Adaptive Neuro-Fuzzy Inference System Controller for Vibration Control of Reduced-Order Finite Element Model of Rotor-Bearing-Support System

Abstract

This paper evaluates the use of adaptive neuro-fuzzy inference system (ANFIS) controller to suppress the vibration in a rotor-bearing-support system, and compare the performance to LQR controller. ANFIS combines the smooth interpolation of fuzzy inference system (FIS) and the learning capability of adaptive neural network. The ANFIS controller design starts with initialization which includes loading the training data and generating the initial FIS. In this case, the gain values obtained from the LQR controller design previously conducted were used as training data for the ANFIS controller. After the training data is provided, the ANFIS controller learns through a certain optimization algorithm to adjust the parameters. In the current work, hybrid algorithm was used due to its faster convergence. To evaluate the performance, the ANFIS output was compared to the training data. From the evaluation, it can be concluded that ANFIS controller can replace LQR controller with no need to solve the LQR’s Riccati equation. However, in the initialization process, it needs training data obtained from LQR control design. Furthermore, ANFIS controller can replace more than one LQR controllers with different weighting matrices Q and/or R. In a more general tone, ANFIS controller can serve as an effective controller, given any arbitrary speed-gain pairs as its training data. Finally, ANFIS controller can serve as a better controller than LQR as long as tuning can be conducted adequately for that purpose.