Optimization of EHA Hydraulic Cylinder Buffer Design Using Enhanced SBO–BP Neural Network and NSGA-II

In order to solve a certain type of Electro-Hydrostatic Actuators (EHA) hydraulic cylinder small cavity buffer end impact problem, based on AMESim to establish a hydraulic cylinder small cavity buffer machine–hydraulic joint simulation model. First, four important structural parameters, namely, the fitting clearance G of the buffer sleeve and buffer hole, the fixed orifice D, the wedge face angle , and the wedge face length L₁ were selected to analyze their influence on the pressure of the buffer chamber and the end speed of the piston. Second, enhanced Social Behavior Optimization (SBO) was used to optimize the back-propagation neural network (BP) model to construct a prediction model for the buffer time T of the small chamber of the hydraulic cylinder, the end-piston speed V_e, the rate of change of the end-piston speed V_r, and the return speed of the hydraulic oil V_h. The SBO–BP model performed well in several key performance evaluation metrics, showing better prediction accuracy and generalization performance. Finally, the multi-objective Non-dominated Sorting Genetic Algorithm II (NSGA-II) was used to optimize the hydraulic cylinder small-cavity buffer structure using the multi-objective NSGA-II with the objectives of the shortest buffer time, the minimum end-piston speed, the minimum change rate of the end-piston speed, and the minimum hydraulic oil reflux speed. The optimized design reduced the piston end speed from 0.060 m/s to 0.032 m/s, corresponding to a 46.7% improvement. The findings demonstrate that the proposed hybrid optimization approach effectively alleviates the end-impact problem of EHA small-cavity buffers and provides a novel methodology for achieving high-performance and reliable actuator designs.