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Robust Optimal Regional Closed-loop Pole Assignment over Positivity Conditions and Differential Evolution

机译:对正性条件和差分演化的鲁棒最优区域闭环极点分配

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摘要

The presented paper describes an optimal closed-loop pole assignment for a vast control feedback framework and structure. The main aim of this paper is a regional selection of the closed-loop central polynomial with additional flexibility and leeway for the following optimization procedure. The optimality of the approach is provided through the positivity condition of the quasi-convex spectral polynomial (QSP). The spectral polynomial presents the uncertainty and closed-loop characteristic of the feedback system. The derivation of the QSP is based on the characteristics of the uncertainty models and metrics with norm H∞. The structure of the controller is freely chosen and is also the subject of the optimization procedure. An objective function for the optimization algorithm Differential Evolution (DE) is presented in multi-criteria form and is directly composed of various QSP's. Such optimization of the objective function improves the transparence, computation expense and the regularity of the optimal/suboptimal solution.
机译:本文描述了一个庞大的控制反馈框架和结构的最优闭环极点分配。本文的主要目的是对闭环中心多项式进行区域选择,为以下优化过程提供额外的灵活性和余地。该方法的最优性是通过准凸谱多项式(QSP)的正性条件提供的。谱多项式呈现了反馈系统的不确定性和闭环特性。QSP的推导基于不确定性模型和范数为H∞的指标的特征。控制器的结构是自由选择的,也是优化过程的主题。优化算法差分进化 (DE) 的目标函数以多准则形式呈现,并直接由各种 QSP 组成。这种目标函数的优化提高了最优/次优解的透明度、计算费用和规律性。

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