粒子滤波在复杂工业过程中的应用

摘要

复杂工业过程往往具有不确定性、非线性、大滞后、强耦合等特点,难以建立在线控制模型.为了克服复杂工业过程中的非高斯、强非线性等因素对系统建模的影响,利用粒子滤波算法对非线性、非高斯系统进行全局优化的优势,对系统模型进行优化,使系统模型能够更加准确地反映系统的真实状态,提出一种基于粒子滤波的径向基函数(RBF)神经网络控制方法,并将其应用到联合制碱生产过程的研究中.联合制碱碳化过程是一个典型的复杂工业过程,具有过程复杂、难以建立在线控制模型等突出特点,以联合制碱碳化过程为对象进行仿真试验研究,并与原先应用过的模糊神经网络控制方法进行效果对比,仿真结果表明:引入粒子滤波算法后,对复杂工业过程的控制更加有效,系统的控制精度和系统响应速度明显提高,可为解决一类复杂系统的建模与优化控制研究提供一条有效的技术途径.%Complex industrial process has the characteristics of uncertainty, nonlinear, non-Gaussian, large delay and strong coupling, so it is difficult to build linear control model. Particle filter (PF) algorithm can be used in global optimization of nonlinear, non-Gaussian system, making the model reflect the real system state accurately. This paper proposed a partical filter based radial basis function (RBF) neural network method, and applies it to the study of synthetic ammonia decarbornization production process. The synthetic ammonia decarbornization process is a complex industrial production process, whose on-line control model is difficult to establish. Some simulation study with the synthetic ammonia decarbornization has shown that after using PF it has better performance than using only fuzzy neural network. The result also shows that the system is more effectively controlled after using PF algorithm. It provides an efficient way for the complex system modelling and optimization control research.