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

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. This matter changes the rules of controlling the system and achieving an acceptable control performance. In fact, the objective here is to reach a trade-off between tracking performance and parametric uncertainty. Two methods are proposed to synchronize the 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 the parameters of the RBF controller. In the second method, with the aim of increasing the robustness of the RBF controller, an error integral term is added to the equations of RBF neural network. For this method, the coefficients of the error integral component and the parameters of RBF neural network are also derived and optimized via PSO algorithm. For better comparison, simulation results show the effectiveness and superiority of the proposed methods in both performances in comparison with SMC controller.