[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2616":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"view_count":32,"doi":33,"paper":34,"created_at":63},2616,"Multi-agent cooperative control for unmanned distributed-drive electric agricultural vehicle in paddy fields: tracking, stability, and energy-aware torque allocation","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112422","With the rapid advancement of intelligent agriculture and autonomous field operations, the distributed drive electric plant protection vehicle (DDEPPV) is increasingly adopted for paddy-field plant protection. However, in soft-soil, low-adhesion, and highly disturbed environments, the tight coupling among path tracking, drive\u002Fyaw stability, and energy consumption-together with uncertain ground parameters-poses major challenges to conventional control. In addition, from-scratch reinforcement learning is difficult to deploy, as early exploration can induce yaw instability and wheel entrapment. To overcome these limitations, we propose a vehicle-level distributed electric-drive control framework that integrates physics-informed priors with multi-agent cooperative learning. A mud-water multiphase wheel-soil interaction model is built via CFD-DEM coupling to identify, under the parameter settings and operating conditions considered in this study, an energy- and sinkage-risk-aware slip-ratio window, thereby providing an interpretable ground-mechanics boundary for subsequent controller design. Under the centralized training and decentralized execution paradigm, the task is decomposed into three agents for path tracking, stability\u002Ftraction regulation, and energy-optimal four-wheel allocation, and trained using model predictive control (MPC) expert-supervised pretraining followed by multi-agent twin delayed deep deterministic policy gradient (MATD3) cooperative fine-tuning. Real-time Hardware-in-the-Loop (HIL) experiments verify improved turning performance and enhanced yaw\u002Ftraction stability, while reducing traction-system electrical energy consumption by 29.4% versus MPC and by an additional 5.4% over unpretrained MATD3, demonstrating unified optimization of accuracy-stability-energy efficiency in paddy fields.","随着智能农业与自主田间作业的快速发展，分布式驱动电动植保车辆（DDEPPV）在水稻田植保作业中得到日益广泛的应用。然而，在软土、低附着力和高扰动环境中，路径跟踪、驱动\u002F偏航稳定性与能耗之间的紧密耦合，加之地面参数的不确定性，给传统控制带来了重大挑战。此外，从零开始的强化学习难以部署，因为早期探索可能引发偏航失稳和车轮陷坑。为克服这些局限，我们提出了一种车辆级分布式电驱动控制框架，将物理信息先验与多智能体协同学习相融合。通过CFD-DEM耦合建立了泥水多相轮-土相互作用模型，在本研究所考虑的参数设置和作业条件下识别出兼顾能耗与下陷风险的滑转率窗口，从而为后续控制器设计提供可解释的地面力学边界。在集中训练-分散执行范式下，将任务分解为路径跟踪、稳定性\u002F牵引力调节和能耗最优四轮分配三个智能体，并采用模型预测控制（MPC）专家监督预训练，随后通过多智能体双延迟深度确定性策略梯度（MATD3）进行协同微调。实时硬件在环（HIL）实验验证了转向性能的改善以及偏航\u002F牵引稳定性的增强，同时牵引系统电能消耗较MPC降低29.4%，较未经预训练的MATD3进一步降低5.4%，展示了水稻田中精度-稳定性-能效的统一优化。",null,"Computers and Electronics in Agriculture","2026-09-15T00:00:00Z","论文",10,false,82,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,22,19,14,9,1,"提出物理先验与多智能体协同学习融合的分布式驱动电动农机控制框架，HIL实验验证能耗降低29.4%，方法新颖、数据扎实，对水田智能装备研发有实质参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","智能农机","水稻生产","多智能体控制",0,"10.1016\u002Fj.compag.2026.112422",{"doi":33,"openalex_id":35,"authors":36,"venue":10,"cited_by_count":32,"oa_url":6,"card":56,"direction":60,"ingested_from":62},"W7213225972",[37,40,42,44,47,49,51,53],{"name":38,"orcid":39},"Wenxiang Xu","https:\u002F\u002Forcid.org\u002F0000-0001-6476-4710",{"name":41,"orcid":9},"Xiaoyu Song",{"name":43,"orcid":9},"Liling Ye",{"name":45,"orcid":46},"Mengnan Liu","https:\u002F\u002Forcid.org\u002F0000-0001-5418-6347",{"name":48,"orcid":9},"He Zheng",{"name":50,"orcid":9},"Mingfeng Wang",{"name":52,"orcid":9},"Ze Liu",{"name":54,"orcid":55},"Maohua Xiao","https:\u002F\u002Forcid.org\u002F0000-0001-5213-1035",{"tldr":57,"method":58,"finding":59,"direction":60,"opportunity":61},"提出多智能体协同控制框架，实现水田分布式驱动电动农机路径跟踪、稳定性与能耗统一优化。","CFD-DEM泥水轮土模型、MPC专家预训练、MATD3多智能体协同微调、HIL","相比MPC降低牵引电耗29.4%，比未预训练MATD3再降5.4%，提升转向与横摆稳定性。","农业人工智能与决策模型","可探索将物理先验与多智能体强化学习迁移至其他软土农田作业场景，并降低对高保真仿真模型的依赖。","openalex","2026-09-16T23:30:01.996916Z"]