SVR Learning-Based Spatiotemporal Fuzzy Logic Controller for Nonlinear Spatially Distributed Dynamic Systems

• Published in: IEEE transaction on neural networks and learning systems 2013-10, Vol.24 (10), p.1635-1647
• Main Authors: ZHANG, Xian-Xia, YE JIANG, LI, Han-Xiong, LI, Shao-Yuan
• Format: Article
• Language: English
• Subjects: Applied sciences; Computer science; control theory; systems; Data processing. List processing. Character string processing; Exact sciences and technology; Fuzzy rule extraction; Logical, boolean and switching functions; Memory organisation. Data processing; Software; spatial fuzzy basis function; spatiotemporal fuzzy logic controller; Studies; SVR learning; Theoretical computing
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2013.2258356

A data-driven 3-D fuzzy-logic controller (3-D FLC) design methodology based on support vector regression (SVR) learning is developed for nonlinear spatially distributed dynamic systems. Initially, the spatial information expression and processing as well as the fuzzy linguistic expression and rule inference of a 3-D FLC are integrated into spatial fuzzy basis functions (SFBFs), and then the 3-D FLC can be depicted by a three-layer network structure. By relating SFBFs of the 3-D FLC directly to spatial kernel functions of an SVR, an equivalence relationship of the 3-D FLC and the SVR is established, which means that the 3-D FLC can be designed with the help of the SVR learning. Subsequently, for an easy implementation, a systematic SVR learning-based 3-D FLC design scheme is formulated. In addition, the universal approximation capability of the proposed 3-D FLC is presented. Finally, the control of a nonlinear catalytic packed-bed reactor is considered as an application to demonstrate the effectiveness of the proposed 3-D FLC.