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In the traditional control theory, an appropriate controller is designed based on a mathematical model of the plant under the assumption that the model provides a complete and accurate characterization of the plant. However, in some practical problems, the mathematical models of plants are difficult or time-consuming to be obtained because the plants are inherently nonlinear and/or exhibit uncertainty. Thus, new methods are proposed to process these caracteristics [1]. In recent years, increased efforts have been centered on developing intelligent control systems that can perform effectively in real-time. These include the development of non-analytical methods of Artificial Intelligence (AI) such as neural networks, fuzzy logic and genetic algorithms [1] but their combinations are also introduced such as Neuro-Fuzzy and Genetic-Fuzzy techniques [2], [3]. Fuzzy logic is a mathematical approach which has the ability to express the ambiguity of human thinking and translate expert knowledge into computable numerical data. It has been shown that fuzzy logic based modeling and control could serve as a powerful methodology for dealing with imprecision and non-linearity efficiently [4]. Also, for real-time applications, its relatively low computational complexity makes it a good candidate.Therefore, fuzzy logic control has emerged as one of the most successful nonlinear control techniques. Fuzzy Logic Controllers (FLC) are based on if – then rules integrating the valuable experiences of human. These rules use linguistic terms to describe systems. The mechanism of a FLC is that the uncertainty is represented by fuzzy sets and an action is generated co-operatively by several rules that are triggered to some degree, and produce smooth and robust control outputs. Recently, many authors proved that it is possible to reproduce the operation of any standard continuous controller using fuzzy controller [5] [8]. Fuzzy logic controllers has shown good performances on the controlling of the complex, illdefined and uncertain systems [9] and are being used siccessfully in many application areas such as mobile robots, subway system, nuclear reactor control and automobile transmission control, etc . During the building of the FLC, the important tasks are the structure identification and parameters tuning [10]. The structure identification of the FLC includes the input-output variables of a controller, the rule base, the determination of the number of rules, the O pe n A cc es s D at ab as e w w w .ite ch on lin e. co m

An Approach to Tune PID Fuzzy Logic Controllers Based on Reinforcement Learning