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The Role of a Novel Discrete-Time MRAC Based Motion Cueing on Loss of Control at a Hexapod Driving Simulator

机译:新型基于离散MRAC的运动提示在六足驾驶模拟器上失控的作用

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The objective of this paper is to present the advantages of Model reference adaptive control (MRAC) motion cueing algorithm against the classical motion cueing algorithm in terms of biomechanical reactions of the participants during the critical maneuvers like chicane in driving simulator real-time. This study proposes a method and an experimental validation to analyze the vestibular and neuromuscular dynamics responses of the drivers with respect to the type of the control used at the hexapod driving simulator. For each situation, the EMG (electromyography) data were registered from arm muscles of the drivers (flexor carpi radialis, brachioradialis). In addition, the roll velocity perception thresholds (RVT) and roll velocities (RV) were computed from the real-time vestibular level measurements from the drivers via a motion-tracking sensor. In order to process the data of the EMG and RVT, Pearson’s correlation and a two-way ANOVA with a significance level of 0.05 were assigned. Moreover, the relationships of arm muscle power and roll velocity with vehicle CG (center of gravity) lateral displacement were analyzed in order to assess the agility/alertness level of the drivers as well as the vehicle loss of control characteristics with a confidence interval of 95%. The results showed that the MRAC algorithm avoided the loss of adhesion, loss of control (LOA, LOC) more reasonably compared to the classical motion cueing algorithm. According to our findings, the LOA avoidance decreased the neuromuscular-visual cues level conflict with MRAC algorithm. It also revealed that the neuromuscular-vehicle dynamics conflict has influence on visuo-vestibular conflict; however, the visuo-vestibular cue conflict does not influence the neuromuscular-vehicle dynamics interactions.
机译:本文的目的是就驾驶模拟器实时执行像弯道这样的关键操纵过程中参与者的生物力学反应,提出模型参考自适应控制(MRAC)运动提示算法相对于经典运动提示算法的优势。这项研究提出了一种方法和实验验证来分析驾驶员的前庭和神经肌肉动力响应,与六脚架驾驶模拟器所用控件的类型有关。对于每种情况,从驾驶员的手臂肌肉(radial腕腕,、腕radi肌)记录肌电图(肌电图)数据。另外,通过运动跟踪传感器从驾驶员的实时前庭水平测量值计算出侧倾速度感知阈值(RVT)和侧倾速度(RV)。为了处理EMG和RVT的数据,分配了Pearson相关性和显着性水平为0.05的双向ANOVA。此外,分析了手臂肌肉力量和侧倾速度与车辆CG(重心)横向位移之间的关系,以评估驾驶员的敏捷性/敏捷性水平以及车辆的控制特性损失(置信区间为95) %。结果表明,与传统的运动提示算法相比,MRAC算法更合理地避免了附着力损失,控制损失(LOA,LOC)。根据我们的发现,避免LOA与MRAC算法减少了神经肌肉视觉提示级别的冲突。研究还表明,神经肌肉与车辆动力学的冲突对视觉-血管的冲突有影响。然而,视觉-前庭提示冲突不影响神经肌肉-车辆动力学相互作用。

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