Modeling Friction through the use of a Genetic Algorithm

Abstract

In the field of robotics, joint friction is an impediment that can have significant negative impact on robot performance by limiting positional accuracy, causing loss of energy and potential instability in applications such as force control and tele-operation. The purpose of this thesis is to discuss a robust friction compensation technique that has been developed for an electric motor, which is an integral part of many industrial robots. This friction compensation modeling technique combines the structure of a mathematical friction model with the optimization capabilities of a Genetic Algorithm. The best combined friction model and motor model (identified by using the Genetic Algorithm off-line) that fits experimentally collected data is used to implement a model-based friction compensator. The addition of the model-based friction compensator into a system results in that system behaving as if it were nearly linear. The friction compensated system also allows for the design of a LTI compensator in the feed-forward path to further improve the performance of the overall system. Extensive experimental testing on a harmonic drive confirms that this technique, causes the compensated system to behave almost like an ideal linear system without friction.