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State-Space Transformations of Uncertain Systems with Purely Real and Conjugate-Complex Eigenvalues into a Cooperative Form

机译:具有纯实数和共轭复杂特征值的不确定系统的状态空间变换为合作形式

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Cooperativity of uncertain dynamic systems can be exploited to simplify several tasks such as the computation of guaranteed state enclosures, the design of interval observers, forecasting worst-case bounds for selected system outputs in predictive control, and the identification of unknown parameters. Although many system models in biological, chemical, and medical applications are naturally cooperative, there is also a great number of systems (typically from the fields of electric, magnetic, and mechanical applications) which do not show this property if the state equations are derived using first-principle techniques. Hence, it is often desired to transform such system models into an equivalent cooperative form. Unfortunately, these transformations are often not straightforward, especially, if linear systems and nonlinear ones with state-dependent system matrices are subject to bounded parameter uncertainty. This paper presents two approaches for the transformation of state equations into a cooperative form for which the original system models do not fulfill sufficient criteria for cooperativity in their basic formulation. These are a time-invariant transformation for systems with purely real eigenvalues and a time-varying transformation in the case of conjugate-complex eigenvalues. Both procedures are tested on real-life application scenarios with interval parameters.
机译:可以利用不确定动态系统的协作性来简化多项任务,例如保证状态封闭的计算,间隔观察器的设计,在预测控制中预测所选系统输出的最坏情况边界以及识别未知参数。尽管生物,化学和医学应用中的许多系统模型都是自然协作的,但是如果推导了状态方程,则还有很多系统(通常来自电气,磁和机械应用领域)无法显示此属性。使用第一原理技术。因此,通常期望将这样的系统模型转换成等效的协作形式。不幸的是,这些转换通常并不简单,特别是如果线性系统和具有状态相关系统矩阵的非线性系统受到有界参数不确定性的影响。本文提出了两种将状态方程式转换为合作形式的方法,在这种方法中,原始系统模型的基本公式不能满足充分的合作标准。对于具有纯实特征值的系统,这是时不变的变换,而对于共轭复特征值,则是时变变换。两种过程都在具有间隔参数的实际应用场景中进行了测试。

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