Stochastic Pole Placement Control of Robot Manipulators using an Extended Kalman Filter

Abstract

A stochastic system model is controlled using a pole placement perturbation technique. An extended Kalman filter provides the controller with state estimates. The identification and control scheme is tested for its ability to manipulate the simulated robot's end-effector along given trajectories, and under various adverse conditions. Comparisons are made with the responses of the system when the controller is given the true states and when, in addition, the noise terms are removed.