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This paper describes an evolutionary genetic algorithm approach for the optimization of a fuzzy reactive controller applied to an autonomous 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.
This paper describes an evolutionary algorithm approach for the optimization of a type-2 fuzzy reactive controller applied to mobile robot navigation. We compare the type-2 fuzzy reactive controller against a type-1, to verify the advantage of type-2 fuzzy logic in control. In this kind of applications.
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.
In this paper, we apply an optimization method inspired on the chemical reactions to find the gain constants involved in the tracking controller for the dynamic model of an unicycle mobile robot. This tracking controller integrates a kinematic and a torque controller based on fuzzy logic theory. The search of these constants was made previously using genetic algorithms. The objective of this paper...
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...
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