Predictor-Based Motion Tracking Control for Cloud Robotic Systems with Delayed Measurements

This paper addresses the problem of motion prediction and tracking control for cloud robotic systems with time-varying delays in measurements. A novel method using an observer-based structure for position and velocity prediction is developed to estimate the real-time information of robot manipulator. The prediction error can converge to zero even if model uncertainties exist in the robot manipulator. Based on the predicted positions and velocities, some sufficient conditions are derived to design suitable tracking controllers such that semi-globally uniformly ultimately bounded tracking performance of the predictor–controller couple can be guaranteed. Finally, the effectiveness and robustness to model uncertainties of the proposed method are verified by a two degree-of-freedom (DOF) robot system.