efficiency of the system.The PSO strategy has strong self-learning ability,can improve the anti-interference ability of the system and enhance the robustness of the system.

关 键 词：粒子群优化(PSO)算法 在线支持向量回归(OSVR) 模型预测控制(MPC) 焦炉冷鼓系统 

分 类 号：TP26[自动化与计算机技术—检测技术与自动化装置]