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MODEL PREDICTIVE MOTION CUEING ALGORITHM FOR A TRUCK SIMULATOR

机译:卡车模拟器的模型预测运动提示算法

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The paper looks at the possibilities of applying a model predictive control (MPC) algorithm to control and optimize the motion cueing algorithm (MCA) employed in state-of-the-art simulators for the purposes of reproducing motion effects (in particular acceleration). Typically, MCAs feature a large number of parameters which need to be configured with respect to both the kinematic and dynamic limitations of the motion platform used and the human motion perception thresholds. When an MPC algorithm is employed, these constraints can be fully respected and the ultimate results can, at least in a sense, be considered optimal. The paper shows the possibilities of interlinking MPC with MCA to dynamically adjust the applicable constraints. In addition, all theoretical results will be verified by means of simulations. Since the research described in this paper has been conducted as part of an advanced truck simulator project pursued by the author the parameters chosen for all simulations respect the properties of the motion platform employed in this project.
机译:本文探讨了应用模型预测控制(MPC)算法来控制和优化最新模拟器中采用的运动提示算法(MCA)的可能性,以再现运动效果(尤其是加速度)。通常,MCA具有大量参数,需要针对所用运动平台的运动和动态限制以及人类运动感知阈值进行配置。当采用MPC算法时,可以完全遵守这些约束条件,并且至少在某种意义上可以将最终结果视为最佳。本文显示了将MPC与MCA互连以动态调整适用约束的可能性。此外,所有理论结果将通过仿真进行验证。由于本文所述的研究已作为作者进行的高级卡车模拟器项目的一部分而进行,因此为所有模拟选择的参数都尊重该项目中使用的运动平台的特性。

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