Abdolhossein Ayoubi, Mohammad Sadegh Sanie, Moharram Kazemi, Saman Sobh Heydar



Synchronization of SA and AV Node Oscillators Using PSO Optimized RBF-based Controllers and Comparison with Adaptive Control

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This paper studies the synchronization of SA and AV Node Oscillators using PSO optimized RBF-based controllers systems. High levels of control activities may excite unmodeled dynamics of a system. The objective is to reach a trade-off between tracking performance and parametric uncertainty. Two methods are proposed to synchronize general forms of Van Der Pol (VDP) Model and their performance. These methods use the radial basis function (RBF)- based neural controllers for this purpose. The first method uses a standard RBF neural controller. Particle swarm optimization (PSO) algorithm is used to derive and optimize RBF controller parameters. In the second method, an error integral term is added to the equations of RBF neural network. The coefficients of error integral component and parameters of RBF neural network are also derived and optimized via PSO algorithm. Simulation results show the effectiveness and superiority of proposed methods in both performances in comparison with adaptive controller


Synchronization, Van der Pol Model, SA and AV Node Oscillators, RBF, PSO Algorithm, Adaptive Conrol, Optimization Algorithm, system Dynamics, Simulation Results, Controller Parameters, impulses.


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Cite this paper

Abdolhossein Ayoubi, Mohammad Sadegh Sanie, Moharram Kazemi, Saman Sobh Heydar. (2017) Synchronization of SA and AV Node Oscillators Using PSO Optimized RBF-based Controllers and Comparison with Adaptive Control. International Journal of Biology and Biomedicine, 2, 98-108


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