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This paper describes an evolutionary algorithm approach for the optimization of a fuzzy reactive controller applied to a mobile robot. The algorithm will optimize the Fuzzy Inference System evaluating the performance of each individual with a Neuro-Fuzzy fitness function that considers the robots covered distance, time used, battery life and the pattern of the trajectory.
A neuro-fuzzy learning algorithm is applied to design a Takagi-Sugeno type Fuzzy Logic Controller (T-S FLC) for a biped robot walking problem. The control design considers an output function imposed on the feedback and several TS-FLC models are determined each by ANFIS, which represent a piece-wise control inputs that together to perform a walking cycle. Two simulations of the closed-loop system for...
The paper proposed a neural solution to the direct torque vector control of three phase induction motor including real-time trained RNN velocity controller and a hysteresis flux and torque controllers, which permitted the speed up reaction to the variable load. The basic equations and elements of the direct field oriented torque control scheme are given. The control scheme is realized by one RNN learned...
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