Economic Performance Optimization by Set Point and Weighting Parameter Tuning based on LQG Controller Design

In the industrial process control, the variance reduction of process input and output contributes to economic performance improvement significantly. Several researches have formulated economic performance optimization problems taking account of a trade-off relation between process input and output variance. In these researches, the set points and a weighting parameter are optimized subject to the constraints of the upper and lower limits of process input and output signals. However, the previous works made no use of the analytical trade-off relation because it is too complex to be used as the constraint conditions. The present work introduces theoretical formulations that relate the weighting parameter with each input and output variance on the condition that the Linear Quadratic Gaussian (LQG) controllers are implemented as lower layer controllers. The proposed approach is applied to a two-input, two-output separation process model, and solves optimal weighting parameter based on LQG control. The numerical example also shows that the obtained optimal weighting parameter is effective for a MPC based learning algorithm.