2006
DOI: 10.1016/j.fss.2006.03.006
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Adaptive fuzzy controller: Application to the control of the temperature of a dynamic room in real time

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Cited by 46 publications
(21 citation statements)
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“…However, we are aware that the plant must be under control, if that, its derivative will have a definite constant sign. Therefore, the use of the information of how the monotonicity of the plant change regarding to the control input can indicate us in which direction we have to move the rule consequents to get the required improvement [5,10]. In the proposed methodology, the adaptation mechanism evaluates the plant's state periodically and suggests a suitable correction (either a reward or a penalty) for the rules responsible of reaching such state.…”
Section: ͵ǥ͵ǥͳǥ ǧǣmentioning
confidence: 99%
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“…However, we are aware that the plant must be under control, if that, its derivative will have a definite constant sign. Therefore, the use of the information of how the monotonicity of the plant change regarding to the control input can indicate us in which direction we have to move the rule consequents to get the required improvement [5,10]. In the proposed methodology, the adaptation mechanism evaluates the plant's state periodically and suggests a suitable correction (either a reward or a penalty) for the rules responsible of reaching such state.…”
Section: ͵ǥ͵ǥͳǥ ǧǣmentioning
confidence: 99%
“…Based on the knowledge about the system to be controlled, the fuzzy controller uses a fix predefined structure (i.e., number of inputs, number of MFs and their parameters, etc.). Fixing such structure may not be always easy, since, it requires a deep knowledge about the system to be controlled [5,19]. Therefore, in order to start the control of the plant using very limited information, we need to use advanced adaptive skills that exceed the aforementioned adaptive process, i.e., more internal parameters of the used controller need to be adapted and optimized.…”
Section: ͵ǥ͵ǥʹǥ ǧǣmentioning
confidence: 99%
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“…However, these controllers are not able to distinguish the operating region and make an appropriate control action with respect to operating conditions [4]. In order to overcome the problem, different types of adaptive fuzzy logic controller such as self-tuning and self-organized controllers have been developed [5,6]. Using a nonlinear predictive controller designed on Takagi-Sugeno fuzzy model Is another method proposed for controlling the steam temperature in the literature [7,8].…”
Section: Introductionmentioning
confidence: 99%