LONG-RANGE PREDICTIVE ADAPTIVE FUZZY RELATIONAL CONTROL

A class of lone-range predictive adaptive fuzzy relational controllers is presented. The plant behavior is described over an extended time h orizon by a fuzzy relational model which is identified based on input- output closed-loop observations of the plant variables, In this class of adaptive controllers the control law attempts to minimize a quadrat ic cost over an extended control horizon. When used with linear models , this approach has revealed a significant potential for overcoming th e limitations of one-step ahead schemes, such as the stabilization of non-minimum phase plants. Here, a uniform framework is adopted for imp lementing both the fuzzy model and the fuzzy controller, namely distri buted fuzzy relational structures gaining from their massive parallel processing features and from the learning capabilities typical of the connectivist approaches. Issues such as maintenance during the adaptat ion process of the meaning of linguistic terms used at both fuzzy syst ems interfaces are addressed, namely by introducing a new design metho dology for on-line fuzzy systems interface adaptation. The examples pr esented reinforce the claim of the usefulness of this new approach.