Adaptive Control of Manipulators Using Uncalibrated Joint-Torque Sensing

The application of joint-torque sensory feedback (JTF) in robot control has been proposed in the past as a substitute for the computed torque method. A controller based on JTF does not require computation of link dynamics. However, the traditional JTF assumes precise measurement of joint torque. This paper presents an adaptive JTF control algorithm that does not rely on this assumption. First, the robot dynamics with JTF is presented in a standard form, where the inertia matrix appears symmetric and positive definite. Subsequently, properties of the dynamics is inves tigated and a condition on the number of parallel joint axes for dynamic decoupling is derived. This can lead to further simplification of control structure for a class of robots. Secondly, an adaptive control law is developed incorporating uncalibrated joint torque signals, i.e., the gains and offsets of multiple sensors are unknown, into the control system. No dynamic model of a robot link is required, and all physical parameters of the joints including inertia of the rotors, link twist angles, and friction parameters are assumed unknown to the controller. Stability analysis together with a condition for bounded control input are presented. The control algorithm is experimentally applied to a robotic arm and experimental results illustrate high tracking performance, albeit neither was the torque sensor calibrated nor the parameters were known.