【作者】 李娜；

【导师】 王印松；

【作者基本信息】 华北电力大学 ， 控制理论与控制工程， 2015， 硕士

【摘要】 随着现代工业的发展,生产过程越来越大型化、复杂化,工作点的变化范围大,使得建模越来越困难。数据驱动作为一种不需要对象模型,仅需要系统的I/O数据就可以完成控制器设计的技术,已成为研究的一个热点。本文以非线性系统作为研究对象,直接利用系统输入输出数据,在线的对PID控制器参数进行优化。首先,针对SISO的非线性系统,采用K-NN(K Nearest Neighbor)算法,从系统数据库中选出与当前时刻系统状态相匹配的数据信息,利用线性加权平均方法得到PID参数值。在此基础上,依据给出的优化准则函数,对所得到的参数值进行优化,实现对PID参数的在线调整,并给出了相关的数据库更新原则,通过对几个非线性系统的仿真试验,证实了优化算法的有效性。其次,将所提出的方法应用在了过热蒸汽温度控制系统中。分别应用Matlab/Signal Constraint工具箱及提出的优化算法对不同负荷下的系统控制器进行离线、在线优化,将所得到的两组优化值作为PID控制器参数,对其进行阶跃扰动实验,结果进一步证实了本文算法的有效性。然后,以75%负荷下的系统为例,对其进行了鲁棒性实验,仿真结果表明该优化算法具有一定的鲁棒性。最后,将提出的优化算法以Matlab/GUI图形用户界面方式实现。用户可以在不了解算法的情况下,进行操作,观察传统PID控制及基于数据驱动的PID优化控制下系统的输出曲线,及其相对应的参数优化值。更多还原

【Abstract】 With the development of modern industry, the process of production is more and more large and complex, the operating point varies widely, which makes it dificult to build models. Data-driven as a technique that doesn’t require the object model, and only requires I/O data to complete controller design has become a hot research. In this paper,we use the I/O data of the system directly to optimize the PID parameters online for nonlinear system. Firsly, we use K-NN(K-Nearest Neighor) algorithm to elect information from the system database that matches the current system status, and then using the linear weighted average method to obtain the PID parameters. On the basis, according to the optimization criterion function, it achieves the PID parameters adjustment online and gives out the relevant database update principle. Through several nonlinear system simulation experiments, it conformes the effectiveness of the optimization algorithm.Secondly, the proposed method is applied to the superheated steam temperature control system.Respectively using Matlab/Signal Constraint toolbox and the proposed method to optimize paramters offline or online for the superheated steam temperature system controller under different load. And then we get the optimized values obtained by the two methods as the PID controller parameters and conduct step disturbance test, the results cofirm the effectiveness of the proposed algorithm further. Then we take a robustness test for 75% load of the system,and the result shows that it has certain robustness. Finally, the optimization algorithm is proposed by Matlab/GUI graphical user interface.User can operate it even if he dosen’t understand the algorithm and observe the out curve of the system and the corresponding value of the parameter optimization in both the traditional PID control and PID optimization based on data-driven control.更多还原