Performance Evaluation of Beluga Whale Optimization–Long Short-Term Memory–Random Forest Networks for Trajectory Control and Energy Optimization in Excavator Systems

Over the past few years, reducing energy consumption in hydraulic excavators has gained increasing attention, driving significant research in this field. One effective strategy involves integrating hydrostatic transmission (HST) and hydraulic pump/motor (HPM) systems into hydraulic excavators. However, challenges like disturbances, throttling-induced pressure drops, and fluid leakage often hinder both positional accuracy and energy efficiency. To tackle these issues, our study proposes a sophisticated dynamic forecasting model for positional control, integrating beluga whale optimization (BWO), long short-term memory (LSTM), and random forest (RF) techniques. The approach begins with dynamic data evaluation using Pearson’s correlation analysis to identify tuning parameters that have moderate to strong correlations with control variables, which are then used as inputs for predictive modeling. Initially, a standalone LSTM framework is developed to estimate the system’s positional output, with BWO optimizing four key tuning parameters. Subsequently, a hybrid BWO-enhanced LSTM-RF system is deployed to capture complex nonlinear patterns, improving the accuracy of motion trajectory predictions. Simulations and experiments confirm that our approach achieves a positional error below 3 mm, ensuring precise tracking and providing reliable data for operators. Compared to conventional proportional–integral–derivative (PID) controllers, standalone LSTM-RF, and a hybrid controller combining particle swarm optimization (PSO), LSTM, a gated recurrent unit (GRU), and PID (PSO-LSTM-GRU-PID), our method achieves superior tracking precision and energy savings of 12.46%, 8.98%, and 3.97%, respectively.