Dedicated neural network design for friction compensation in robot drives

In the paper we demonstrate a neural network-based controller design and prototyping following the mechatronic approach. A unified treatment of all system components (mechanical, electrical and computational) is made possible thanks to the integrated software-hardware platform. The neural network in the presented approach is used to provide a linearising feedback loop for friction compensation in a robot drive. The efficiency of the experimental friction identification is improved thanks to dedicated network architecture. The proposed solution is implemented in DSP hardware and the simulation results are verified through laboratory experiments.