A New Tuning Method for Two-Degree-of-Freedom Internal Model Control under Parametric Uncertainty

Internal model control （IMC） yields very good performance for set point tracking, but gives sluggish response for disturbance rejection problem. A two-degree-of-freedom IMC （2DOF-IMC） has been developed to overcome the weakness. However, the setting of parameter becomes a complicated matter if there is an uncertainty model. The present study proposes a new tuning method for the controller. The proposed tuning method consists of three steps. Firstly, the worst case of the model uncertainty is determined. Secondly, the parameter of set point con- troller using maximum peak （Mp） criteria is specified, and finally, the parameter of the disturbance rejection con- troller using gain margin （GM） criteria is obtained. The proposed method is denoted as Mp-GM tuning method. The effectiveness of Mp-GM tuning method has evaluated and compared with IMC-controller tuning program （IMCTUNE） as bench mark. The evaluation and comparison have been done through the simulation on a number of first order plus dead time （FOPDT） and higher order processes. The FOPDT process tested includes processes with controllability ratio in the range 0.7 to 2.5. The higher processes include second order with underdarnped and third order with nonminimum phase processes. Although the two of higher order processes are considered as difficult processes, the proposed Mp-GM tuning method are able to obtain the good controller parameter even under process uncertainties.

Prediction of Particles-Air Movement in Silo during Filling Operation

Awareness of dust explosion hazards during silo filling operation is important for safety measures. Thus, information on particles-air flow field is required to assess the likelihood of the hazard. Flow field visualization via experimental investigation associated with difficulties and risks. Hence, in the present study, a modeling formulation using commercial computational fluid dynamics (CFD) code, FLUENT software was employed to predict an insight of flow field distribution, in terms of mean and root mean square (RMS) velocities vectors in cylindrical silo during axial filling. According to the simulation results, predicted flow field has a great influence to the silo height and distance to the silo wall due to gravitational force and movement of fugitive dust and re-circulation of air. The results showed that the predicted data were in very good agreement with experimental data obtained from the literature. The maximum error was around 10%. The study has gone some way towards enhancing our understanding of the particles-air behavior inside industrial equipments during filling operation.