Mechanical arm control method based on RBF neural network

Abstract

The invention discloses a mechanical arm control method based on an RBF neural network. The method includes the steps of firstly, providing a cognition learning model mechanism of a mechanical arm; secondly, putting forward a behavior cognition model based on cerebellum-basal ganglion and a hybrid learning algorithm; thirdly, establishing a mathematical model for the mechanical arm to autonomouslylearn through the artificial neural network and a reinforcement learning method; fourthly, establishing a mechanical arm imitation experimental model in a Matlab; fifthly, verifying the mechanical arm control method based on the RBF neural network. The method has the advantages that the method is not only suitable for the mechanical arm, but also can be applied to other mechanical fields; the method can be applied to other control fields; the method is more suitable for application, and the workloads of programmers can be greatly reduced; the mechanical arm with the autonomous learning capacity has higher competitiveness in future.

Description

technical field [0001] The invention relates to a method for controlling a manipulator, in particular to a method for controlling a manipulator based on an RBF neural network. Background technique [0002] At present, the basis for the development of robots is intelligence. In the robot control system, the most critical thing is the learning mechanism and ability. Simulating the learning mechanism of intelligent agents, so that robots can automatically acquire new knowledge and skills through continuous training and learning like living organisms, and realize self-improvement, is a hot issue in the field of robot control. [0003] In actual engineering, the payload of the manipulator will change, and many parameters cannot be accurately predicted during the movement. The adaptive control method of the RBF network has the advantage of not requiring prior knowledge of unknown parameters, such as the quality of the load. , the position of the terminal of the manipulator and th...

AI Technical Summary

Problems solved by technology

[0003] In actual engineering, the payload of the manipulator will change, and many parameters cannot be accurately predicted during the movement. The adaptive control method of the RBF network has the advantage of not requiring prior knowledge of unknown parameters, such as the quality of the load. , the position of the terminal of the manipulator and the force on the object acting on the terminal, so there is no need to train the neural network offline

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