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A Global Maximum Error Controller Based Nonlinearity Aware TPWL Method for Reducing Nonlinear Systems
Cybernetics and Systems ( IF 1.1 ) Pub Date : 2020-05-18 , DOI: 10.1080/01969722.2020.1770501
Shifali Kalra 1 , Mashuq Un Nabi 1
Affiliation  

Abstract Model order reduction is a common practice to reduce large order systems so that their simulation and control become easy. Nonlinearity aware trajectory piecewise linear is a variation of trajectory piecewise linearization technique of order reduction that is used to reduce nonlinear systems. With this scheme, the reduced approximation of the system is generated by weighted sum of the linearized and reduced sub-models obtained at certain linearization points on the system trajectory. This scheme uses dynamically inspired weight assignment that makes the approximation nonlinearity aware. Just as weight assignment, the process of linearization points selection is also important for generating faithful approximations. This article uses a global maximum error controller based linearization points selection scheme according to which a state is chosen as a linearization point if the error between a current reduced model and the full order nonlinear system reaches a maximum value. A combination that not only selects linearization points based on an error controller but also assigns dynamic inspired weights is shown in this article. The proposed scheme generates approximations with higher accuracies. This is demonstrated by applying the proposed method to some benchmark nonlinear circuits including RC ladder network and inverter chain circuit and comparing the results with the conventional schemes.

中文翻译:

一种减少非线性系统的基于全局最大误差控制器的非线性感知TPWL方法

摘要 模型降阶是对大阶系统进行降阶以使其仿真和控制变得容易的一种常见做法。非线性感知轨迹分段线性是轨迹分段线性化技术的一种变体,用于减少非线性系统。使用这种方案,系统的简化近似是通过在系统轨迹上的某些线性化点获得的线性化和简化子模型的加权和来生成的。该方案使用动态启发的权重分配,使近似非线性意识到。正如权重分配一样,线性化点选择的过程对于生成忠实的近似值也很重要。本文采用基于全局最大误差控制器的线性化点选择方案,如果当前简化模型与全阶非线性系统之间的误差达到最大值,则选择状态作为线性化点。本文展示了一种组合,它不仅基于误差控制器选择线性化点,而且还分配动态启发权重。所提出的方案生成具有更高准确度的近似值。通过将所提出的方法应用于一些基准非线性电路,包括 RC 梯形网络和逆变器链电路,并将结果与​​传统方案进行比较,证明了这一点。本文展示了一种组合,它不仅基于误差控制器选择线性化点,而且还分配动态启发权重。所提出的方案生成具有更高准确度的近似值。通过将所提出的方法应用于一些基准非线性电路,包括 RC 梯形网络和逆变器链电路,并将结果与​​传统方案进行比较,证明了这一点。本文展示了一种组合,它不仅基于误差控制器选择线性化点,而且还分配动态启发权重。所提出的方案生成具有更高准确度的近似值。通过将所提出的方法应用于一些基准非线性电路,包括 RC 梯形网络和逆变器链电路,并将结果与​​传统方案进行比较,证明了这一点。
更新日期:2020-05-18
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