Development of Intelligent Controller with Virtual Sensing

In many industrial plants, some key variables cannot always be measured on-line and for the purpose of control, an alternative of sensing system is required. This paper is concerned with a development of an alternative intelligent control strategy, which is an integration between the neuro-fuzzy based controller and virtual sensing system. This allows an immeasurable variable to be inferred and used for control. The virtual sensor is composed of the Diagonal Recurrent Neural Network (DRNN) for plant modeling and the Extended Kalman Filter (EKF) as the estimator with inputs from DRNN. The integration between virtual sensor and the controller enables a development of an on-line control scheme involving the immeasurable variable. The real-time implementation demonstrates the applicability and the performance of the proposed intelligent control scheme, especially in dealing with nonlinear processes.