TIME DELAY COMPENSATION USING ADAPTIVE LINEAR NEURAL NETWORKS FOR NETWORKED CONTROL SYSTEMS
ABDALLA, A., ASSAL, M., ELBASHEIR, H. (2007). TIME DELAY COMPENSATION USING ADAPTIVE LINEAR NEURAL NETWORKS FOR NETWORKED CONTROL SYSTEMS. EKB Journal Management System, 12(ASAT Conference, 29-31 May 2007), 1-10. doi: 10.21608/asat.2007.23957
AHMED E. ABDALLA; MOHAMMED H. ASSAL; HYDER O. ELBASHEIR. "TIME DELAY COMPENSATION USING ADAPTIVE LINEAR NEURAL NETWORKS FOR NETWORKED CONTROL SYSTEMS". EKB Journal Management System, 12, ASAT Conference, 29-31 May 2007, 2007, 1-10. doi: 10.21608/asat.2007.23957
ABDALLA, A., ASSAL, M., ELBASHEIR, H. (2007). 'TIME DELAY COMPENSATION USING ADAPTIVE LINEAR NEURAL NETWORKS FOR NETWORKED CONTROL SYSTEMS', EKB Journal Management System, 12(ASAT Conference, 29-31 May 2007), pp. 1-10. doi: 10.21608/asat.2007.23957
ABDALLA, A., ASSAL, M., ELBASHEIR, H. TIME DELAY COMPENSATION USING ADAPTIVE LINEAR NEURAL NETWORKS FOR NETWORKED CONTROL SYSTEMS. EKB Journal Management System, 2007; 12(ASAT Conference, 29-31 May 2007): 1-10. doi: 10.21608/asat.2007.23957

TIME DELAY COMPENSATION USING ADAPTIVE LINEAR NEURAL NETWORKS FOR NETWORKED CONTROL SYSTEMS

International Conference on Aerospace Sciences and Aviation Technology
Article 42, Volume 12, ASAT Conference, 29-31 May 2007, May 2007, Page 1-10  XML PDF (159.65 K)
Document Type: Original Article
DOI: 10.21608/asat.2007.23957
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Authors
AHMED E. ABDALLA1; MOHAMMED H. ASSAL2; HYDER O. ELBASHEIR3
1Egyptian Army.
2Modern Academy,Cairo, Egypt.
3Sudanese Army.
Abstract
In real time systems, particularly control systems, delays or dropped packets may cause performance degradation and system destabilization. In order to consider the uncertainty of communication delays and packet losses, intelligent computational approaches such as fuzzy logic, neural networks, and genetic algorithm can be used. In this paper, The effect of time delay is compensated via building undelayed plant model based on delayed model data using the Adaptive Linear Neuron networks (ADALINE). In ADALINE the linear networks are adjusted at each time step based on new input and target vectors which can find weights and biases that minimize the network's sum-squared error for recent input and target vectors. The proposed works are applied on distributed control of a DC servo system. The network is built using the true time MATLAB toolbox. Several simulation examples are applied using CAN network to clarify the efficiency of the proposed methods.
Keywords
Networked Control Systems; ADALINE; time delay; Neural Networks; Distributed control; and CAN Network
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