Search

In addressing challenges associated with the constrained enhancement of range performance in pure electric tractors, the difficulty in achieving genuinely eco-friendly agricultural machinery with hybrid powertrains, and the outdated approaches to energy optimization in hydrogen fuel cell tractors, a comprehensive methodology covering the entire spectrum of system design, optimization, and validation is presented. This method constructs an integrated electromechanical and hydraulic power bond graph model for an agricultural tractor, encompassing energy systems, drive systems, and lifting systems. It achieves rapid interaction between 20-sim software and Matlab/Simulink software through a script toolbox. In order to tackle the real-time applicability challenges of dynamic programming, a hierarchical dynamic programming is proposed. The dynamic programming strategy results serve as the upper-level output. At the lower layer, preprocessing of impact value variables is carried out to filter the input variables for the general regression neural network. This enables the real-time application of the dynamic programming algorithm and significantly reduces training time. Finally, validation is conducted through both model-in-the-loop and hardware-in-the-loop. The results demonstrate the correctness and superiority of the designed tractor simulation model and energy management strategy model compared to the power-following strategy. Specifically, in plowing conditions, the dynamic programming and hierarchical dynamic programming strategies exhibited reductions of 10.278% and 6.728%, respectively, in equivalent hydrogen consumption compared to power-following strategy. In transportation conditions, the reductions are 16.02% for dynamic programming strategy and 4.87% for hierarchical dynamic programming strategy, both relative to power-following strategy. This study lays the theoretical groundwork for modeling dynamic systems of tractors and optimizing energy management.

The competition for shared resources in printing manufacturing system may lead to deadlock, which could cause unnecessary downtime and bring vast economic loss for enterprises. Supervisor is widely studied in the literature to solve the deadlock control problem. With the objective of improving the robustness of reconfigurable printing manufacturing system (RPMS), this paper designs a new Agent- Resource- Workstation (ARW) model with a supervisor. A new reconfiguration of workstation models (NRWMs) is proposed based on ARW model. The supervisors can control reconfiguration behavior of NRWMs by limiting the time and frequency of interaction between workstation models. Then, formal verification is performed by process algebra to show its external behavior. We use the mCRL2 tool to carry out simulation experiments on NRWMs and provide the experimental results. The results show that robust deadlock control policy for the RPMS is secure and effective.

In order to reduce the influence of ground vibration generated by tractors during driving and operation on driving stability and driver ’s ride comfort, this paper proposes a tractor active seat suspension control method based on model predictive control (MPC). Through the establishment of three degrees of freedom tractor active seat suspension model, electromagnetic actuator (linear motor) model and ground excitation input model, the inner and outer loop control strategy of linear motor active seat suspension based on outer loop MPC control and inner loop current hysteresis comparison control is proposed. The influence of MPC parameters on the vibration performance and control cost of seat suspension is compared and analyzed through experiments. The appropriate system parameters are selected through optimization analysis. The simulation and experimental results show that compared with the passive seat suspension and PID seat suspension, the tractor active seat suspension based on model predictive control improves the ride comfort and vibration reduction characteristics of the tractor, and on this basis, it reduces the energy consumption required for the seat suspension system to achieve active control, while ensuring the timeliness of MPC control.