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.

Highlights

  • Many industrial processes and physical systems such as industrial chemical reactor 1, 2, semiconductor manufacturing 3, and thermal processing 4 are “distributed” in space

  • We aim at developing a new 3-D FLC design methodology based on clustering and support vector machine SVM regression

  • The initial rule-base is simplified by merging similar 3-D fuzzy sets and similar 3-D fuzzy rules based on similarity measure technique

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Summary

Introduction

Many industrial processes and physical systems such as industrial chemical reactor 1, 2 , semiconductor manufacturing 3 , and thermal processing 4 are “distributed” in space. The process model may not be obtained in many complex situations, and a model-free control method has to be used This leads to the recent development of the novel three-dimensional fuzzy-logic control 3-D FLC 5–8 , which has the inherent capability to process spatiotemporal dynamic systems. We often lack expert knowledge for control that is usually hidden in an input-output data set Under this circumstance, a data-driven design becomes a good choice for the 3-D FLC, that is, extraction of fuzzy rules from a spatiotemporal input-output data set. Traditional data-driven FLC design methods have been developed in the past three decades They are usually composed of three parts: rule generation, structure optimization, and parameter optimization. We aim at developing a new data-driven 3-D FLC design method based on clustering and SVM-regression learning.

Preliminaries
Linear SVM Regression
Clustering and SVM-Regression Learning-Based 3-D FLC Design
Rule Extraction and 3-D MF Construction
Structure Simplication
Similarity Measure-Based Structure Simplification
Parameter Learning
A Catalytic Packed-Bed Reactor
Spatiotemporal Data Collection
Design of a Clustering and SVM-Regression Learning-Based 3-D FLC
Control-Performance Validation
Findings
Conclusions
Full Text
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