A paper published in 1960 by Rudolf Kálmán “A New Approach to Linear Filtering and Prediction Problems” is the basis for the Kalman Filter. The Kalman filter has been successfully used in a wide variety of applications:
• The guidance of commercial airplanes
• Seismic data processing
• Nuclear power plant instrumentation
• Vehicle navigation and control (e.g. the Apollo vehicle),
• Radar tracking algorithms for ABM applications
However, the complexity of the Kalman filter algorithm is often a barrier in the application of this filtering technique in the process industry. The original Kalman filter as illustrated below was designed to address a general multivariate environment where the process and measurement noise covariance Q and R are known and used to dynamically calculate the Kalman gain, K.
At Emerson Exchange, 2013, Willy Wojsznis and I will host a workshop (8-4361) “Addressing Control in the Presence of Process and Measurement Noise” in which we will present a practical application of a scalar Kalman Filter. In the workshop we discuss the expected improvements in closed loop control that may be achieve when the control measurement is characterized by significant measurement or process noise. Also, we will show a DeltaV linked composite available through the Application Exchange (in late July, 2013) for implementation of the Kalman filter. This composite is designed for use with the PID function block in closed loop control and may be installed on any version of DeltaV. Information will be provided on a DeltaV module that may be used to demonstrate and get more familiar with the Kalman filter in a test environment.
If you are interested in learning more about how the Kalman filter may be applied in DeltaV, then I encourage you to attend this workshop at Emerson Exchange 2013. In case you are not able to attend Emerson Exchange, I will post the workshop presentation in a blog on the workshop after Emerson Exchange.