Fuzzy Logic controller for glycerin bleaching temperature control

Abstract

Controlling a process with significant lag is always a challenge task. The response is slow and can cause instability of the controller. In this work the Fuzzy Logic controller is purposed to control the heating stage for glycerin bleaching process. The Fuzzy PD and Fuzzy PI controllers are designed with different sets of membership function and rules. 3, 5 and 7 membership functions with 9, 25 and 49 rules are compared and evaluated to find the suitable set for this application. It was found that the implementation of Fuzzy Logic controller with 25 rules is sufficient to give good control for glycerin bleaching heating process.

Modelo matemático por identificación paramétrica y diseño de controlador difuso en la sección de enfriamiento de una planta de calentamiento-enfriamiento de fluidos

“…El número de funciones de pertenencia de las variables difusas están en valores impares 3, 5 y 7 que darían como resultado 9, 25 y 49 reglas respectivamente. En la investigación realizada por [20] se determinó que 25 reglas son suficientes para dar un buen control de temperatura en la glicerina y que si aumentaba el número de reglas, no había mayor cambio en la respuesta del controlador.…”

“…estimado en la ecuación (15), el controlador difuso y las ganancias del PID tal como propone [20]. Se implementa el modelo residual para observar un comportamiento más real a la planta.…”

Real time robustness test evaluation performance for intelligent fuzzy controller in extraction process

“…Controller performance is used to evaluate the controller response by measuring the step response of the controller. The good controller is defined as a controller that could minimize the error between desired target and output in the steady-state manner [17]. The standard performance indexes used in the system's step response are percentage overshoot, rise time, settling time and accumulated error [3][4][5][6][7][18][19].…”