Experimental study on active vibration control for a kind of two-link flexible manipulator

This paper presents experimental investigations on active vibration control of a two-link flexible manipulator (TLFM), utilizing a generalized minimum variance self-tuning control (GMVSTC) and Takagi-Sugeno model based fuzzy neural network control (TS-FNN) schemes. The GMVSTC algorithm consists of an on-line identifier in the form of controlled autoregressive moving average model and a vibration control signal generator, and the TS-FNN control algorithm generates control actions taking full advantages of fuzzy logic controller and a neural controller. Experimental setup of the two-link flexible manipulator is constructed. Experimental comparison research on vibration attenuation is conducted during and after the motor motion, to verify the designed controllers. The effectiveness of the designed controllers is evaluated in terms of vibration suppression as compared to that of the classical PD control. The experimental results demonstrate that the designed controller can damp out both the large and the small amplitude vibration of a two-link flexible manipulator more quickly than that the traditional linear PD controller, especially for the small amplitude residual vibration.