A PWA model identification method based on optimal operating region partition with the output-error minimization for nonlinear systems. (April 2020)

• Record Type: Journal Article
• Authors: Song, Chunyue; Wang, Jiaorao; Ma, Xinda; Zhao, Jun
• Is Part Of: Journal of process control. Volume 88(2020)
• Page Start: 1
• Page End: 9
• Publication Date: 2020-04
• Subjects: Nonlinear system identification -- PWA -- Operating region partition -- Clustering -- PSO; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2020.01.011
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: A PWA model identification method based on optimal operating region partition with the output-error minimization for nonlinear systems is initially addressed. The output-error minimization index provides more precise identification results. Softmax function is adopted for determining the initial boundaries and it can partition operating space completely. The optimal parameters and operating regions are determined simultaneously by Particle Swarm Optimization (PSO) in order to avoid the mismatch between them. Abstract: When piecewise affine (PWA) model-based control methods are applied to nonlinear systems, the first question is how to get sub-models and corresponding operating regions. Motivated by the fact that the operating region of each sub-model is an important component of a PWA model and the parameters of a sub-model are strongly coupled with the operating region, a new PWA model identification method based on optimal operating region partition with the output-error minimization for nonlinear systems is initiated. Firstly, construct local data sets from input-output data and get local models by using the least square (LS) method. Secondly, cluster local models according to the feature vectors and identify the parameter vectors of sub-models by weighted least squares (WLS) method. Thirdly, get the initial operating region partition by using a normalized exponential function, which is to partition the operating space completely. Finally, simultaneously determine the optimal parameter vectors of sub-models and the optimal operating region partition underlying the output-error minimization, which is executed by particle swarm optimization (PSO) algorithm. Simulation results demonstrate that the proposed method can improve model accuracy compared with two existing methods.