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In this paper, an optimization of fuzzy logic controller (FLC) based maximum power point tracking (MPPT) using genetic algorithm (GA) is performed. The optimization process is performed by tuning the FLC's data base (DB) represented by parameters of membership functions (MFs) used. The tuning process is based on an objective function that is defined in terms of the statistic quantity named integral...
In this study, we proposed a controller design method of a two wheeled self-balancing vehicle (TWSBV) based on a fuzzy T-S model (T-S Fuzzy) associated with a genetic algorithm (GA). To achieve the stable controller of TWSBV, we used GA to determine the proper state feedback gains and used the T-S Fuzzy formed by the heuristic experiment with two fuzzy membership functions: vehicle body angle and...
Fuzzy system is well known system for its capabilities by solving various kinds of control problems. In this article, we proposed a method for optimizing the fuzzy logic controller through genetic algorithms and neural networks. The optimization method is composed of (1) the neural network with clustering is designed to learn an initial rule base, if no prior knowledge about the system is available...
Multi-objective designs are genuine models for intricate combinatorial optimization problems. This paper presents a fast elitist non-dominated sorting multi objective genetic algorithm to develop smartly tuned fuzzy logic controllers with a finer trade-off between interpretability and exactitude in linguistic fuzzy modeling problems. The multi-objective genetic algorithm produces a group of non-dominated...
Currently, there is an increasing interest to genetic fuzzy systems with its learning and optimizing capabilities. The performance of a genetic fuzzy system is most depended on the ability of finding the best optimized rule-set and the precision of fuzzy variable definition. In this paper, a new method of optimization learning and modifying for genetic fuzzy system and the arithmetic is introduced,...
This paper presents the idea of using the Switched Reluctance Motor (SRM) as an alternative to previously used drives, in wide good and other industrial applications. In order to show the advantage of the SRM, the speed control of a switched reluctance motor (SRM) is designed by blending two artificial intelligence techniques, genetic algorithms and fuzzy PI control. Here the Genetic Algorithm (GA)...
A new fuzzy method for the motion control of underactuated robots is proposed in this paper. The control objective is to move the end-effector from a given position to a target point. A new fuzzy controller for the motion control of underactuated robots is present. The best fuzzy control rules and optimal membership functions are automatically generated off-line by the global optimization of genetic...
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