利用BP神经网络来预测不同工况下的轮胎侧向力，从carsim中获得数据，利用BP神经网络训练得到轮胎侧向力估计模型 主要内容如下：1、在线计算k值，可便于后续和别的控制器联合，2、利用BP神经网
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利用BP神经网络来预测不同工况下的轮胎侧向力，从carsim中获得数据，利用BP神经网络训练得到轮胎侧向力估计模型。主要内容如下：1、在线计算k值，可便于后续和别的控制器联合，2、利用BP神经网络对从carsim中获得的轮胎数据进行训练获得精准的神经网络模型，并生成simulink模块3、将生成的simulink模块和LQR控制器结合，实时估计轮胎侧向力。4、内含有帮助文档图为108km h下的单移线轨迹跟踪效果，侧向力估计效果，以及BP神经网络的预测误差，轮胎侧向力预测效果还不错 <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/90182758/2/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/90182758/bg1.jpg"/><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">标题<span class="ff2">：</span>基于<span class="_ _0"> </span><span class="ff3">BP<span class="_ _1"> </span></span>神经网络的轮胎侧向力预测模型研究与应用</div><div class="t m0 x1 h2 y2 ff1 fs0 fc0 sc0 ls0 ws0">摘要<span class="ff2">：</span>本文基于<span class="_ _0"> </span><span class="ff3">BP<span class="_ _1"> </span></span>神经网络模型<span class="ff2">，</span>通过对从<span class="_ _0"> </span><span class="ff3">carsim<span class="_ _1"> </span></span>中获取的轮胎数据进行训练<span class="ff2">，</span>实现对不同工况</div><div class="t m0 x1 h2 y3 ff1 fs0 fc0 sc0 ls0 ws0">下的轮胎侧向力的精准预测<span class="ff4">。</span>通过在线计算<span class="_ _0"> </span><span class="ff3">k<span class="_ _1"> </span></span>值<span class="ff2">，</span>使得该模型能够与其他控制器联合使用<span class="ff2">，</span>并结合</div><div class="t m0 x1 h2 y4 ff3 fs0 fc0 sc0 ls0 ws0">LQR<span class="_ _1"> </span><span class="ff1">控制器实现实时侧向力估计<span class="ff4">。</span>文章还附带有详细的帮助文档<span class="ff2">，</span>通过图示展示了<span class="_ _0"> </span></span>108km/h<span class="_ _1"> </span><span class="ff1">下的单</span></div><div class="t m0 x1 h2 y5 ff1 fs0 fc0 sc0 ls0 ws0">移线轨迹跟踪效果<span class="ff4">、</span>侧向力估计效果以及<span class="_ _0"> </span><span class="ff3">BP<span class="_ _1"> </span></span>神经网络的预测误差<span class="ff4">。</span></div><div class="t m0 x1 h2 y6 ff1 fs0 fc0 sc0 ls0 ws0">关键词<span class="ff2">：<span class="ff3">BP<span class="_ _1"> </span></span></span>神经网络<span class="ff2">，</span>轮胎侧向力预测<span class="ff2">，<span class="ff3">carsim</span>，<span class="ff3">simulink<span class="_ _1"> </span></span></span>模块<span class="ff2">，<span class="ff3">LQR<span class="_ _1"> </span></span></span>控制器</div><div class="t m0 x1 h2 y7 ff1 fs0 fc0 sc0 ls0 ws0">引言<span class="ff2">：</span></div><div class="t m0 x1 h2 y8 ff1 fs0 fc0 sc0 ls0 ws0">在汽车动力学控制领域中<span class="ff2">，</span>准确预测轮胎侧向力对于提高车辆操控性能至关重要<span class="ff4">。</span>传统的方法通常依</div><div class="t m0 x1 h2 y9 ff1 fs0 fc0 sc0 ls0 ws0">赖于复杂的物理模型<span class="ff2">，</span>但这些模型需要大量的实验和参数调优<span class="ff2">，</span>且对于不同工况下的预测效果较为有</div><div class="t m0 x1 h2 ya ff1 fs0 fc0 sc0 ls0 ws0">限<span class="ff4">。</span>因此<span class="ff2">，</span>借助机器学习方法<span class="ff2">，</span>特别是<span class="_ _0"> </span><span class="ff3">BP<span class="_ _1"> </span></span>神经网络模型<span class="ff2">，</span>可以更好地解决该问题<span class="ff4">。</span>本文将介绍利用</div><div class="t m0 x1 h2 yb ff3 fs0 fc0 sc0 ls0 ws0">BP<span class="_ _1"> </span><span class="ff1">神经网络来预测不同工况下轮胎侧向力的研究与应用<span class="ff4">。</span></span></div><div class="t m0 x1 h2 yc ff3 fs0 fc0 sc0 ls0 ws0">1.<span class="_ _2"> </span><span class="ff1">在线计算<span class="_ _0"> </span></span>k<span class="_ _1"> </span><span class="ff1">值</span></div><div class="t m0 x1 h2 yd ff1 fs0 fc0 sc0 ls0 ws0">为了便于与其他控制器联合使用<span class="ff2">，</span>我们需要在训练<span class="_ _0"> </span><span class="ff3">BP<span class="_ _1"> </span></span>神经网络模型时在线计算<span class="_ _0"> </span><span class="ff3">k<span class="_ _1"> </span></span>值<span class="ff4">。<span class="ff3">k<span class="_ _1"> </span></span></span>值是一个关</div><div class="t m0 x1 h2 ye ff1 fs0 fc0 sc0 ls0 ws0">键参数<span class="ff2">，</span>通过合理调节可以使得模型在不同工况下的预测能力更强<span class="ff4">。</span>在线计算<span class="_ _0"> </span><span class="ff3">k<span class="_ _1"> </span></span>值的方法可以根据具</div><div class="t m0 x1 h2 yf ff1 fs0 fc0 sc0 ls0 ws0">体需求和实际情况进行选择<span class="ff2">，</span>例如可以利用车辆动力学模型和实时采集的传感器数据进行计算<span class="ff4">。</span></div><div class="t m0 x1 h2 y10 ff3 fs0 fc0 sc0 ls0 ws0">2.<span class="_ _2"> </span>BP<span class="_ _1"> </span><span class="ff1">神经网络模型训练及<span class="_ _0"> </span></span>Simulink<span class="_ _1"> </span><span class="ff1">模块生成</span></div><div class="t m0 x1 h2 y11 ff1 fs0 fc0 sc0 ls0 ws0">通过从<span class="_ _0"> </span><span class="ff3">carsim<span class="_ _1"> </span></span>中获取的轮胎数据<span class="ff2">，</span>我们可以对<span class="_ _0"> </span><span class="ff3">BP<span class="_ _1"> </span></span>神经网络模型进行训练<span class="ff2">，</span>以获得对轮胎侧向力的</div><div class="t m0 x1 h2 y12 ff1 fs0 fc0 sc0 ls0 ws0">精准预测能力<span class="ff4">。</span>训练过程中<span class="ff2">，</span>我们需要选择适当的输入特征<span class="ff2">，</span>例如轮胎滑移角<span class="ff4">、</span>侧偏角等<span class="ff4">。</span>同时<span class="ff2">，</span>还</div><div class="t m0 x1 h2 y13 ff1 fs0 fc0 sc0 ls0 ws0">需要进行数据预处理<span class="ff2">，</span>例如归一化处理和特征选择<span class="ff2">，</span>以提高训练效果和模型泛化能力<span class="ff4">。</span>最后<span class="ff2">，</span>通过</div><div class="t m0 x1 h2 y14 ff3 fs0 fc0 sc0 ls0 ws0">Simulink<span class="_ _1"> </span><span class="ff1">模块的生成<span class="ff2">，</span>我们可以方便地将该模型应用于实际控制系统中<span class="ff4">。</span></span></div><div class="t m0 x1 h2 y15 ff3 fs0 fc0 sc0 ls0 ws0">3.<span class="_ _2"> </span><span class="ff1">轮胎侧向力实时估计与<span class="_ _0"> </span></span>LQR<span class="_ _1"> </span><span class="ff1">控制器结合</span></div><div class="t m0 x1 h2 y16 ff1 fs0 fc0 sc0 ls0 ws0">将生成的<span class="_ _0"> </span><span class="ff3">Simulink<span class="_ _1"> </span></span>模块与<span class="_ _0"> </span><span class="ff3">LQR<span class="_ _1"> </span></span>控制器结合<span class="ff2">，</span>即可实现实时轮胎侧向力的估计<span class="ff4">。<span class="ff3">LQR<span class="_ _1"> </span></span></span>控制器是一种</div><div class="t m0 x1 h2 y17 ff1 fs0 fc0 sc0 ls0 ws0">常用的线性控制器<span class="ff2">，</span>通过融合轮胎侧向力信息<span class="ff2">，</span>可以实现车辆操控性能的优化<span class="ff4">。</span>通过实时估计轮胎侧</div><div class="t m0 x1 h2 y18 ff1 fs0 fc0 sc0 ls0 ws0">向力<span class="ff2">，<span class="ff3">LQR<span class="_ _1"> </span></span></span>控制器可以更加准确地调整车辆的行驶状态<span class="ff2">，</span>提高操控响应性和稳定性<span class="ff4">。</span></div><div class="t m0 x1 h2 y19 ff3 fs0 fc0 sc0 ls0 ws0">4.<span class="_ _2"> </span><span class="ff1">帮助文档与实验结果展示</span></div><div class="t m0 x1 h2 y1a ff1 fs0 fc0 sc0 ls0 ws0">为了方便使用者了解和应用该轮胎侧向力预测模型<span class="ff2">，</span>我们附带了详细的帮助文档<span class="ff2">，</span>包括模型训练步骤</div><div class="t m0 x1 h2 y1b ff4 fs0 fc0 sc0 ls0 ws0">、<span class="ff1">参数设置和模型应用方法等</span>。<span class="ff1">同时<span class="ff2">，</span>我们通过实验结果展示了<span class="_ _0"> </span><span class="ff3">108km/h<span class="_ _1"> </span></span>下的单移线轨迹跟踪效果</span></div><div class="t m0 x1 h2 y1c ff4 fs0 fc0 sc0 ls0 ws0">、<span class="ff1">侧向力估计效果以及<span class="_ _0"> </span><span class="ff3">BP<span class="_ _1"> </span></span>神经网络的预测误差</span>。<span class="ff1">实验结果表明<span class="ff2">，</span>该轮胎侧向力预测模型具有较好的</span></div><div class="t m0 x1 h2 y1d ff1 fs0 fc0 sc0 ls0 ws0">预测精度和实用性<span class="ff4">。</span></div><div class="t m0 x1 h2 y1e ff1 fs0 fc0 sc0 ls0 ws0">结论<span class="ff2">：</span></div></div><div class="pi" data-data='{"ctm":[1.568627,0.000000,0.000000,1.568627,0.000000,0.000000]}'></div></div>