Multi-level Computing With Words Model to Autonomous Systems Control

Abstract

An autonomous system which must react to unforeseen situations is considered. The control task of such system is characterized by the processing of data from large number of sensors, uncertainty and dynamics. The traditional automation approaches cannot be used to create control system that satisfies these conditions. Fuzzy logic solves this problem due to ability to data generalize and take into account uncertainty, but only for simple applications presented by a small amount of data from sensors. L. Zadeh Computing with Words (CWW) approach overcome the problem of large dimensionality if the situation description is presented by a small number of words, but a high level of abstraction. However, the problem remains how numerical data from sensors to convert into the words representing the meaning of these data at a high level of abstraction. Three-phases CWW model is proposed to solve this problem. At the first phase, granular computing engine reveal the meaning of data from sensors and represents its by words of zero-level abstraction. Then abstracting with words engine maps its words into words of higher abstraction level representing the meaning of complex dynamic situations. And in the end, CWW engine obtain control decisions using as fuzzy inference inputs the meaning of the words of high levels abstraction. Such word-based processing of data from sensors is based on the proposed fuzzy models of the external and internal meanings of the word. An example of signal switching control of a smart traffic light is given.