Modeling the Static Friction in a Robot Joint by Genetically Optimized BP Neural Network

This paper aims to present a method for improving the modeling precision of static friction. To some extent, as the traditional static friction models available can’t be unified to characterize all the friction situations, a back propagation neural network (BPNN) was proposed to weaken the requirements of traditional static friction models. In details, relative speed of interacting surfaces and joint load are typically considered as the inputs of BPNN, whose output is the predicted static friction. Furthermore, to speed up the convergence and improve the global generalization capability of BPNN, we use genetic algorithm (GA) to optimize the initial values of weights and thresholds. All the training samples follow with reciprocating constant-speed experiments of friction under the changes of joint speed and load. Three comparative experiments indicate that using GA to optimize the initial values of weights and thresholds benefit to improve the convergence rate of network and prediction accuracy, and comparing with the traditional model of static friction, the BPNN model has a higher prediction precision and excellent generalization capability.