Broad learning extreme learning machine for forecasting and eliminating tremors in teleoperation

Abstract 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.