Parameter Identification in Model Based Networked Control Systems Using Kalman Filters
Source: University of Notre Dame
The applicability of Model Based Networked Control Systems (MB-NCS) is often limited by the inexact knowledge of the dynamics of the system being controlled. On-line identification of system parameters is used in this paper to upgrade the model of the system, which is used to control the real system when feedback information is unavailable. Background material is offered on the topic of parameter identification with emphasis on the Recursive Least Squares algorithm. The Extended Kalman Filter (EKF) is analyzed in detail in the context of parameter identification and implemented in the Model Based Networked Control Systems (MB-NCS) framework. Simulations are included that show the efficiency of these tools.