Fuzzy adaptive noise filtering and vibration control for a flexible robot

Abstract

End effector tracking of a two-link flexible robot is simulated using a linear quadratic Gaussian (LQG) dynamic regulator with an extended Kalman filter (EKF), a LQG with fuzzy logic adaptive EKF (FLAEKF), LQG with an EKF and a FLAEKF combined with time delays in the feedback loop to model nonminimum phase (NMP) response for a sensor noncollocated at the end effector and in the feed forward loop for corrective control action. A fuzzy logic system (FLS) vibration suppression control strategy is simulated for comparison. Results demonstrate FLS adaptive vibration suppression produces greater tracking accuracy than an EKF, FLAEKF or corrective time delays. In comparison with classical FID control or even with more advanced adaptive control strategies FLS vibration suppression gives better tracking control while execution time remains acceptable.