FRBF neural network and new Smith predictor for wireless networked control systems

Authors:
Feng Du;Wencai Du
Affiliations:
College of Information Sciences & Technology, Hainan University, Haikou;College of Information Sciences & Technology, Hainan University, Haikou
Venue:
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Year:
2009

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Abstract

Wireless network delay is the main factor that deteriorates the performance of the wireless networked control systems (WNCS). In order to effectively restrain the impact of network delay, as well as controlled plant might be time-variant or nonlinear, a novel approach is proposed that new Smith predictor combined with fuzzy radial basis function neural network (FRBFNN) for the WNCS. Because new Smith predictor hides predictor model of the network delay into real network data transmission process, further the network delay no longer need to be measured, identified or estimated on-line. Simultaneously this new Smith predictor doesn't include the prediction model of the controlled plant, thus it doesn't need to know the exact mathematical model of the controlled plant beforehand. It is applicable to some occasions that network delay is random, time-variant or uncertain, larger than one, even tens of sampling periods, and there are some data dropouts in closed loop. Based on IEEE 802.15.4 (ZigBee), the results of simulation show that this approach is effective.