Neuro-fuzzy friction compensation to robotic actuators

The main objective of this paper is to propose a new friction compensation mechanism applied to robotic actuators. Friction is a phenomenon that changes with time and with actuator's operational conditions. To deal with these parameters variations, it is proposed a neuro-fuzzy algorithm for friction identification and compensation. A neural network (NN) was trained off line. The NN output (compensation friction torque) is multiplied by a gain, obtained with a fuzzy inference algorithm, to deal with friction parameters variations and to adjust the compensation torque. Experimental results showed good performance, indicating that the actuator becomes approximately linear.