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Abstract

The control scheme presented utilizes both feed-forward and feedback controller elements. The former of the two elements comprises a neural network with both an identification and control role, and the latter is a fuzzy neural algorithm which is introduced to provide additional control enhancement. The feed-forward controller provides only coarse control, whereas the feedback controller can generate on-line conditional proposition rules automatically to improve the overall control action. To evaluate the performance of the controller, a simulated robot manipulator study was undertaken and the results show how well the proposed controller can minimize the error between an actual and desired end-effector trajectory.

Keywords

neural networks fuzzy logic adaptive control

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