the steam temperature during the extraction process have a great influence on the essential oil quality. Lately, many of the research efforts are seen trying to develop a new control technique to regulate the steam temperature. However, PID is noted to have a simple structure, very straight forward implementation and ease to handle especially when deal with the industrial process. But, the challenges during design the PID is to tune the PID parameters gain that suite to the plant. To overcome this problem, the online tuning PID using the fuzzy logic controller with self-tuning method is utilized to regulate the steam temperature. From the result, the performance of self-tuning fuzzy PID (STFPID) demonstrated good improvement in term of process dynamic characteristic which are process rise time, process settling time, percent overshoot and steady state error. In this work, the proposal control technique has shown the ability to track any parameters changes and have fast recovery the output response during disturbance period.

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This paper present the implementation of real time PID controller for hydro-diffusion steam distillation essential oil extraction system based on comparison of Gradient Descent (GD) and Ziegler Nichols (ZN) tuning methods. The first order of Auto Regressive Exogenous (ARX) model was used to describe the behavior of the temperature system and will be use in the controller design. A PID controller is expected to execute a robust response towards parameters changes and better control of steam temperature during distillation process. The system had been evaluated based on rise time, % overshoot, settling time and root mean square error (RMSE). The temperature control was achieved by controlling the voltage fed to the heater ranging from 0V to 5V via digitalto-analogue converter (DAC). Robustness test of the PID controller are based on: i) introduce disturbance and ii) set-point tracking. From the result, the performance of PID controller using GD tuning method reveals that this controller can be adapt for the system because being sensitive to parameters changes and robust as the response can compensate the load disturbance and set point change.