基于改进LSTM-MPC的拖拉机PTO动态载荷液压加载控制方法研究

Hydraulic Loading Control Method for Tractor PTO Dynamic Load Application Based on Improved LSTM-MPC

摘要:

拖拉机PTO转矩载荷加载试验是评估动力输出特性与田间作业可靠性的关键手段。由于田间载荷受工况扰动影响具有强随机性与不可重复性,提取其载荷特征并在试验场实现可控、可重复复现具有重要意义。针对转矩液压加载系统强时变非线性及参数动态漂移导致的加载精度不足问题,本文提出一种面向PTO转矩载荷谱高精度复现的改进LSTM-MPC混合优化控制方法。构建基于预测模型的MPC滚动优化框架,利用LSTM的时序建模能力刻画系统输入输出动态关系,并采用数值微扰方法进行梯度近似,结合投影梯度下降实现在线优化求解,以提升载荷谱跟踪精度并兼顾实时性;建立包含斜盘式柱塞泵与先导式溢流阀精细化特性的AMESim液压加载系统模型,实现了对斜盘式柱塞泵与先导式溢流阀内部复杂特性的精细化表征并作为控制对象。依托AMESim-Matlab联合仿真平台,与当前主流的FNN-MPC、传统MPC以及开环控制进行对比。结果表明,LSTM-MPC决定系数R2达0.9709,最大超调量为5.32%,在预测精度与动态响应方面具备显著优势,为提升拖拉机PTO转矩载荷加载精度提供了新方法。

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.