Generalized predictive control of a vision-based tracking system using kalman filtering technique

By lack of measurements of the tool position in space, the control loop of a robot does not close around the end effector. Computer vision provides a means for closed-loop control of the robot tool position but also introduces significant time delay and measurement noise. A state estimator and a dynamic visual controller are then necessary for fast response and desired performance. The Generalized Predictive Control (GPC) algorithm is considered in this paper for a vision-guided robot tracking system with the help of the Kalman filter and robust control has been achieved.