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An integrated feedforward-feedback control structure utilizing a simplified global gravitational search algorithm to control nonlinear systems
Sādhanā ( IF 1.4 ) Pub Date : 2020-10-06 , DOI: 10.1007/s12046-020-01491-2
Omar Farouq Lutfy

This paper presents an integrated feedforward-feedback control structure to control nonlinear dynamical systems. This intelligent control system exploits a modified recurrent wavelet neural network (MRWNN) in the feedforward (FF) and the feedback (FB) loops of the control structure. Specifically, the MRWNN is proposed to boost the approximation performance of a previously reported network by employing two amendments to the original structure. To optimize the parameters of both the FF and the FB controllers, an enhanced version of the gravitational search algorithm (GSA) is developed to improve the searching capability of the original algorithm. In particular, two modifications were adopted, including the removal of two control parameters related to the gravitational constant in the original algorithm and the utilization of the global best solution to constitute the next generation of agents. Hence, the proposed algorithm is called the simplified global gravitational search algorithm (SGGSA), which has demonstrated better optimization performance compared to those of other techniques, including the original GSA. By conducting several evaluation tests using different nonlinear time-variant dynamical systems, the effectiveness of the proposed control structure was confirmed in terms of control precision and robustness against external disturbances. In addition, the MRWNN has exhibited a superior control performance compared with other related controllers.



中文翻译:

集成前馈-反馈控制结构,利用简化的全局重力搜索算法控制非线性系统

本文提出了一种集成的前馈-反馈控制结构来控制非线性动力学系统。该智能控制系统在控制结构的前馈(FF)和反馈(FB)回路中采用了改进的递归小波神经网络(MRWNN)。具体而言,提出了MRWNN,通过对原始结构进行两次修改来提高先前报告的网络的逼近性能。为了优化FF和FB控制器的参数,开发了重力搜索算法(GSA)的增强版本,以提高原始算法的搜索能力。特别是,进行了两项修改,包括删除原始算法中与重力常数有关的两个控制参数,以及利用全局最佳解来构成下一代代理。因此,提出的算法称为简化的全球重力搜索算法(SGGSA),与包括原始GSA在内的其他技术相比,该算法已显示出更好的优化性能。通过使用不同的非线性时变动力学系统进行几次评估测试,从控制精度和抵抗外部干扰的鲁棒性方面,证实了所提出的控制结构的有效性。此外,与其他相关控制器相比,MRWNN还具有出色的控制性能。该算法被称为简化的全球重力搜索算法(SGGSA),与包括原始GSA在内的其他技术相比,该算法具有更好的优化性能。通过使用不同的非线性时变动力学系统进行几次评估测试,从控制精度和抵抗外部干扰的鲁棒性方面,证实了所提出的控制结构的有效性。此外,与其他相关控制器相比,MRWNN还具有出色的控制性能。该算法被称为简化的全球重力搜索算法(SGGSA),与包括原始GSA在内的其他技术相比,该算法具有更好的优化性能。通过使用不同的非线性时变动力学系统进行几次评估测试,从控制精度和抵抗外部干扰的鲁棒性方面,证实了所提出的控制结构的有效性。此外,与其他相关控制器相比,MRWNN还具有出色的控制性能。所提出的控制结构的有效性在控制精度和抵抗外部干扰的鲁棒性方面得到了证实。此外,与其他相关控制器相比,MRWNN还具有出色的控制性能。所提出的控制结构的有效性在控制精度和抵抗外部干扰的鲁棒性方面得到了证实。此外,与其他相关控制器相比,MRWNN还具有出色的控制性能。

更新日期:2020-10-07
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