CONTROL SYSTEMS

Control Systems

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Control Systems

Introduction

Fuzzy logic is a mathematical system that analyzes analog input values in terms of fuzzy variables that takes continuous values between 0 and 1, in contrast to classical or digital logic, which operates on discrete values of either 0 or 1.Fuzzy logic is an approach to reasoning where the rules of inference are approximate rather than exact. It's useful for manipulating information that is incomplete, imprecise, or unreliable. Fuzzy systems, which employ fuzzy logic in their control strategy or operation, have been widely used in production and research, e.g. in control process of steel producing, positioning of subway trains in terminals, and robot motion control. Fuzzy logic inference makes it possible for machines to measure and calculate as humans do based on experience and estimation. Fuzzy rules may be easier to derive and faster to use than explicit formulae (Von Altrock, 1995).

Recent research and practice indicate that fuzzy logic is quite successful in solving problems to which traditional methods are powerless. However, how to construct a fuzzy system remains a problem. In traditional methods, the amount of work needed to extract and adjust the membership functions and rules of a fuzzy system expands exponentially with an increase in the number of input variables. The scale of the neural network used to derive the fuzzy rules becomes too large to be utilized. Its local minimums will also dramatically increase due to its employing of Back Propagation (BP) algorithm as method of parameter adjustment. The algorithm is prone to converge to some local minimums, therefore, the resulting parameters in the network is not optimized as desired. Furthermore, there is not an integrated and proven mechanism to detect the convergence to local minimums in BP algorithm (Arabacioglu, 2010). The quality of the result depends ...