This paper proposes a fuzzy neural network controller for position control of industrial robot joint drives. It overcomes the shortcomings of traditional PID that it is difficult to achieve control effects on nonlinear and uncertain factors and that simple fuzzy control cannot completely eliminate steady-state errors. By optimizing the learning of fuzzy rules through neural networks, the position accuracy of the robot joint end is improved, and a better control effect is achieved.
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