A new tuning method for two-degree-of-freedom internal model control under parametric uncertainty

The purpose of controller tuning is to determine the parameters of controller in order to ensure the time response of close-loop control system at the desired performance. Proportional Integral Derivative (PID) controller has been used in the industry since 1940’s for this purpose. However, the PID controller can not completely compensate for the complexity of industrial processes and desired high product quality due to interactions, nonlinearities, and time delay of the process variables. Internal model control (IMC) has been developed to overcome the deficiencies of the PID. Unfortunately, IMC yields very good performance for set point tracking, but gives sluggish response for disturbance rejection problem. The present study has developed a controller for disturbance rejection based on feedback / feedforward IMC structure. The controller is then called as feedback 2DOF-IMC. A new tuning method has been proposed for the controller. The proposed tuning method consists of three steps: Firstly, determine the worst case of the model uncertainty. Secondly, specify the parameter of set point controller using maximum peak (Mp) criteria. And thirdly, obtain the parameter of the disturbance rejection controller using gain margin (GM) criteria. The proposed method is called Mp-GM tuning method. The effectiveness of the proposed feedback 2DOF-IMC and Mp-GM tuning method has evaluated and compared with standard 2DOF-IMC using IMCTUNE and Kaya 2DOF-IMC using Mp-GM tuning as bench mark. The evaluation and comparison are investigated through simulation and implementation on a number of first order plus dead time (FOPDT) and higher order processes. The FOPDT process tested include processes with controllability ratio in the range 0.7 to 2.5. The higher processes include second order with underdamped and third order with nonminimum phase processes. Although the two of higher order process are considered difficult processes, the proposed feedback 2DOF-IMC and Mp-GM tuning method were able to obtain the optimal controller even under process uncertainties. The proposed feedback 2DOF-IMC and the proposed Mp-GM tuning are also successfully implemented in real-time on a laboratory scale air heater pilot plant. The process model is divided into two regions. The time responses show that the proposed feedback 2DOF-IMC and the proposed Mp- GM tuning gave faster set point tracking and disturbance rejection responses than 1DOF-IMC and standard 2DOF-IMC in both regions