用神经网络增强电机模型，实现重型机械臂高精度实时控制。
Surrogate-Enhanced Modeling and Adaptive Modular Control of All-Electric Heavy-Duty Robotic Manipulators
- 融合神经网络与物理模型的代理驱动方法，提升电机建模精度。
- 在仿真中实现亚厘米级末端轨迹跟踪，实验验证负载下控制有效。
- 适合需高精度、模块化控制的重型电动机械臂研发与部署。
本文提出一种面向全电动重型机械臂（HDRM）的统一系统级建模与控制框架，该机械臂由机电直线执行器（EMLA）驱动。通过集成在专用测试平台训练的神经网络，构建了融合机电动态特性的代理增强型执行器模型，并嵌入改进的虚拟分解控制（VDC）架构，引入自然自适应律。推导出的解析式HDRM模型支持分层控制结构，可将高层力与速度目标无缝映射为实时执行器指令，附带基于李雅普诺夫的稳定性证明。在立方体及自定义平面三角轨迹的多域仿真中，所提自适应模块化控制器实现了亚厘米级笛卡尔空间跟踪精度。在真实负载模拟条件下对1-自由度平台的实验验证了该控制策略的有效性。结果表明，将代理增强型EMLA模型嵌入VDC方法，可实现全电动HDRM的模块化、实时控制，支持其在下一代移动作业装备中的部署。
原文摘要 · Abstract (English)
This paper presents a unified system-level modeling and control framework for an all-electric heavy-duty robotic manipulator (HDRM) driven by electromechanical linear actuators (EMLAs). A surrogate-enhanced actuator model, combining integrated electromechanical dynamics with a neural network trained on a dedicated testbed, is integrated into an extended virtual decomposition control (VDC) architecture augmented by a natural adaptation law. The derived analytical HDRM model supports a hierarchical control structure that seamlessly maps high-level force and velocity objectives to real-time actuator commands, accompanied by a Lyapunov-based stability proof. In multi-domain simulations of both cubic and a custom planar triangular trajectory, the proposed adaptive modular controller achieves sub-centimeter Cartesian tracking accuracy. Experimental validation of the same 1-DoF platform under realistic load emulation confirms the efficacy of the proposed control strategy. These findings demonstrate that a surrogate-enhanced EMLA model embedded in the VDC approach can enable modular, real-time control of an all-electric HDRM, supporting its deployment in next-generation mobile working machines.
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