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This paper proposes a gain scheduling adaptive control scheme based on fuzzy systems, neural networks and genetic algorithms: an optimal fuzzy PI controller is developed, by a genetic algorithm, according to some design specifications, and a neural network is designed to learn and tune online the fuzzy controller parameters at different operating points from ones used in the learning process. Simulation...
This paper proposes an original iterative feedback tuning (IFT) method employing genetic algorithms to develop a class of fuzzy control systems. The approach is based on using the linear case results from the original IFT method and on replacing the parameter update law by genetic algorithms. Then, these results are transferred to the fuzzy case in terms of the modal equivalence principle resulting...
This paper presents an intelligent gain scheduling adaptive control approach for nonlinear plants. A fuzzy PI discrete controller is optimally designed by using a multiobjective genetic algorithm for simultaneously satisfying the following specifications: overshoot and settling time minimizations and output response smoothing. A neural gain scheduler is designed, by the backpropagation algorithm,...
Silent pole synchronies motor has a fast dynamic response. In this paper, the method of tracing flux of rotors turn has been used to control the drive. For finding optimum parameters and ordinary controllers coefficients we have used the genetic algorithm. The design of controllers according to fuzzy-logic and genetic algorithm and also the performance of synchronies drive in the case of applying...
In this paper, a gain-adjusted fuzzy PI/PD (GFPIPD) adaptive controller is proposed. The proposed controller first constructs fuzzy rules for fuzzy PD/PI controller with the fixed weighting. Then the fuzzy rules, which self-learning their parameters for a desired condition, are learned through the accumulated GA. Finally, fuzzy gain-adjusted mechanism is further learned through the accumulated GA...
We propose a fuzzy PI-PD controller that is tuned by using genetic algorithm (GA). The fuzzy PI-PD controller preserves the linear structure of the conventional one, but has self-tuned gains. The proportional, integral and derivative gains are nonlinear functions of their input signals having certain adaptive capability in set-point tracking performance. The proposed design is then optimized using...
The design methods of fuzzy logic controllers (FLCs) using the genetic/evolutionary algorithms (GEAs) are appearing as systematic methods. These methods easily provide an optimized design and form the framework for further progress. This paper describes the design of P-, PI-, PD-, PID-like FLCs based on theoretical fuzzy concepts and genetic-based optimization. In Pi-like FLC, the most important feature...
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