Improving Controller Performance in a Powder Blending Process using Predictive Control

The paper will discuss the general topic of predictive control techniques for industrial applications, in particular the implementation of Kalman Filter State Estimators in such control schemes. The paper presents the investigation of predictive control methodologies for the control of a model powder blending process. This investigation was carried out in an effort to improve the performance of a closed loop control system which was limited by the inaccuracy of parameters measured by a suite of smart sensors, in this instance constituent concentration and flowrate of a two-part powder blend as estimated by statistical models analysing multi-sensor data. A number of predictive control principles, including the addition of Kalman Filters to traditional closed loop control and Model Predictive Control, were investigated using MATLAB (R) Software to improve the estimation of these parameters and thus control them more precisely. The results obtained show that faster dynamic response and greater accuracy can be achieved through the implementation of Kalman filters.