Research on the dynamic compensation based on genetic wavelet neural networks for the robot wrist force sensor

Abstract

A kind of new dynamic compensation method is presented based on improved genetic algorithm (IGA) and wavelet neural networks (WNN) and the principle of algorithm is introduced for a new type robot wrist force sensor. In this method, the dynamic compensation model of the wrist force sensor can be set up according to measurement data of the dynamic calibration, where the structure and parameters of wavelet neural networks of the dynamic compensation model are optimized by genetic algorithm. The results show that the proposed new dynamic compensation method can overcome the shortcomings of BP algorithm, such as easy convergence to the local minimum points, and the network complexity, the convergence and the generalization ability are good compromised and the training speed and precision of compensation model are increased.