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Improving the Accuracy of a Robot by Using Neural Networks (Neural Compensators and Nonlinear Dynamics)
The subject of this paper is a programmable con trol system for a robotic manipulator. Considering the complex nonlinear dynamics involved in practical applications of systems and robotic arms, the traditional control method is here replaced by the designed Elma and adaptive radial basis function neural network—thereby improving the system stability and response rate. Related controllers and compensators were developed and trained using MATLAB-related software. The training results of the twodoi:10.3390/robotics11040083 fatcat:7kmaum5vlndcpc65nfimpkmn6i