Robust adaptive backstepping tracking control of stochastic nonlinear systems with unknown input saturation: A command filter approach

摘要

Summary >This paper studies the problem of adaptive observer‐based radial basis function neural network tracking control for a class of strict‐feedback stochastic nonlinear systems comprising an unknown input saturation, uncertainties, and unknown disturbances. To handle the issue of a non‐smooth saturation input signal, a smooth function is chosen to approximate the saturation function and the state observer is used to estimate unmeasured states. By the so‐called command filter method in the controller design procedure, the implementation complexity is reduced in the proposed backstepping method. Moreover, a radial basis function neural network is deployed to reconstruct the unknown nonlinear functions. In addition, the gains of all radial basis function neural networks are updated through one updating law leading to a minimal learning parameter which is independent of the number of neural nodes and the order of the system. Comparing with the existing results, the proposed approach can stabilize a constrained stochastic system more effectively and with less computational burden. Finally, a practical example shows the performance of the proposed controller design. </abstract> </span> <span class="z_kbtn z_kbtnclass hoverxs" style="display: none;">展开▼</span> </div> <div class="translation abstracttxt"> <span class="zhankaihshouqi fivelineshidden" id="abstract"> <span>机译：</span><Abstract Type =“Main”XML：Lang =“en”> <标题类型=“main”>摘要</ title> 本文研究了一类严格反馈随机非线性系统的自适应观察者径向基函数神经网络跟踪控制的问题，包括未知的输入饱和，不确定性和未知干扰。为了处理非平滑饱和输入信号的问题，选择平滑函数以近似饱和度函数，并且状态观察者用于估计未测量状态。通过所谓的命令滤波器方法在控制器设计过程中，以所提出的反向下的方法减少了实现复杂性。此外，部署了径向基函数神经网络以重建未知的非线性功能。此外，通过一个更新法律更新所有径向基函数神经网络的增益，导致最小的学习参数，其独立于神经节点的数量和系统的顺序。与现有结果相比，所提出的方法可以更有效地稳定受约束的随机系统，并且计算负担较少。最后，一个实际的例子显示了所提出的控制器设计的性能。</ p> </摘要> </span> <span class="z_kbtn z_kbtnclass hoverxs" style="display: none;">展开▼</span> </div> </div> <div class="record"> <h2 class="all_title" id="enpatent33" >著录项</h2> <ul> <li> <span class="lefttit">来源</span> <div style="width: 86%;vertical-align: text-top;display: inline-block;"> <a href='/journal-foreign-21736/'>《International Journal of Robust and Nonlinear Control》</a> <b style="margin: 0 2px;">|</b><span>2020年第8期</span><b style="margin: 0 2px;">|</b><span>共18页</span> </div> </li> <li> <div class="author"> <span class="lefttit">作者</span> <p id="fAuthorthree" class="threelineshidden zhankaihshouqi"> </p> <span class="z_kbtnclass z_kbtnclassall hoverxs" id="zkzz" style="display: none;">展开▼</span> </div> </li> <li> <div style="display: flex;"> <span class="lefttit">作者单位</span> <div style="position: relative;margin-left: 3px;max-width: 639px;"> <div class="threelineshidden zhankaihshouqi" id="fOrgthree"> </div> <span class="z_kbtnclass z_kbtnclassall hoverxs" id="zhdw" style="display: none;">展开▼</span> </div> </div> </li> <li > <span class="lefttit">收录信息</span> <span style="width: 86%;vertical-align: text-top;display: inline-block;"></span> </li> <li> <span class="lefttit">原文格式</span> <span>PDF</span> </li> <li> <span class="lefttit">正文语种</span> <span>eng</span> </li> <li> <span class="lefttit">中图分类</span> <span><a href="https://www.zhangqiaokeyan.com/clc/7742.html" title="自动化系统">自动化系统;</a></span> </li> <li class="antistop"> <span class="lefttit">关键词</span> <p style="width: 86%;vertical-align: text-top;"> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=adaptive neural network&option=203" rel="nofollow">adaptive neural network;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=command filter&option=203" rel="nofollow">command filter;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=input saturation&option=203" rel="nofollow">input saturation;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=minimal learning parameter&option=203" rel="nofollow">minimal learning parameter;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=observer&option=203" rel="nofollow">observer;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=stochastic nonlinear systems&option=203" rel="nofollow">stochastic nonlinear systems;</a> </p> <div class="translation"> 机译：自适应神经网络;命令过滤器;输入饱和度;最小的学习参数;观察者;随机非线性系统; 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href="/academic-degree-domestic_mphd_thesis/020315878677.html">具有死区输入的高阶非线性系统自适应跟踪控制研究</a> <b>[A] </b> <span> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=李冰欢&option=202" target="_blank" rel="nofollow" class="tuijian_auth tuijian_authcolor"> . 李冰欢</a> <span> . 2020</span> </span> </div> </li> </ul> <ul style="display: none;"> <li> <div> <b>1. </b><a class="enjiyixqcontent" href="/patent-detail/06120113808484.html">未知随机非线性系统自适应控制方法、控制器、终端、介质</a> <b>[P]</b> . <span> 中国专利： CN113820955A </span> <span> . 2021-12-21</span> </div> </li> <li> <div> <b>2. </b><a class="enjiyixqcontent" href="/patent-detail/061203190292.html">基于未知目标轨迹的非线性系统鲁棒自适应跟踪控制方法</a> <b>[P]</b> . <span> 中国专利： CN106154839B </span> <span> . 2019.02.01</span> </div> </li> <li> <div> <b>3. </b><a class="enjiyixqcontent" href="/patent-detail/06130419510083.html">INDUCTION MOTOR CONTROLLING SYSTEM OF ELECTRONIC POWER STEERING IN A VEHICLE, USING ADAPTIVE BACKSTEPPING CONTROLLER, CAPABLE OF IMPROVING CONTROL 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