基于无迹卡尔曼滤波的质心侧偏角估计算法设计及其与Trucksim联合仿真验证 ,基于无迹卡尔曼滤波的质心侧偏角估计算法设计及其与Trucksim联合仿真验证 ,基于无迹卡尔曼滤波的质心侧偏角估计算法s
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基于无迹卡尔曼滤波的质心侧偏角估计算法设计及其与Trucksim联合仿真验证。,基于无迹卡尔曼滤波的质心侧偏角估计算法设计及其与Trucksim联合仿真验证。,基于无迹卡尔曼滤波的质心侧偏角估计算法simulink模型设计，通过加速度计和陀螺仪分别对车辆横向加速度和横摆角速度进行测量，以车辆横向速度与横摆角速度作为系统状态量，以车辆横向加速度和横摆角速度作为量测量。根据UT变得到Sigma采样点集，根据时间更新得到的下一时刻的先验估计值。基于所搭建的估计算法与trucksim联合仿真在双移线与正弦工况下，得到结果验证算法。注意，trucksim工程需要手动配置，这里给出了具体接口1、前左轮转角2、前右轮转角3、纵向车速4、实际侧偏角5、横摆角速度6、一轴左侧车轮纵向力Fx17、一轴右侧车轮纵向力Fx28、二轴左侧车轮纵向力Fx39、二轴右侧车轮纵向力Fx410、三轴左侧车轮纵向力Fx511、三轴左侧车轮纵向力Fx612、一轴左侧车轮侧向力Fy113、一轴右侧车轮侧向力Fy214、二轴左侧车轮侧向力Fy115、二轴右侧车轮侧向力Fy216、三轴左侧车 <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/90400421/2/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/90400421/bg1.jpg"/><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">基于无迹卡尔曼滤波的质心侧偏角估计算法<span class="_ _0"> </span><span class="ff2">Simulink<span class="_ _1"> </span></span>模型设计</div><div class="t m0 x1 h2 y2 ff1 fs0 fc0 sc0 ls0 ws0">摘要<span class="ff3">：</span>本文基于无迹卡尔曼滤波<span class="ff3">（<span class="ff2">Unscented Kalman Filter, UKF</span>）</span>算法<span class="ff3">，</span>通过加速度计和陀</div><div class="t m0 x1 h2 y3 ff1 fs0 fc0 sc0 ls0 ws0">螺仪对车辆的横向加速度和横摆角速度进行测量<span class="ff3">，</span>实现对车辆质心侧偏角的估计<span class="ff4">。</span>首先<span class="ff3">，</span>介绍了无迹</div><div class="t m0 x1 h2 y4 ff1 fs0 fc0 sc0 ls0 ws0">卡尔曼滤波算法的原理<span class="ff3">，</span>并给出<span class="_ _0"> </span><span class="ff2">Simulink<span class="_ _1"> </span></span>模型的设计思路<span class="ff4">。</span>然后<span class="ff3">，</span>通过与<span class="_ _0"> </span><span class="ff2">TruckSim<span class="_ _1"> </span></span>联合仿真<span class="ff3">，</span></div><div class="t m0 x1 h2 y5 ff1 fs0 fc0 sc0 ls0 ws0">在双移线与正弦工况下验证了所搭建的估计算法的性能<span class="ff4">。</span>最后<span class="ff3">，</span>给出了<span class="_ _0"> </span><span class="ff2">TruckSim<span class="_ _1"> </span></span>工程中需要手动配</div><div class="t m0 x1 h2 y6 ff1 fs0 fc0 sc0 ls0 ws0">置的具体接口参数列表<span class="ff3">，</span>并提供了相关仿真结果<span class="ff4">。</span>本文的研究成果对于车辆动力学的研究和控制具有</div><div class="t m0 x1 h2 y7 ff1 fs0 fc0 sc0 ls0 ws0">一定的指导意义<span class="ff4">。</span></div><div class="t m0 x1 h2 y8 ff1 fs0 fc0 sc0 ls0 ws0">关键词<span class="ff3">：</span>无迹卡尔曼滤波<span class="ff4">、</span>质心侧偏角估计<span class="ff4">、<span class="ff2">Simulink<span class="_ _1"> </span></span></span>模型<span class="ff4">、<span class="ff2">TruckSim<span class="_ _1"> </span></span></span>联合仿真<span class="ff4">、</span>车辆动力学</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">在车辆动力学研究中<span class="ff3">，</span>准确估计车辆质心的侧偏角对于车辆的稳定性控制以及驾驶员的安全至关重要</div><div class="t m0 x1 h2 yb ff4 fs0 fc0 sc0 ls0 ws0">。<span class="ff1">为了实现对车辆侧偏角的准确估计<span class="ff3">，</span>本文采用了无迹卡尔曼滤波算法<span class="ff3">，</span>并基于<span class="_ _0"> </span><span class="ff2">Simulink<span class="_ _1"> </span></span>平台进行</span></div><div class="t m0 x1 h2 yc ff1 fs0 fc0 sc0 ls0 ws0">了模型设计<span class="ff4">。</span>通过与<span class="_ _0"> </span><span class="ff2">TruckSim<span class="_ _1"> </span></span>软件联合仿真<span class="ff3">，</span>验证了所搭建的估计算法的性能<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">无迹卡尔曼滤波原理</span></div><div class="t m0 x1 h2 ye ff1 fs0 fc0 sc0 ls0 ws0">无迹卡尔曼滤波是一种非线性滤波算法<span class="ff3">，</span>在状态估计中被广泛应用<span class="ff4">。</span>本文将该算法应用于车辆动力学</div><div class="t m0 x1 h2 yf ff1 fs0 fc0 sc0 ls0 ws0">模型中<span class="ff3">，</span>通过测量车辆的横向加速度和横摆角速度<span class="ff3">，</span>估计车辆质心的侧偏角<span class="ff4">。</span>具体步骤如下<span class="ff3">：</span></div><div class="t m0 x1 h2 y10 ff2 fs0 fc0 sc0 ls0 ws0">-<span class="_ _2"> </span><span class="ff1">步骤<span class="_ _0"> </span></span>1<span class="ff3">：<span class="ff1">系统建模<span class="ff4">。</span>根据车辆动力学方程</span>，<span class="ff1">建立车辆的状态空间模型</span>，<span class="ff1">将车辆侧偏角和横摆角速</span></span></div><div class="t m0 x2 h2 y11 ff1 fs0 fc0 sc0 ls0 ws0">度作为系统状态量<span class="ff3">，</span>车辆横向加速度和横摆角速度作为量测量<span class="ff4">。</span></div><div class="t m0 x1 h2 y12 ff2 fs0 fc0 sc0 ls0 ws0">-<span class="_ _2"> </span><span class="ff1">步骤<span class="_ _0"> </span></span>2<span class="ff3">：</span>UT<span class="_ _1"> </span><span class="ff1">变换<span class="ff4">。</span>根据<span class="_ _0"> </span></span>UKF<span class="_ _1"> </span><span class="ff1">的原理<span class="ff3">，</span>通过对系统状态量进行<span class="_ _0"> </span></span>UT<span class="_ _1"> </span><span class="ff1">变换<span class="ff3">，</span>得到<span class="_ _0"> </span></span>Sigma<span class="_ _1"> </span><span class="ff1">采样点集<span class="ff3">，</span></span></div><div class="t m0 x2 h2 y13 ff1 fs0 fc0 sc0 ls0 ws0">用于状态估计<span class="ff4">。</span></div><div class="t m0 x1 h2 y14 ff2 fs0 fc0 sc0 ls0 ws0">-<span class="_ _2"> </span><span class="ff1">步骤<span class="_ _0"> </span></span>3<span class="ff3">：<span class="ff1">时间更新<span class="ff4">。</span>根据<span class="_ _0"> </span></span></span>UT<span class="_ _1"> </span><span class="ff1">变换得到的<span class="_ _0"> </span></span>Sigma<span class="_ _1"> </span><span class="ff1">采样点集<span class="ff3">，</span>通过时间更新公式<span class="ff3">，</span>得到下一时刻的</span></div><div class="t m0 x2 h2 y15 ff1 fs0 fc0 sc0 ls0 ws0">先验估计值<span class="ff4">。</span></div><div class="t m0 x1 h2 y16 ff2 fs0 fc0 sc0 ls0 ws0">-<span class="_ _2"> </span><span class="ff1">步骤<span class="_ _0"> </span></span>4<span class="ff3">：<span class="ff1">量测更新<span class="ff4">。</span>利用量测更新公式</span>，<span class="ff1">结合测量的车辆横向加速度和横摆角速度</span>，<span class="ff1">得到对车辆</span></span></div><div class="t m0 x2 h2 y17 ff1 fs0 fc0 sc0 ls0 ws0">侧偏角的估计值<span class="ff4">。</span></div><div class="t m0 x1 h2 y18 ff2 fs0 fc0 sc0 ls0 ws0">-<span class="_ _2"> </span><span class="ff1">步骤<span class="_ _0"> </span></span>5<span class="ff3">：<span class="ff1">迭代更新<span class="ff4">。</span>根据系统的实时测量数据</span>，<span class="ff1">不断迭代更新车辆侧偏角的估计值</span>，<span class="ff1">实现对质心</span></span></div><div class="t m0 x2 h2 y19 ff1 fs0 fc0 sc0 ls0 ws0">侧偏角的准确估计<span class="ff4">。</span></div><div class="t m0 x1 h2 y1a ff2 fs0 fc0 sc0 ls0 ws0">3. Simulink<span class="_ _1"> </span><span class="ff1">模型设计</span></div><div class="t m0 x1 h2 y1b ff1 fs0 fc0 sc0 ls0 ws0">基于无迹卡尔曼滤波算法的<span class="_ _0"> </span><span class="ff2">Simulink<span class="_ _1"> </span></span>模型设计主要包括以下几个部分<span class="ff3">：</span>加速度计和陀螺仪的数据读</div><div class="t m0 x1 h2 y1c ff1 fs0 fc0 sc0 ls0 ws0">取模块<span class="ff4">、<span class="ff2">UT<span class="_ _1"> </span></span></span>变换模块<span class="ff4">、</span>时间更新模块和量测更新模块<span class="ff4">。</span>其中<span class="ff3">，</span>加速度计和陀螺仪的数据读取模块通过</div><div class="t m0 x1 h2 y1d ff1 fs0 fc0 sc0 ls0 ws0">相应的接口参数读取<span class="_ _0"> </span><span class="ff2">TruckSim<span class="_ _1"> </span></span>软件中的实际侧偏角<span class="ff4">、</span>横摆角速度和横向加速度数据<span class="ff4">。<span class="ff2">UT<span class="_ _1"> </span></span></span>变换模块</div><div class="t m0 x1 h2 y1e ff1 fs0 fc0 sc0 ls0 ws0">根据读取到的数据进行变换<span class="ff3">，</span>得到<span class="_ _0"> </span><span class="ff2">Sigma<span class="_ _1"> </span></span>采样点集<span class="ff4">。</span>时间更新模块和量测更新模块根据<span class="_ _0"> </span><span class="ff2">UT<span class="_ _1"> </span></span>变换得到</div><div class="t m0 x1 h2 y1f ff1 fs0 fc0 sc0 ls0 ws0">的<span class="_ _0"> </span><span class="ff2">Sigma<span class="_ _1"> </span></span>采样点集和测量数据<span class="ff3">，</span>更新车辆侧偏角的估计值<span class="ff4">。</span>通过对<span class="_ _0"> </span><span class="ff2">Simulink<span class="_ _1"> </span></span>模型的设计<span class="ff3">，</span>可以实</div><div class="t m0 x1 h2 y20 ff1 fs0 fc0 sc0 ls0 ws0">现对车辆侧偏角的实时估计<span class="ff4">。</span></div></div><div class="pi" data-data='{"ctm":[1.568627,0.000000,0.000000,1.568627,0.000000,0.000000]}'></div></div>