(A) study on the control of nonlinear systems using fuzzy learning method

Abstract

During the past several years, fuzzy control has emerged as one of the most active and fruitful areas for research in the applications of fuzzy set theory, especially in the realm of industrial processes, which do not lend themselves to control by conventional methods because of a lack of quantitative data regarding the input-output relations. Fuzzy control is based on a fuzzy logic system which is much closer in spirit to human thinking and natural language than traditional logical systems. The fuzzy logic controller provides an algorithm which can convert a linguistic control strategy based on expert knowledge into an automatic control strategy. However, in the case of complex systems, it is nearly impossible to obtain the fuzzy control rules only by humans`` intuition and experience. To design more intelligent control systems, it is necessary to make use of humans`` adaptation and learning ability such as human works can make use of their experience. This dissertation proposes a new fuzzy learning method to organize the control rules by itself. In contrast with the traditional method which uses a predetermined perfermance, the learning algorithm introduces a reference model to generate a desired output and minimizes a performance index based on the error between the reference and plant output. When the performance index function is composed of the quantities of the error, error-rate, and learning delay, the control rules can be easily obtained by changing the quantities. The cost function is minimized by a gradient method and the control input is also updated. Especially in a tracking problem of a nonlinear plant, by constructing the rule matrix about the various operating points, we can obtain powerful control rules which are applicable to an application phase as well as a learning phase of the plant. When only the consequent part of control rules is updated, the finer quantization level to acquire higher accuracy increases the number of fuzzy control rule...