Force Estimation Based Compliance Control of Harmonically Driven Manipulators

Abstract

The problem of estimating external forces exerted on a robotic manipulator with harmonic drive gearing without a force-torque sensor is considered. Manipulator dynamics together with motor current feedback is used to estimate external joint torques, which are transformed into estimated external end effector forces using knowledge of the manipulator's kinematics. Adaptive control is used to tune the parameters of the robot's modeled dynamics, while adaptive radial basis function (RBF) "neural" networks are used to learn the friction model. Compliance control is implemented on a two degree of freedom manipulator based on the force estimates. Results are compared to compliance control using a six-axis force-torque sensor mounted on the manipulator