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The aim of this work is the combination of radial basis function networks (RBF) and fuzzy techniques to enhance the sliding mode controllers. In fact, three RBFs networks were used to estimate the model parameters and to respond to model variation and disturbances, a sequential training algorithm based on Kalman filter was implemented, and to eliminate the chattering effect, a fuzzy controller was...
A sliding mode control has been a great interest in the control engineering community, with many applications particularly in the robot manipulators control. This paper presents investigations into the development of Sliding Mode control approach based neural network, where the model parameters are used in the equivalent control law. A neural model of robot parameters is calculated. The first one,...
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