Synchronisation of SA and AV node oscillators using PSO optimised RBF-based controllers and comparison with adaptive control

Abdolhossein Ayoubi, M. S. Sanie, M. Kazemi
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引用次数: 5

Abstract

This paper studies the synchronisation of SA and AV node oscillators using PSO optimised RBF-based controllers systems. High levels of control activities may excite unmodelled dynamics of a system. The objective is to reach a trade-off between tracking performance and parametric uncertainty. Two methods are proposed to synchronise 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 optimisation (PSO) algorithm is used to derive and optimise 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 optimised via PSO algorithm. Simulation results show the effectiveness and superiority of proposed methods in both performances in comparison with the adaptive controller.
利用PSO优化的rbf控制器同步SA和AV节点振荡器,并与自适应控制进行比较
本文研究了基于粒子群优化的rbf控制器系统中SA和AV节点振荡器的同步问题。高水平的控制活动可能激发系统未建模的动力学。目标是达到跟踪性能和参数不确定性之间的权衡。提出了两种方法来同步范德波(VDP)模型的一般形式及其性能。这些方法使用基于径向基函数(RBF)的神经控制器来实现此目的。第一种方法使用标准的RBF神经控制器。采用粒子群优化算法推导和优化RBF控制器参数。第二种方法是在RBF神经网络方程中加入误差积分项。推导了RBF神经网络的误差积分系数和参数,并利用粒子群算法对其进行了优化。仿真结果表明,与自适应控制器相比,所提方法在两种性能上都具有优越性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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