Design and optimization of a fuzzy-neural hybrid controller for an artificial muscle robotic arm using genetic algorithms

Humanoids are increasingly used in the service sectors around the world to work with, or assist humans. However current humanoid designs place limitations on direct engagement with the human in terms of safety and usability. In this paper, we present an approach for the control of hybrid, high-speed and safe human-robot interaction systems with highly non-linear dynamic behavior. The proposed approach comprises the three soft computing techniques, namely back propagation neural network, fuzzy and genetic algorithms. This open-loop controller was applied to a Bridgestone Hybrid Robot Arm (BHRA). BHRA has three electric motors and four artificial muscles, arranged in an agonist/antagonist, and opposing pair configuration, that drive the five-degrees of freedom of the robot arm. The behaviors of the artificial muscles are observed under the effects of the links driven by the electric motors and it is shown that the proposed biologically-plausible controller could produce more accurate trajectories at higher speeds when compared to conventional PID and stand alone or combined versions of Neural Network and Fuzzy controllers.