Model‐free online tuning of controller parameters using a globalized local search algorithm

Butt Khurram; Sepehri Nariman

摘要

Summary >In an earlier work, the authors proposed a globalized bounded Nelder‐Mead algorithm with deterministic restarts and a linearly growing memory vector. It was shown that the algorithm was a favorable option for solving multimodal optimization problems like controller tuning because of the greater probability of finding the global minimum and lesser numerical cost. Therefore, the algorithm was successfully used for model‐based offline tuning of sliding mode controller parameters for a servo‐pneumatic position control application. However, such offline tuning requires a sufficiently adequate system model, which, in some applications, is difficult to attain. Moreover, it is not generally appreciated as an essential requirement for controller tuning by the end user like the industry. An improvement in performance of optimization algorithm for tuning is expected if it relies on measurements coming directly from an actual physical system and not just its mathematical model. Therefore, in this paper, we apply the aforementioned algorithm for model‐free online optimization of controller parameters. The application involves the programmatic control of a real‐time interface of a physical system by the algorithm for data flow and logical decisions for optimization. For comparison with the results of the model‐based offline tuning suggested in earlier work, the sliding mode controller parameters are tuned online for the same position control application. The experimental results reveal that the system performance with controller parameters tuned online using the algorithm compares favorably to the one with model‐based offline tuning especially at higher priority level for accuracy. The improvement in system performance amounts to 21%. </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 XMLNS =“http://www.wiley.com/namespaces/wiley”type =“main”xml：lang =“en”> <标题类型=“main”>摘要</ title> 在早期的工作中，作者提出了一种全球化的有界Nelder-Mead算法，具有确定性的重启和线性生长的存储器向量。结果表明，该算法是解决控制器调谐等多式化优化问题的有利选择，因为找到了全局最小和较小的数值成本的概率更大。因此，该算法已成功用于基于模型的伺服气动位置控制应用的滑动模式控制器参数的离线调谐。然而，这种离线调整需要足够的足够的系统模型，在某些应用中，难以实现。此外，通常不欣赏作为作为行业的最终用户的控制器调整的基本要求。如果它依赖于直接来自实际物理系统的测量，并且不仅仅是其数学模型，期望优化算法的性能改进。因此，在本文中，我们应用上述用于控制器参数的无模型在线优化算法。该应用程序涉及通过算法控制物理系统的实时界面，以进行数据流和逻辑决策以进行优化。为了比较早期工作中建议的基于模型的离线调谐的结果，滑模控制器参数在线调谐相同的位置控制应用。实验结果表明，使用该算法调谐的控制器参数的系统性能有利地对比较的基于模型的离线调谐，特别是在更高的优先级以获得精度。系统性能的改善量为21％。</ 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-27675/'>《Optimal Control Applications and Methods》</a> <b style="margin: 0 2px;">|</b><span>2018年第5期</span><b style="margin: 0 2px;">|</b><span>共16页</span> </div> </li> <li> <div class="author"> <span class="lefttit">作者</span> <p id="fAuthorthree" class="threelineshidden zhankaihshouqi"> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Butt Khurram&option=202" target="_blank" rel="nofollow">Butt Khurram;</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Sepehri Nariman&option=202" target="_blank" rel="nofollow">Sepehri Nariman;</a> </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"> <p>Fluid Power and Telerobotics Research Laboratory Department of Mechanical Engineering Faculty of EngineeringUniversity of ManitobaWinnipeg Canada;</p> <p>Fluid Power and Telerobotics Research Laboratory Department of Mechanical Engineering Faculty of EngineeringUniversity of ManitobaWinnipeg Canada;</p> </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/1147.html" title="应用数学">应用数学;</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=online controller optimization&option=203" rel="nofollow">online controller optimization;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=servo‐pneumatic position control&option=203" rel="nofollow">servo‐pneumatic position control;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=sliding mode controller&option=203" rel="nofollow">sliding mode controller;</a> </p> <div class="translation"> 机译：在线控制器优化;伺服气动位置控制;滑模控制器; 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href="/patent-detail/061204679548.html">SISO紧格式无模型控制器基于Attention机制循环神经网络的参数自整定方法</a> <b>[P]</b> . <span> 中国专利： CN111781821B </span> <span> . 2021.09.24</span> </div> </li> <li> <div> <b>2. </b><a class="enjiyixqcontent" href="/patent-detail/061204679225.html">SISO紧格式无模型控制器基于集成学习的参数自整定方法</a> <b>[P]</b> . <span> 中国专利： CN112015083B </span> <span> . 2021.09.24</span> </div> </li> <li> <div> <b>3. </b><a class="enjiyixqcontent" href="/patent-detail/06130454899723.html">GLOBAL SEARCH DEVICE FOR PARALLEL GENETIC ALGORITHM UTILIZING LOCAL SOLUTION AND STORAGE MEDIUM STORED GLOBAL SEARCH PROGRAM OF PARALLEL GENETIC ALGORITHM USING SEARCH DEVICE AND LOCAL SOLUTION</a> <b>[P]</b> . <span> 外国专利：  JPH10134019A </span> <span> . 1998-05-22</span> </div> <p class="zwjiyix translation" style="max-width: initial;height: auto;word-break: break-all;white-space: initial;text-overflow: initial;overflow: initial;"> <span>机译：使用本地解决方案和存储的并行遗传算法的全局搜索设备使用搜索设备和本地解决方案存储的并行遗传算法的全局搜索程序 </span> </p> </li> <li> <div> <b>4. </b><a class="enjiyixqcontent" href="/patent-detail/06130404004611.html">GENERATOR DYNAMIC MODEL PARAMETER ESTIMATION AND TUNING USING ONLINE DATA AND SUBSPACE STATE SPACE MODEL</a> <b>[P]</b> . <span> 外国专利：  US2018349525A1 </span> <span> . 2018-12-06</span> </div> <p class="zwjiyix translation" style="max-width: initial;height: auto;word-break: break-all;white-space: initial;text-overflow: initial;overflow: initial;"> <span>机译：在线数据和子空间状态空间模型的发电机动态模型参数估计与调整 </span> </p> </li> <li> <div> <b>5. </b><a class="enjiyixqcontent" href="/patent-detail/06130406800730.html">Generator dynamic model parameter estimation and tuning using online data and subspace state space model</a> <b>[P]</b> . <span> 外国专利：  US9864820B2 </span> <span> . 2018-01-09</span> </div> <p class="zwjiyix translation" style="max-width: initial;height: auto;word-break: break-all;white-space: initial;text-overflow: initial;overflow: initial;"> <span>机译：使用在线数据和子空间状态空间模型的发电机动态模型参数估计和调整 </span> </p> </li> </ul> </div> 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