Abstract:The PTO torque load application test of a tractor is a key approach for evaluating power take- off performance and the reliability of field operations. Because field loads are strongly stochastic and non- repeatable due to operating-condition disturbances, extracting representative load characteristics and reproducing them in a controllable and repeatable test-bench environment is of great significance. To address the insufficient loading accuracy caused by the strong time-varying nonlinearity and dynamic parameter drift of a torque hydraulic loading system, an improved LSTM-MPC hybrid optimization control method for high-precision reproduction of PTO torque load spectra was proposed. A prediction- model-based receding-horizon MPC framework was developed, where the LSTM was employed to capture the input-output dynamics, and numerical perturbation was adopted for gradient approximation, combined with projected gradient descent to achieve online optimization, to enhance spectrum-tracking accuracy while maintaining real-time feasibility. In addition, an AMESim hydraulic loading system model incorporating refined characteristics of the swash-plate axial piston pump and the pilot-operated relief valve was established to provide a detailed representation of the internal nonlinear behaviors and to serve as the control plant. Based on an AMESim-Matlab co-simulation platform, the proposed method was compared with mainstream FNN-MPC, conventional MPC, and open-loop control. The results showed that the LSTM MPC achieved a coefficient of determination R2 of 0. 970 9 with a maximum overshoot of 5. 32% , demonstrating clear advantages in both prediction accuracy and dynamic response and offering a solution for improving the loading accuracy of tractor PTO torque spectra.
