An Ab Initio Fuzzy Dynamical System Theory: Controllability and Observability
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Fuzzy set is a generalization of the classical set. A classical set is distinguished from another by a sharp boundary at some threshold value and therefore, they are also known as crisp set. In fuzzy theory, sharp boundary and crisp set are replaced by partial truth and fuzzy sets. The idea of partial truth facilitates information description especially those communicated through natural language whose transition between descriptive terms are not abrupt discontinuities. Instead, the transition is a smooth change over a range corresponding to the degree of fulfillment each intermediate elements has according to the operating definition of the concept. The shape of a fuzzy set is defined by its membership function. This, by far, has been the common extent of concern regarding the membership function. Different applications may use the membership function to describe different variables such as speed, position, temperature, dirtiness, traffic conditions etc. But the underlying application of fuzzy sets remains the same: to describe information whose membership function, created in an initial setting, preserve the same size and shape throughout its entire application. In other word, fuzzy sets are utilized as if they are static entities. Nothing has been said about how an initially defined membership function can develop over time with respect to a system. The current research proposes a new framework that concerns the evolution of membership functions. We introduce the concept of membership function propagation as a dynamic description of uncertainty. Given a dynamical system with a set of uncertain initial states which can be represented by membership functions, the membership function propagation describes how these membership functions evolve over time with respect to the system. The evolution produces a set of propagated membership functions that have different size and shape from their predecessors. They represent the uncertainty associated with the states of the system at a given time. This new description also confers new definitions for two important concepts in control theory, namely controllability and observability. These two concepts are re-introduced in a fuzzy sense, based on the concept of membership function propagation. By assuming convexity of the fuzzy set, criterions for controllability and observability can be derived. These criterions are illustrated by MATLAB and SIMULINK simulations of an inverted pendulum and a 2 degree of freedom mechanical manipulator.