Carsim与Simulink联合仿真：基于Dugoff轮胎模型与卡尔曼滤波算法的车辆状态估计，涵盖横摆角速度、质心侧偏角等四状态及车轮转动角速度的分析与讨论 ,基于Dugoff轮胎模型的Carsim
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Carsim与Simulink联合仿真：基于Dugoff轮胎模型与卡尔曼滤波算法的车辆状态估计，涵盖横摆角速度、质心侧偏角等四状态及车轮转动角速度的分析与讨论。,基于Dugoff轮胎模型的Carsim与Simulink联合仿真：高精度车辆状态估计，涵盖横摆角速度等五大状态及卡尔曼滤波算法交流。,Carsim和simulink联合仿真车辆状态估计估计的状态为:横摆角速度，质心侧偏角，纵向车速，侧向车速，4个轮子的转动角速度先基于dugoff轮胎模型进行了轮胎纵向力和侧向力计算，再基于容积卡尔曼滤波CKF或者无迹卡尔曼滤波UKF 进行了车辆状态估计，精度很高，图中的工况为双移线工况和正弦工况提供模型任何细节的讲解，同时可以交流理论相关的部分，包括卡尔曼滤波算法。,Carsim; simulink联合仿真; 车辆状态估计; 横摆角速度; 质心侧偏角; 纵向车速; 侧向车速; 轮胎转动角速度; Dugoff轮胎模型; 容积卡尔曼滤波(CKF); 无迹卡尔曼滤波(UKF); 双移线工况; 正弦工况; 理论交流; 卡尔曼滤波算法。,Carsim与Simulink联合仿真：多状态估计与Du <link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/css/base.min.css" rel="stylesheet"/><link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/css/fancy.min.css" rel="stylesheet"/><link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/90426228/2/raw.css" rel="stylesheet"/><div id="sidebar" style="display: none"><div id="outline"></div></div><div class="pf w0 h0" data-page-no="1" id="pf1"><div class="pc pc1 w0 h0"><img alt="" class="bi x0 y0 w1 h1" src="/image.php?url=https://csdnimg.cn/release/download_crawler_static/90426228/bg1.jpg"/><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">### <span class="_ _0"> </span><span class="ff2">技术探索之旅：</span>Carsim<span class="_ _0"> </span><span class="ff2">与<span class="_ _1"> </span></span>Simulink<span class="_ _0"> </span><span class="ff2">联袂的车辆状态估计艺术</span></div><div class="t m0 x1 h2 y2 ff1 fs0 fc0 sc0 ls0 ws0">#### <span class="_ _0"> </span><span class="ff2">一、导言</span></div><div class="t m0 x1 h2 y3 ff2 fs0 fc0 sc0 ls0 ws0">在智能汽车的快速演进时代，<span class="_ _2"></span>精准的状态估计是确保车辆行为正确控制的核心一环。<span class="_ _2"></span>本文将</div><div class="t m0 x1 h2 y4 ff2 fs0 fc0 sc0 ls0 ws0">带你一探<span class="_ _1"> </span><span class="ff1">Carsim<span class="_ _0"> </span></span>与<span class="_ _1"> </span><span class="ff1">Simulink<span class="_ _0"> </span></span>联合仿真车辆状态估计的魅力，尤其关注于横摆角速度、质心</div><div class="t m0 x1 h2 y5 ff2 fs0 fc0 sc0 ls0 ws0">侧偏角、纵向车速、侧向车<span class="_ _3"></span>速以及四个轮子转动角速度<span class="_ _3"></span>的估计过程。我们将从<span class="_ _1"> </span><span class="ff1">Dugoff<span class="_"> </span></span>轮胎</div><div class="t m0 x1 h2 y6 ff2 fs0 fc0 sc0 ls0 ws0">模型出发，<span class="_ _3"></span>深入探讨<span class="_ _3"></span>容积卡尔曼<span class="_ _3"></span>滤波（<span class="ff1">CKF<span class="_ _3"></span></span>）和无迹卡<span class="_ _3"></span>尔曼滤波<span class="_ _3"></span>（<span class="ff1">UKF</span>）的车<span class="_ _3"></span>辆状态估<span class="_ _3"></span>计技</div><div class="t m0 x1 h2 y7 ff2 fs0 fc0 sc0 ls0 ws0">术。</div><div class="t m0 x1 h2 y8 ff1 fs0 fc0 sc0 ls0 ws0">#### <span class="_ _0"> </span><span class="ff2">二、</span>Dugoff<span class="_ _0"> </span><span class="ff2">轮胎模型：车辆动力的基石</span></div><div class="t m0 x1 h2 y9 ff1 fs0 fc0 sc0 ls0 ws0">Dugoff<span class="_"> </span><span class="ff2">轮胎模型是<span class="_ _3"></span>车辆<span class="_ _3"></span>动力学<span class="_ _3"></span>模拟的<span class="_ _3"></span>重要一<span class="_ _3"></span>环。它<span class="_ _3"></span>能够<span class="_ _3"></span>精确计<span class="_ _3"></span>算轮胎<span class="_ _3"></span>的纵向<span class="_ _3"></span>力和<span class="_ _3"></span>侧向力<span class="_ _3"></span>，</span></div><div class="t m0 x1 h2 ya ff2 fs0 fc0 sc0 ls0 ws0">为车辆的操控提供有力支持。<span class="_ _2"></span>该模型基于物理原理，<span class="_ _2"></span>详细描述了轮胎与地面之间的相互作用</div><div class="t m0 x1 h2 yb ff2 fs0 fc0 sc0 ls0 ws0">力，是车辆行为模拟的基础。</div><div class="t m0 x1 h2 yc ff1 fs0 fc0 sc0 ls0 ws0">#### <span class="_ _0"> </span><span class="ff2">三、</span>Simulink<span class="_ _0"> </span><span class="ff2">与<span class="_ _1"> </span></span>Carsim<span class="_ _0"> </span><span class="ff2">的联袂：双移线与正弦工况的仿真</span></div><div class="t m0 x1 h2 yd ff1 fs0 fc0 sc0 ls0 ws0">Simulink<span class="_"> </span><span class="ff2">和<span class="_ _0"> </span></span>Carsim<span class="_"> </span><span class="ff2">的联合<span class="_ _3"></span>仿真为<span class="_ _3"></span>车辆动<span class="_ _3"></span>力学研<span class="_ _3"></span>究提供<span class="_ _3"></span>了强大<span class="_ _3"></span>的工具<span class="_ _3"></span>。在双移<span class="_ _3"></span>线和正<span class="_ _3"></span>弦工况</span></div><div class="t m0 x1 h2 ye ff2 fs0 fc0 sc0 ls0 ws0">下，<span class="_ _4"></span>系统能够模拟真实的车辆行驶环境，<span class="_ _4"></span>通过传感器数据和模型预测，<span class="_ _4"></span>实现对车辆状态的精</div><div class="t m0 x1 h2 yf ff2 fs0 fc0 sc0 ls0 ws0">确估计。</div><div class="t m0 x1 h2 y10 ff1 fs0 fc0 sc0 ls0 ws0">#### <span class="_ _0"> </span><span class="ff2">四、卡尔曼滤波算法：状态估计的利器</span></div><div class="t m0 x1 h2 y11 ff2 fs0 fc0 sc0 ls0 ws0">卡尔曼滤波算法是一种高效的递归滤波器，<span class="_ _2"></span>能够在动态系统中估计状态变量。<span class="_ _2"></span>本文将重点介</div><div class="t m0 x1 h2 y12 ff2 fs0 fc0 sc0 ls0 ws0">绍容积卡尔<span class="_ _3"></span>曼滤波（<span class="_ _3"></span><span class="ff1">CKF</span>）和无迹<span class="_ _3"></span>卡尔曼滤<span class="_ _3"></span>波（<span class="ff1">UKF</span>）在<span class="_ _3"></span>车辆状态<span class="_ _3"></span>估计中的应<span class="_ _3"></span>用。这两<span class="_ _3"></span>种算</div><div class="t m0 x1 h2 y13 ff2 fs0 fc0 sc0 ls0 ws0">法通过引入更多的状态变量和更精确的更新机制，大大提高了状态估计的精度。</div><div class="t m0 x1 h2 y14 ff1 fs0 fc0 sc0 ls0 ws0">**<span class="ff2">容积卡尔曼滤波（</span>CKF<span class="ff2">）</span>**</div><div class="t m0 x1 h2 y15 ff1 fs0 fc0 sc0 ls0 ws0">CKF<span class="_ _0"> </span><span class="ff2">通过使用容积积分规则来逼近概率密度函数，<span class="_ _4"></span>从而得到更加准确的状态估计值。<span class="_ _5"></span>在车辆</span></div><div class="t m0 x1 h2 y16 ff2 fs0 fc0 sc0 ls0 ws0">状态估计中，<span class="ff1">CKF<span class="_ _0"> </span></span>能够有效地处理非线性系统的问题，提高估计的精度。</div><div class="t m0 x1 h2 y17 ff1 fs0 fc0 sc0 ls0 ws0">**<span class="ff2">无迹卡尔曼滤波（</span>UKF<span class="ff2">）</span>**</div><div class="t m0 x1 h2 y18 ff1 fs0 fc0 sc0 ls0 ws0">UKF<span class="_ _0"> </span><span class="ff2">利用无迹变换来逼近概率密度函数，<span class="_ _5"></span>其优点在于能够处理高维度的非线性系统。<span class="_ _5"></span>在车辆</span></div><div class="t m0 x1 h2 y19 ff2 fs0 fc0 sc0 ls0 ws0">状态估计中，<span class="ff1">UKF<span class="_ _0"> </span></span>能够处理更复杂的动力学模型，进一步提高估计的准确性。</div><div class="t m0 x1 h2 y1a ff1 fs0 fc0 sc0 ls0 ws0">#### <span class="_ _0"> </span><span class="ff2">五、实例分析：联合仿真下的车辆状态估计</span></div><div class="t m0 x1 h2 y1b ff2 fs0 fc0 sc0 ls0 ws0">在联合仿真环境下，我们首<span class="_ _3"></span>先基于<span class="_ _1"> </span><span class="ff1">Dugoff<span class="_ _0"> </span></span>轮胎模型计算轮<span class="_ _3"></span>胎的纵向力和侧向力。然后<span class="_ _3"></span>，利</div><div class="t m0 x1 h2 y1c ff2 fs0 fc0 sc0 ls0 ws0">用<span class="_ _1"> </span><span class="ff1">CKF<span class="_ _0"> </span></span>或<span class="_ _1"> </span><span class="ff1">UKF<span class="_ _0"> </span></span>算<span class="_ _3"></span>法对车辆<span class="_ _3"></span>的状态进行<span class="_ _3"></span>估计。通<span class="_ _3"></span>过实际仿<span class="_ _3"></span>真数据的对<span class="_ _3"></span>比，我们<span class="_ _3"></span>可以看到，<span class="_ _3"></span>这</div><div class="t m0 x1 h2 y1d ff2 fs0 fc0 sc0 ls0 ws0">两种卡尔曼滤波算法在车辆状态估计中均表现出色，<span class="_ _2"></span>尤其是在双移线和正弦工况下，<span class="_ _2"></span>估计的</div><div class="t m0 x1 h2 y1e ff2 fs0 fc0 sc0 ls0 ws0">横摆角速度、<span class="_ _4"></span>质心侧偏角、<span class="_ _4"></span>纵向车速、<span class="_ _4"></span>侧向车速以及四个轮子的转动角速度等关键参数均具</div></div><div class="pi" data-data='{"ctm":[1.611830,0.000000,0.000000,1.611830,0.000000,0.000000]}'></div></div>