A Study on the Speed Decision Control of Agricultural Vehicles in a Collaborative Multi-Machine Operation Scenario

The driving speed of autonomous agricultural vehicles is influenced by surrounding cooperative vehicles during cooperative operations, leading to challenges in simultaneously optimizing operational efficiency, energy consumption, safety, and driving smoothness. This bottleneck hinders the development of autonomous cooperative systems. To address this, we propose a hierarchical speed decision control framework. The speed decision layer employs a maximum entropy-constrained proximal policy optimization (DMEPPO) reinforcement learning method, incorporating operational efficiency, energy consumption, safety, and smoothness as reward metrics to determine the optimal speed target. The speed control layer utilizes a Linear Matrix Inequality (LMI)-based robust control method for precise speed tracking. The experimental results demonstrate that the proposed DMEPPO achieved convergence after 2000 iterations and better learning performance, while the LMI-based controller achieved robust and responsive tracking. This architecture provides a theoretical foundation for speed decision control in agricultural vehicle cooperation scenarios. By considering aspects of speed decision-making control such as energy conservation, good solutions can be provided for the sustainable development of agriculture.