Unwanted errors caused by hand tremors are a bottleneck for the application of teleoperation robots in space explorations, underwater explorations, and minimally invasive surgery. In order to eliminate hand tremor signals in teleoperation control systems, two tremor-filtering models based on artificial neural networks are defined. With the purpose of decreasing the errors of tremor filtering models, a novel Broad Learning Extreme Learning Machine with Improved Equilibrium Optimizer (IEO-BLELM) is proposed. Firstly, the structure of Extreme Learning Machine (ELM) is re-designed by coupling with broad learning. Time series and smoothing are introduced as feature extraction layer and enhancement layer, respectively. Secondly, different activation functions are selected to construct Broad Learning ELM (BLELM). An Improved Equilibrium Optimizer is introduced to optimize input weights, thresholds, and parameters of the BLELM model. To verify the performance of the IEO-BLELM model, the proposed model is applied to six examples and compared with other models. The results show that Mean Absolute Error (MAE) of the proposed model in six examples is at least lower than 0.253. As compared with the ELM, the MAE of the IEO-BLELM model can be decreased by 51.03% through reasonable improvement strategies. In particular, estimation errors are mainly contributed to peak and the proposed model significantly reduces the peak errors. The forecasting performance of the proposed model is better than that of previously existing models. In general, this study provides effective models to eliminate hand tremor signals in teleoperation control systems.

手颤引起的不必要的错误是遥操作机器人在太空探索、水下探索和微创手术中应用的瓶颈。为了消除遥操作控制系统中的手颤信号，定义了两种基于人工神经网络的颤抖滤波模型。为了减少震颤滤波模型的误差，提出了一种新型的具有改进平衡优化器的广泛学习极限学习机（IEO-BLELM）。首先，结合广泛学习重新设计极限学习机（ELM）的结构。分别引入时间序列和平滑作为特征提取层和增强层。其次，选择不同的激活函数来构建广泛学习 ELM（BLELM）。引入了改进的均衡优化器来优化 BLELM 模型的输入权重、阈值和参数。为了验证 IEO-BLELM 模型的性能，将所提出的模型应用于六个示例并与其他模型进行比较。结果表明，所提出模型在六个例子中的平均绝对误差（MAE）至少低于0.253。与 ELM 相比，IEO-BLELM 模型的 MAE 可以通过合理的改进策略降低 51.03%。特别地，估计误差主要是对峰值的贡献，所提出的模型显着降低了峰值误差。所提出模型的预测性能优于现有模型。总的来说，这项研究提供了有效的模型来消除遥操作控制系统中的手颤信号。