Order Diminution of Bicycle-Robot Controller by Exploiting Markov-Parameters based Error-Minimization Using Jaya Algorithm

2022 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES) Pub Date : 2022-12-14 DOI:10.1109/PEDES56012.2022.10080767

U. Yadav, V. Meena, Himanshu Monga, N. Patnana, Vinay Singh

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引用次数: 1

Abstract

In this proposal, order diminution of self-balanced linear-momentum based bicycle-robot (LMBR) controller is proposed with the help of time-moments (TMs) and Markov-parameters (MPs) of higher-order (HO) LMBR controller and its desired reduced-order (RO) model by utilizing jaya algorithm. Firstly, the TMs and MPs of HO LMBR controller and its RO model are obtained. The TMs of HO LMBR controller and RO model are exploited for steady state matching. The MPs are utilized to construct the objective function which is weighted summation of errors in between MPs of HO LMBR controller and RO model. The framed objective function is minimized with the help of jaya algorithm. The minimization of errors between MPs of HO LMBR controller and RO model are done for ascertainment of unknown coefficients of desired RO model. The proposed RO model for HO LMBR controller is compared with the model obtained by exploiting other order reduction methods available in the literature.

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基于Jaya算法的马尔可夫参数误差最小化的自行车机器人控制器降阶

本文利用高阶(HO)自平衡线性动量自行车机器人(LMBR)控制器的时间矩(TMs)和马尔可夫参数(MPs)及其期望的降阶(RO)模型，利用jaya算法提出了自平衡线性动量自行车机器人(LMBR)控制器的降阶方法。首先，得到了HO - LMBR控制器及其RO模型的TMs和MPs。利用HO - LMBR控制器和RO模型的TMs进行稳态匹配。利用MPs构造目标函数，该目标函数是HO LMBR控制器的MPs与RO模型之间误差的加权和。利用jaya算法对框架目标函数进行最小化。为了确定期望RO模型的未知系数，最小化了HO LMBR控制器的MPs与RO模型之间的误差。将所提出的RO模型与利用其他文献中可用的降阶方法得到的模型进行了比较。

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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来源期刊

2022 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES)

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