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Top1. Introduction
Nowadays, FLC are widely used due to their facilities, precision and stability. This paper presents an adaptive controller that overpasses the traditional criterions of controllers (effectiveness, precision and robustness ext.). The proposed adaptive algorithm is able to reduce the energy consumption during the control process compared with other conventional controllers. In the early 1950s began the first attempts of the elaboration of adaptive control systems. a formal definition of adaptive control has been center of interest for many Control engineers and for example the vocabulary including the terms “Self-Organising Control (SOC) system (Procyk & Mamdani, 1979)”, “parameter adaptive SOC”, “performance adaptive SOC2, and “learning control system” (Åstrøm & Wittenmark, 1995) was proposed by a committee under the Institute of Electrical and Electronics Engineers (IEEE) in 1973. The acceptation of these terms were very limited, however, but a little later E.H. Mamdani and students invents a self organizing fuzzy controller (SOC) (Procyk & Mamdani, 1979) who explains the meaning of these terms. Their idea is to try to identify and replace the responsible rule of the poor control performance with a convenient rule. In the majority of SOC approaches, this reliance is expressed using only the monotonicity sign (the Jacobian matrix is not always available). SOCs have showed great effectiveness in “simple” applications since only the rule consequents are to be tuned. In highly nonlinear plants, more sophisticated fuzzy controller have to be constructed, that’s because, SOCs are only capable of a coarse tuning of the controller parameters (Lin & Mon, 2003; Pourdehi, Karimipour, Noroozi, & Shabaninia, 2010; Park & Park, 2004; Cho, Park, & Park, 2002; Pomares, Rojas, González, Rojas, Damas, & Fernández, 2002). For example in Lin and Mon (2003) a nonlinear system is controlled by the suggestion of the hybrid adaptive fuzzy controller that adopts a state feedback control policy. The principal benefit of this approach is that the robust gain of the sliding-mode controller is tuned by the use of the adaptive fuzzy control law to coping with the uncertainties and modeling error of the nonlinear robotic system. In Cho, Park, and Park (2002) the parameters of fuzzy feedback linearization controller are adjusted indirectly from the estimates of plant parameters.
The presented methodology offers asymptotic tracking of a reference signal for the systems with uncertain or slowly time-varying parameters. The major disadvantage of the latter approach is that this parallel interaction is not always optimum and that a certain quantity of data should be extracted from the plant in real time in order to achieve global learning. The main focus of this paper is testing the performances of an Adaptive and Self-Structured FLC via simulation. Noting that the proposed algorithm is a real life controller and it can adapt and tune their internal parameters according to the state of the controlled plant. The proposed methodology should be robust against modification of the parameters of the plant (break-downs). Our fuzzy controller can start with a set of empty rules, it is important to note, that no initial knowledge about the control policy is required. The proposed methodology has been used for a simulated temperature control process and the result was compared with a conventional Fuzzy Control algorithms, the result obtained explains the effectiveness and the robustness of our approach.