发表机构
Virginia Polytechnic Institute and State University; College of Engineering, Virginia Commonwealth University（弗吉尼亚理工大学; 弗吉尼亚联邦大学工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对车辆遥操作的通信延迟与环境干扰问题，提出集成WV与RBFN的单向框架，经仿真与硬件在环验证，其鲁棒性与效率优于PID等控制器，适配车辆边缘计算。
AI 中文摘要
车辆直接遥操作面临关键技术瓶颈：通信延迟，以及操作者无法物理感知未建模的环境干扰（如空气阻力、坡度角），再加上高度非线性的轮胎-路面动力学。为应对这些挑战，本文提出一种定制化的单向遥操作框架。该系统集成波变量（WV）方法，以被动地保证随机延迟下的稳定性；同时采用自适应径向基函数网络（RBFN），主动补偿车辆特有的不确定性。与现有为双边机械臂设计的WV-神经网络架构不同，本框架具备专为车辆纵向和横向动力学设计的解耦自适应律。此外，与依赖大量模型的预测控制器相比，无模型的RBFN可实现快速在线自适应，且无需高昂的计算开销。基于前期理论构建，本文完成全面对比分析与实际硬件验证。针对PID、LQR、MPC和NMPC的仿真基准测试显示，RBFN在应对未建模干扰时具备更优鲁棒性，且执行时间比MPC和NMPC少几个数量级，适合资源受限的车辆边缘计算。最后，采用1/10比例车辆在4G网络下的硬件在环实验，验证了该系统在物理路面不确定性下的实用性、安全性和鲁棒轨迹跟踪能力。
英文摘要
Direct teleoperation of vehicles faces critical technical bottlenecks: communication latency and the operator's inability to physically perceive unmodeled environmental disturbances (e.g., aerodynamic drag, bank angles) coupled with highly nonlinear tire-road dynamics. To address these challenges, we propose a tailored unilateral teleoperation framework. The system integrates the Wave Variable (WV) approach to passively guarantee stability under stochastic delays, and an adaptive Radial Basis Function Network (RBFN) to actively compensate for vehicle-specific uncertainties. Unlike existing WV-neural network architectures designed for bilateral robotic arms, our framework features decoupled adaptive laws specifically designed for vehicle longitudinal and lateral dynamics. Furthermore, compared to model-heavy predictive controllers, the model-free RBFN offers rapid online adaptation without heavy computational overhead. Building upon our preliminary theoretical formulation, this brief paper presents comprehensive comparative analyses and real-world hardware validations. Simulation benchmarks against PID, LQR, MPC, and NMPC demonstrate that the RBFN achieves superior robustness against unmodeled disturbances while requiring orders of magnitude less execution time than MPC and NMPC, making it ideal for resource-constrained vehicle edge computing. Finally, hardware-in-the-loop experiments using a 1/10th scale vehicle over a 4G network validate the system's practical feasibility, safety, and robust trajectory tracking under physical road uncertainties.
