Constructing rule-bases for multivariable fuzzy control by self-learning Part 1. System structure and learning algorithms

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Abstract
A novel method is presented capable of constructing rule-bases via self-learning for the use of fuzzy controllers. The controlled process is assumed to be a multivariable system with strong interaction within variables and with pure time delays in control. The objective of the proposed system is to build, in the case of two-input two-output systems, two separated and decoupled rule-bases for two control loops with some design requirements. The paper is divided into two parts. In the first part, a system structure comprising four functional modules is proposed. Then, the paper focuses on the issues concerning the learning algorithm. By introducing learning errors, three learning update laws are suggested. Furthermore, the convergence property of the learning algorithms is analysed in the sense of some defined norms. In addition, some comments and remarks about the proposed algorithms are given. The second part of the paper deals mainly with the issues of the methodology for rule-base formation and the application to the problem of multivariable control of blood pressure.

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