This article presents a novel hybrid algorithm based on the grey-wolf optimizer and whale optimization algorithm, referred here as grey-wolf optimizer–whale optimization algorithm, for the dynamic parametric modelling of a four degree-of-freedom master–slave robot manipulator system. The first part of this work consists of testing the feasibility of the grey-wolf optimizer–whale optimization algorithm by comparing its performance with a grey-wolf optimizer, whale optimization algorithm and particle swarm optimization using 10 benchmark functions. The grey-wolf optimizer–whale optimization algorithm is then used for the model identification of an experimental master–slave robot manipulator system using the autoregressive moving average with exogenous inputs model structure. Obtained results demonstrate that the hybrid algorithm is effective and can be a suitable substitute to solve the parameter identification problem of robot models.

本文提出了一种基于灰狼优化器和鲸鱼优化算法的新颖混合算法，这里称为灰狼优化器-鲸鱼优化算法，用于四自由度主从机器人机械手系统的动态参数建模。这项工作的第一部分包括通过将其性能与灰狼优化器，鲸鱼优化算法和使用10个基准函数的粒子群优化进行比较，测试灰狼优化器-鲸鱼优化算法的可行性。然后使用灰狼优化程序-鲸鱼优化算法，使用具有外生输入模型结构的自回归移动平均值，对实验性主/从机器人操纵器系统进行模型识别。