Fuzzy adaptive vibration suppression and noise filtering for flexible robot control

Abstract

Tracking the end effector of a two-link flexible robot is simulated using control strategies with an inverse dynamics robot model and Jacobian transpose control law. Results are presented for linear quadratic Gaussian (LQG) dynamic regulator with extended Kalman filter (EKF); LQG with fuzzy logic adaptive EKF (FLAEKF); LQG with EKF and FLAEKF combined with fuzzy logic system (FLS) vibration suppression. In general, FLS vibration suppression overrides noise filtering in achieving tracking accuracy. In comparison with classical PID control or even with more advanced adaptive control strategies FLS vibration suppression gives better trajectory tracking while execution time remains acceptable.