ANALYSIS OF THE THROTTLE SETTINGS UNDER UNCERTAIN INFORMATION

Abstract

Although classical fuzzy logic controllers are capable of modelling non-linear control systems, they fail to consider the reliability of linguistic information, sensor measurements, and expert knowledge. In this paper, an intelligent controller based on the use of Z-numbers is developed for steam-turbine throttle control. Linguistic information and its confidence degree are considered simultaneously in such a controller. The temperature and pressure values are taken as input variables, while the throttle rotation is selected as the controller output variable. At first, the Z-number representation system is constructed to include the credibility of linguistic measurements and control rules. Then, a Mamdani Type-1 fuzzy controller with nine rules is constructed. Fuzzy controller evaluation is conducted using 30 pairs of temperature and pressure input values, where obtained output values are used to train and validate an adaptive neuro-fuzzy inference system. Dataset consists of 20 training and 10 independent test samples. Grid partitioning and fuzzy c-means clustering algorithms along with linear and constant Sugeno type output functions are compared using root mean square error measure. Selected neuro-fuzzy system obtains zero training error and 8.877 independent test RMSE. These results prove the validity of developed method based on Z-number logic, fuzzy inference and neuro-fuzzy learning.