Improving Estimation Performance in Networked Control Systems Applying the Send-on-delta Transmission Method

This paper is concerned with improving performance of a state estimation problem over a network in which a send-on-delta (SOD) transmission method is used. The SOD method requires that a sensor node transmit data to the estimator node only if its measurement value changes more than a given specified δ value. This method has been explored and applied by researchers because of its efficiency in the network bandwidth improvement. However, when this method is used, it is not ensured that the estimator node receives data from the sensor nodes regularly at every estimation period. Therefore, we propose a method to reduce estimation error in case of no sensor data reception. When the estimator node does not receive data from the sensor node, the sensor value is known to be in a (−δi,+δi) interval from the last transmitted sensor value. This implicit information has been used to improve estimation performance in previous studies. The main contribution of this paper is to propose an algorithm, where the sensor value interval is reduced to (−δi/2,+δi/2) in certain situations. Thus, the proposed algorithm improves the overall estimation performance without any changes in the send-on-delta algorithms of the sensor nodes. Through numerical simulations, we demonstrate the feasibility and the usefulness of the proposed method.

[1]  James Moyne,et al.  Using deadbands to reduce communication in networked control systems , 2002, Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301).

[2]  Hong Ye,et al.  Scheduling of networked control systems , 2001 .

[3]  Young Soo Suh,et al.  Modified Kalman filter for networked monitoring systems employing a send-on-delta method , 2007, Autom..

[4]  K. Åström,et al.  Comparison of Riemann and Lebesgue sampling for first order stochastic systems , 2002, Proceedings of the 41st IEEE Conference on Decision and Control, 2002..

[5]  M. Miskowicz.  Analytical approximation of the uniform magnitude-driven sampling effectiveness , 2004, 2004 IEEE International Symposium on Industrial Electronics.

[6]  Bruce A. Francis,et al.  Limited Data Rate in Control Systems with Networks , 2002 .

[7]  Marek Miskowicz,et al.  Send-On-Delta Concept: An Event-Based Data Reporting Strategy , 2006, Sensors (Basel, Switzerland).

[8]  Nandit Soparkar,et al.  Trading computation for bandwidth: reducing communication in distributed control systems using state estimators , 2002, IEEE Trans. Control. Syst. Technol..

[9]  Guanrong Chen,et al.  Introduction to random signals and applied Kalman filtering, 2nd edn. Robert Grover Brown and Patrick Y. C. Hwang, Wiley, New York, 1992. ISBN 0‐471‐52573‐1, 512 pp., $62.95. , 1992 .

[10]  J. Ploennigs,et al.  A traffic model for networked devices in the building automation , 2004, IEEE International Workshop on Factory Communication Systems, 2004. Proceedings..

[11]  Wilmar Hernandez,et al.  Improving the Responses of Several Accelerometers Used in a Car Under Performance Tests by Using Kalman Filtering , 2001 .