Simulation of process identification and controller tuning for flow control system

29th Symposium of Malaysian Chemical Engineers 2016

Chew, I.M.^1,2 ; Wong, F.^2 ; Bono, A.^2 ; Wong, K.I.^1

Faculty of Engineering, Curtin University Malaysia, CDT 250, Sarawak, Miri

98009, Malaysia^1

Faculty of Engineering, Universiti Malaysia Sabah, Jalan UMS, Sabah, Kota Kinabalu

88400, Malaysia^2

关键词: Controller performance;    Disturbance rejection performance;    Industrial processs;    Performance analysis;    Process control simulators;    Process identification;    Satisfactory control;    Stability performance;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/206/1/012058/pdf
DOI  :  10.1088/1757-899X/206/1/012058

来源: IOP

【 摘 要 】

PID controller is undeniably the most popular method used in controlling various industrial processes. The feature to tune the three elements in PID has allowed the controller to deal with specific needs of the industrial processes. This paper discusses the three elements of control actions and improving robustness of controllers through combination of these control actions in various forms. A plant model is simulated using the Process Control Simulator in order to evaluate the controller performance. At first, the open loop response of the plant is studied by applying a step input to the plant and collecting the output data from the plant. Then, FOPDT of physical model is formed by using both Matlab-Simulink and PRC method. Then, calculation of controller's setting is performed to find the values of Kc and τi that will give satisfactory control in closed loop system. Then, the performance analysis of closed loop system is obtained by set point tracking analysis and disturbance rejection performance. To optimize the overall physical system performance, a refined tuning of PID or detuning is further conducted to ensure a consistent resultant output of closed loop system reaction to the set point changes and disturbances to the physical model. As a result, the PB = 100 (%) and τi = 2.0 (s) is preferably chosen for setpoint tracking while PB = 100 (%) and τi = 2.5 (s) is selected for rejecting the imposed disturbance to the model. In a nutshell, selecting correlation tuning values is likewise depended on the required control's objective for the stability performance of overall physical model.