Position control for intelligent pneumatic actuator using generalized minimum variance control

Abstract

Pneumatic actuators are finding increasing acceptance these days due to their low cost, ease of maintenance and moderately high power to weight ratio. A position model for IPA has been proposed by using system identification techniques resulted in a transfer function model. Generalized minimum variance control (GMVC) is one of the commonly control algorithms that used in the pneumatic actuators area. The performance of an algorithm is obtained by combining a generalized minimum variance control with a recursive estimator for the controller parameters. In this project, an indirect self-tuning generalized minimum variance control is used to assure the system output response can track any changes in the reference set point. This project aims to design generalized minimum variance controller to control the position of the IPA. The controller will be designed using MATLAB/SIMULINK based on the proposed models. The simulations results will be compared with the other types of controller (fuzzy logic controller) simulation results in order to see the performance of the GMVC.