基于扩展卡尔曼观测器的无模型预测电流控制仿真中包含普基于ESO，与EKF两个观测器，可自行切对比

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基于扩展卡尔曼观测器的无模型预测电流控制 仿真中包含普基于ESO，与EKF两个观测器，可自行切对比
<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/90213822/2/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/90213822/bg1.jpg"/><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">基于扩展卡尔曼观测器的无模型预测电流控制<span class="ff2">：</span>仿真中的两种观测器比较与应用</div><div class="t m0 x1 h2 y2 ff1 fs0 fc0 sc0 ls0 ws0">引言<span class="ff2">：</span></div><div class="t m0 x1 h2 y3 ff1 fs0 fc0 sc0 ls0 ws0">随着现代电力电子技术的飞速发展<span class="ff2">，</span>电流控制成为了电力系统中至关重要的环节<span class="ff3">。</span>为了提高电流控制</div><div class="t m0 x1 h2 y4 ff1 fs0 fc0 sc0 ls0 ws0">的性能<span class="ff2">，</span>研究者们不断探索新的控制策略<span class="ff3">。</span>本文重点探讨基于扩展卡尔曼观测器的无模型预测电流控</div><div class="t m0 x1 h2 y5 ff1 fs0 fc0 sc0 ls0 ws0">制<span class="ff2">，</span>特别涉及仿真环境中使用的两种观测器<span class="ff2">：</span>扩展卡尔曼滤波器<span class="ff2">（<span class="ff4">EKF</span>）</span>和扩展卡尔曼观测器<span class="ff2">（<span class="ff4">ESO</span></span></div><div class="t m0 x1 h2 y6 ff2 fs0 fc0 sc0 ls0 ws0">）<span class="ff3">。<span class="ff1">本文将介绍这两种观测器的原理</span>、<span class="ff1">特点</span></span>，<span class="ff1">并通过仿真实验进行性能对比<span class="ff3">。</span></span></div><div class="t m0 x1 h2 y7 ff1 fs0 fc0 sc0 ls0 ws0">一<span class="ff3">、</span>扩展卡尔曼观测器<span class="ff2">（<span class="ff4">ESO</span>）</span>原理及其在无模型预测电流控制中的应用</div><div class="t m0 x1 h2 y8 ff1 fs0 fc0 sc0 ls0 ws0">扩展卡尔曼观测器<span class="ff2">（<span class="ff4">ESO</span>）</span>是一种强大的非线性系统状态估计工具<span class="ff3">。</span>在电力系统中<span class="ff2">，<span class="ff4">ESO<span class="_ _0"> </span></span></span>可以用来估</div><div class="t m0 x1 h2 y9 ff1 fs0 fc0 sc0 ls0 ws0">计电池的荷电状态<span class="ff3">、</span>系统状态以及输出误差等<span class="ff3">。</span>在无模型预测电流控制中<span class="ff2">，<span class="ff4">ESO<span class="_ _0"> </span></span></span>通过对系统状态的估</div><div class="t m0 x1 h2 ya ff1 fs0 fc0 sc0 ls0 ws0">计<span class="ff2">，</span>实现对电流的准确控制<span class="ff3">。</span>具体而言<span class="ff2">，<span class="ff4">ESO<span class="_ _0"> </span></span></span>通过对系统状态的观测和预测<span class="ff2">，</span>为电流控制器提供实时</div><div class="t m0 x1 h2 yb ff1 fs0 fc0 sc0 ls0 ws0">的状态信息<span class="ff2">，</span>从而实现电流的精确调节<span class="ff3">。</span>此外<span class="ff2">，</span>由于<span class="_ _1"> </span><span class="ff4">ESO<span class="_ _0"> </span></span>具有在线自适应能力<span class="ff2">，</span>能够在系统参数变化</div><div class="t m0 x1 h2 yc ff1 fs0 fc0 sc0 ls0 ws0">时自动调整观测器参数<span class="ff2">，</span>因此具有良好的鲁棒性<span class="ff3">。</span></div><div class="t m0 x1 h2 yd ff1 fs0 fc0 sc0 ls0 ws0">二<span class="ff3">、</span>扩展卡尔曼滤波器<span class="ff2">（<span class="ff4">EKF</span>）</span>原理及其在电流控制中的应用</div><div class="t m0 x1 h2 ye ff1 fs0 fc0 sc0 ls0 ws0">扩展卡尔曼滤波器<span class="ff2">（<span class="ff4">EKF</span>）</span>是一种常用的非线性滤波算法<span class="ff3">。</span>与<span class="_ _1"> </span><span class="ff4">ESO<span class="_ _0"> </span></span>类似<span class="ff2">，<span class="ff4">EKF<span class="_ _0"> </span></span></span>也可以用于估计系统的</div><div class="t m0 x1 h2 yf ff1 fs0 fc0 sc0 ls0 ws0">状态<span class="ff3">。</span>在电流控制中<span class="ff2">，<span class="ff4">EKF<span class="_ _0"> </span></span></span>通过对系统状态的估计<span class="ff2">，</span>为控制器提供准确的参考信息<span class="ff3">。</span>然而<span class="ff2">，</span>由于<span class="_ _1"> </span><span class="ff4">EKF</span></div><div class="t m0 x1 h2 y10 ff1 fs0 fc0 sc0 ls0 ws0">在非线性系统中的性能受到线性化误差的影响<span class="ff2">，</span>因此在某些情况下<span class="ff2">，</span>其性能可能不如<span class="_ _1"> </span><span class="ff4">ESO<span class="ff3">。</span></span>此外<span class="ff2">，</span></div><div class="t m0 x1 h2 y11 ff4 fs0 fc0 sc0 ls0 ws0">EKF<span class="_ _0"> </span><span class="ff1">的运算复杂度相对较高<span class="ff2">，</span>这在一些实时性要求较高的场合可能带来一定的挑战<span class="ff3">。</span></span></div><div class="t m0 x1 h2 y12 ff1 fs0 fc0 sc0 ls0 ws0">三<span class="ff3">、</span>仿真实验及性能对比</div><div class="t m0 x1 h2 y13 ff1 fs0 fc0 sc0 ls0 ws0">为了对比<span class="_ _1"> </span><span class="ff4">ESO<span class="_ _0"> </span></span>和<span class="_ _1"> </span><span class="ff4">EKF<span class="_ _0"> </span></span>在基于无模型预测电流控制中的性能差异<span class="ff2">，</span>我们在仿真环境中进行了大量实验</div><div class="t m0 x1 h2 y14 ff3 fs0 fc0 sc0 ls0 ws0">。<span class="ff1">实验结果表明<span class="ff2">，</span>在大多数情况下<span class="ff2">，<span class="ff4">ESO<span class="_ _0"> </span></span></span>的估计精度和鲁棒性均优于<span class="_ _1"> </span><span class="ff4">EKF</span></span>。<span class="ff1">特别是在系统参数变化较</span></div><div class="t m0 x1 h2 y15 ff1 fs0 fc0 sc0 ls0 ws0">大的情况下<span class="ff2">，<span class="ff4">ESO<span class="_ _0"> </span></span></span>能够迅速适应系统变化<span class="ff2">，</span>保持较高的估计精度<span class="ff2">；</span>而<span class="_ _1"> </span><span class="ff4">EKF<span class="_ _0"> </span></span>的线性化误差可能导致其性</div><div class="t m0 x1 h2 y16 ff1 fs0 fc0 sc0 ls0 ws0">能下降<span class="ff3">。</span>此外<span class="ff2">，</span>在运算复杂度方面<span class="ff2">，<span class="ff4">ESO<span class="_ _0"> </span></span></span>相比<span class="_ _1"> </span><span class="ff4">EKF<span class="_ _0"> </span></span>具有优势<span class="ff2">，</span>更适用于实时性要求较高的场合<span class="ff3">。</span></div><div class="t m0 x1 h2 y17 ff1 fs0 fc0 sc0 ls0 ws0">四<span class="ff3">、</span>两种观测器的切换对比</div><div class="t m0 x1 h2 y18 ff1 fs0 fc0 sc0 ls0 ws0">在实际应用中<span class="ff2">，</span>我们可以根据系统需求和实际情况在<span class="_ _1"> </span><span class="ff4">ESO<span class="_ _0"> </span></span>和<span class="_ _1"> </span><span class="ff4">EKF<span class="_ _0"> </span></span>之间进行切换<span class="ff3">。</span>在某些场景下<span class="ff2">，<span class="ff4">ESO</span></span></div><div class="t m0 x1 h2 y19 ff1 fs0 fc0 sc0 ls0 ws0">可能更适合<span class="ff2">；</span>而在其他场景下<span class="ff2">，<span class="ff4">EKF<span class="_ _0"> </span></span></span>可能更具优势<span class="ff3">。</span>这种切换机制可以根据系统的实时性能<span class="ff3">、</span>估计精</div><div class="t m0 x1 h2 y1a ff1 fs0 fc0 sc0 ls0 ws0">度<span class="ff3">、</span>运算资源等因素进行动态调整<span class="ff3">。</span>通过仿真实验对比<span class="ff2">，</span>我们发现这种切换机制可以有效地提高系统</div><div class="t m0 x1 h2 y1b ff1 fs0 fc0 sc0 ls0 ws0">的整体性能<span class="ff3">。</span></div><div class="t m0 x1 h2 y1c ff1 fs0 fc0 sc0 ls0 ws0">五<span class="ff3">、</span>结论</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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