Regular and fuzzy extended Kalman filtering for a two-link flexible robot manipulator

Abstract

A Linear quadratic Gaussian (LQG) control scheme with either a regular extended Kalman filter (EKF) or a fuzzy logic adaptive EKF (FLAEKF) state estimator implemented in the control loop was used to control a two-link flexible robot manipulator tracking a square trajectory 12.6m x 12.6m. Simulations were performed to ascertain the extent of divergence that may develop in a regular EKF and how effectively a FLAEKF could reduce or eliminate this divergence. Trajectories were obtained using LQG with a regular EKF resulting in divergence according to the intensity of non-white process and measurement noise disturbances. They were compared to more precise trajectories obtained using LQG with a FLAEKF. The results confirm the ability of a FLAEKF state estimator to effectively correct divergence that would otherwise occur with a regular EKF state estimator and to maintain robot-tracking precision albeit at a greater computational time burden.