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Predictive operator modeling for virtual prototyping of hydraulic excavators

机译:液压挖掘机虚拟样机的预测操作员建模

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摘要

Stricter laws with regard to emission regulation and rising fuel costs have led to an increasing demand for highly efficient mobile machinery. Electronic components and hybrid propulsion systems can help to achieve these goals, but they also make the development of typical construction machinery such as hydraulic excavators a complex, expensive and time-consuming task. An improvement in the development process can be achieved through the usage of virtual prototypes. In this work, a physical simulation model of a 40-ton excavator is presented as an example for the virtual machine. However, virtual prototyping requires not only a detailed machine model, but also a driver model that computes realistic actuation signals during simulation. Therefore, a predictive driver model based on a state machine and decentralized model predictive controllers has been developed. The accelerated proximal gradient method is applied in the dual domain in order to solve the underlying constrained optimization problem. Results for the combined simulation of the virtual driver and the virtual excavator are presented. Particularly, the influence of different driver parameters is analyzed with regard to the resulting cycle time and fuel consumption.
机译:有关排放法规和更严格的燃料成本的更严格法律导致对高效移动机械的需求不断增长。电子元件和混合动力系统可以帮助实现这些目标,但它们也使诸如液压挖掘机之类的典型工程机械的开发成为一项复杂,昂贵且耗时的任务。通过使用虚拟原型可以改善开发过程。在这项工作中,以40吨挖掘机的物理仿真模型为例,给出了虚拟机的示例。但是,虚拟原型不仅需要详细的机器模型,而且还需要在仿真过程中计算实际驱动信号的驱动程序模型。因此,已经开发了基于状态机和分散模型预测控制器的预测驾驶员模型。为了解决潜在的约束优化问题,将加速近端梯度法应用于对偶域。给出了虚拟驱动程序和虚拟挖掘机组合仿真的结果。特别地,关于最终的循环时间和燃料消耗,分析了不同驾驶员参数的影响。

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