A Clustering and SVM Regression Learning‐Based Spatiotemporal Fuzzy Logic Controller with Interpretable Structure for Spatially Distributed Systems

Abstract

Many industrial processes and physical systems are spatially distributed systems. Recently, a novel 3‐D FLC was developed for such systems. The previous study on the 3‐D FLC was concentrated on an expert knowledge‐based approach. However, in most of situations, we may lack the expert knowledge, while input‐output data sets hidden with effective control laws are usually available. Under such circumstance, a data‐driven approach could be a very effective way to design the 3‐D FLC. In this study, we aim at developing a new 3‐D FLC design methodology based on clustering and support vector machine (SVM) regression. The design consists of three parts: initial rule generation, rule‐base simplification, and parameter learning. Firstly, the initial rules are extracted by a nearest neighborhood clustering algorithm with Frobenius norm as a distance. Secondly, the initial rule‐base is simplified by merging similar 3‐D fuzzy sets and similar 3‐D fuzzy rules based on similarity measure technique. Thirdly, the consequent parameters are learned by a linear SVM regression algorithm. Additionally, the universal approximation capability of the proposed 3‐D fuzzy system is discussed. Finally, the control of a catalytic packed‐bed reactor is taken as an application to demonstrate the effectiveness of the proposed 3‐D FLC design.