A Data-driven Control Scheme for Improving Tracking Control Performance of Robot Manipulators: Experimental Studies

This article presents a data-driven control application to robot manipulation for implementing the time-delayed control (TDC) algorithm. TDC scheme uses the previous information to cancel out all the dynamics except the inertial torque in robot manipulators. The accuracy of estimating the inertia matrix plays an important role in control performance as well as the stability of TDC. Necessary information for the time-delayed control is inertia and acceleration signals. Since selecting the constant inertia matrix is simple but concerned with the poor performance, better estimation is required. Based on the input and output data of a robot manipulator, necessary models are obtained by a recursive least squares (RLS) algorithm and those models are used for estimating acceleration signals by designing a state observer (SOB). Here the models of a robot arm are decoupled, linearized, and identified by RLS algorithm and the joint acceleration signals are identified by a state observer in on-line fashion. Combining RLS, SOB, and TDC yields RST scheme for a robot manipulator to improve the tracking control performance by providing solutions for TDC problems. Tracking control performances of a mobile manipulator by the RST scheme are empirically tested.