基于simulink的拓展卡尔曼滤波的估计路面附着系数估算，ekf算法基于matlab内含道夫轮胎模型，七自由度车辆模型，非c

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基于simulink的拓展卡尔曼滤波的估计路面附着系数估算，ekf算法基于matlab 内含道夫轮胎模型，七自由度车辆模型，非carsim联合仿真，运行结果如下各个输出收敛，效果不错
<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/89759297/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/89759297/bg1.jpg"/><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">基于<span class="_ _0"> </span><span class="ff2">Simulink<span class="_ _1"> </span></span>的拓展卡尔曼滤波的估计路面附着系数估算<span class="ff3">，<span class="ff2">EKF<span class="_ _1"> </span></span></span>算法基于<span class="_ _0"> </span><span class="ff2">MATLAB</span></div><div class="t m0 x1 h2 y2 ff1 fs0 fc0 sc0 ls0 ws0">内含道夫轮胎模型<span class="ff3">，</span>七自由度车辆模型<span class="ff3">，</span>非<span class="_ _0"> </span><span class="ff2">CarSim<span class="_ _1"> </span></span>联合仿真<span class="ff3">，</span>运行结果如下各个输出收敛<span class="ff3">，</span>效果不</div><div class="t m0 x1 h2 y3 ff1 fs0 fc0 sc0 ls0 ws0">错</div><div class="t m0 x1 h2 y4 ff1 fs0 fc0 sc0 ls0 ws0">摘要<span class="ff3">：</span></div><div class="t m0 x1 h2 y5 ff1 fs0 fc0 sc0 ls0 ws0">本文基于<span class="_ _0"> </span><span class="ff2">Simulink<span class="_ _1"> </span></span>平台<span class="ff3">，</span>提出了一种基于拓展卡尔曼滤波<span class="ff3">（<span class="ff2">EKF</span>）</span>的路面附着系数估算方法<span class="ff3">，</span>以实</div><div class="t m0 x1 h2 y6 ff1 fs0 fc0 sc0 ls0 ws0">现对车辆运动过程中路面附着系数的实时估计<span class="ff4">。</span>方法采用<span class="_ _0"> </span><span class="ff2">MATLAB<span class="_ _1"> </span></span>实现<span class="ff3">，</span>包括道夫轮胎模型和七自由</div><div class="t m0 x1 h2 y7 ff1 fs0 fc0 sc0 ls0 ws0">度车辆模型<span class="ff3">，</span>并与非<span class="_ _0"> </span><span class="ff2">CarSim<span class="_ _1"> </span></span>联合仿真验证<span class="ff4">。</span>实验结果显示<span class="ff3">，</span>各个输出参数均收敛<span class="ff3">，</span>表明所提方法能</div><div class="t m0 x1 h2 y8 ff1 fs0 fc0 sc0 ls0 ws0">够有效估算路面附着系数<span class="ff3">，</span>并获得了良好的估计效果<span class="ff4">。</span></div><div class="t m0 x1 h2 y9 ff2 fs0 fc0 sc0 ls0 ws0">1.<span class="_ _2"> </span><span class="ff1">引言</span></div><div class="t m0 x1 h2 ya ff1 fs0 fc0 sc0 ls0 ws0">在汽车控制系统中<span class="ff3">，</span>准确估计车辆所行驶路面的附着系数对提高车辆的稳定性和安全性至关重要<span class="ff4">。</span>而</div><div class="t m0 x1 h2 yb ff1 fs0 fc0 sc0 ls0 ws0">针对路面附着系数的估算方法中<span class="ff3">，</span>卡尔曼滤波被广泛应用且具有较好的效果<span class="ff4">。</span>本文旨在通过拓展卡尔</div><div class="t m0 x1 h2 yc ff1 fs0 fc0 sc0 ls0 ws0">曼滤波算法<span class="ff3">，</span>实现对路面附着系数的实时估算<span class="ff4">。</span></div><div class="t m0 x1 h2 yd ff2 fs0 fc0 sc0 ls0 ws0">2.<span class="_ _2"> </span><span class="ff1">理论基础</span></div><div class="t m0 x1 h2 ye ff2 fs0 fc0 sc0 ls0 ws0">2.1.<span class="_"> </span><span class="ff1">拓展卡尔曼滤波</span></div><div class="t m0 x1 h2 yf ff1 fs0 fc0 sc0 ls0 ws0">拓展卡尔曼滤波是一种在非线性系统中应用的卡尔曼滤波扩展方法<span class="ff4">。</span>其核心思想是通过线性近似和状</div><div class="t m0 x1 h2 y10 ff1 fs0 fc0 sc0 ls0 ws0">态误差协方差矩阵的更新<span class="ff3">，</span>对非线性系统进行状态估计<span class="ff4">。</span>在本文中<span class="ff3">，</span>我们将利用拓展卡尔曼滤波来估</div><div class="t m0 x1 h2 y11 ff1 fs0 fc0 sc0 ls0 ws0">计车辆行驶过程中的路面附着系数<span class="ff4">。</span></div><div class="t m0 x1 h2 y12 ff2 fs0 fc0 sc0 ls0 ws0">2.2.<span class="_"> </span><span class="ff1">道夫轮胎模型</span></div><div class="t m0 x1 h2 y13 ff1 fs0 fc0 sc0 ls0 ws0">道夫轮胎模型是一种用于描述轮胎行为的模型<span class="ff3">，</span>它考虑了轮胎与路面之间的力学相互作用<span class="ff4">。</span>本文中<span class="ff3">，</span></div><div class="t m0 x1 h2 y14 ff1 fs0 fc0 sc0 ls0 ws0">我们将在拓展卡尔曼滤波框架下<span class="ff3">，</span>应用道夫轮胎模型来建立车辆的状态方程<span class="ff4">。</span></div><div class="t m0 x1 h2 y15 ff2 fs0 fc0 sc0 ls0 ws0">2.3.<span class="_"> </span><span class="ff1">七自由度车辆模型</span></div><div class="t m0 x1 h2 y16 ff1 fs0 fc0 sc0 ls0 ws0">七自由度车辆模型是一种常用的描述车辆运动的模型<span class="ff3">，</span>它包括车辆的坐标系<span class="ff4">、</span>质心位置<span class="ff4">、</span>车辆速度<span class="ff4">、</span></div><div class="t m0 x1 h2 y17 ff1 fs0 fc0 sc0 ls0 ws0">陀螺仪测量等关键参数<span class="ff4">。</span>本文中<span class="ff3">，</span>我们将基于七自由度车辆模型<span class="ff3">，</span>建立车辆的观测方程<span class="ff4">。</span></div><div class="t m0 x1 h2 y18 ff2 fs0 fc0 sc0 ls0 ws0">3.<span class="_ _2"> </span><span class="ff1">实验设计</span></div><div class="t m0 x1 h2 y19 ff1 fs0 fc0 sc0 ls0 ws0">为验证所提方法的有效性<span class="ff3">，</span>我们在<span class="_ _0"> </span><span class="ff2">MATLAB<span class="_ _1"> </span></span>中搭建了<span class="_ _0"> </span><span class="ff2">Simulink<span class="_ _1"> </span></span>模型<span class="ff3">，</span>并进行非<span class="_ _0"> </span><span class="ff2">CarSim<span class="_ _1"> </span></span>联合仿真</div><div class="t m0 x1 h2 y1a ff4 fs0 fc0 sc0 ls0 ws0">。<span class="ff1">实验设置包括车辆行驶过程中的路面附着系数变化</span>、<span class="ff1">传感器测量误差等</span>。</div><div class="t m0 x1 h2 y1b ff2 fs0 fc0 sc0 ls0 ws0">4.<span class="_ _2"> </span><span class="ff1">实验结果与讨论</span></div><div class="t m0 x1 h2 y1c ff1 fs0 fc0 sc0 ls0 ws0">通过实验<span class="ff3">，</span>我们得到了车辆行驶过程中路面附着系数的估计结果<span class="ff3">，</span>并与真值进行对比<span class="ff4">。</span>实验结果显示</div><div class="t m0 x1 h2 y1d ff3 fs0 fc0 sc0 ls0 ws0">，<span class="ff1">各个输出参数均收敛</span>，<span class="ff1">并且与真值非常接近</span>，<span class="ff1">表明所提方法在实际应用中具有良好的估计效果<span class="ff4">。</span></span></div><div class="t m0 x1 h2 y1e ff2 fs0 fc0 sc0 ls0 ws0">5.<span class="_ _2"> </span><span class="ff1">总结与展望</span></div></div><div class="pi" data-data='{"ctm":[1.568627,0.000000,0.000000,1.568627,0.000000,0.000000]}'></div></div>
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