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arXiv 1408.6809eess.SYcs.SY
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计算LPV系统诱导L2范数的下界

Computation of lower bounds for the induced L2 norm of LPV systems

Institute for Computer Science and Control(计算机科学与控制研究所);University of Minnesota(明尼苏达大学)

Tamas Peni, Peter J. Seiler

AI总结:

本文提出计算LPV系统诱导L2范数下界的新算法,通过限制参数轨迹为周期信号,利用最新结果精确计算L2范数,并提供下界补充分析以指导系统性能改进。

摘要:

确定线性参数变化(LPV)系统诱导L2范数是许多分析和鲁棒控制设计过程中的重要部分。大多数先前工作集中在高效计算诱导L2范数的上界。上界条件通常基于缩放的小增益定理动态乘数或参数依赖的李雅普诺夫函数耗散不等式。本文提出了一种互补算法来计算诱导L2范数的下界。所提出的方案通过将参数轨迹限制为周期信号来计算增益的下界。这种限制使能够利用最新结果精确计算周期线性时变系统的L2范数。所提出的下界算法有两个优势。首先,下界补充了标准上界技术。具体而言,界之间的小间隙表明进一步计算,例如使用更复杂的李雅普诺夫函数的上界是不必要的。其次,下界算法返回LPV系统的一个劣参数轨迹,可以进一步分析以提供系统性能的洞察。

英文摘要:

Determining the induced L2 norm of a linear, parameter-varying (LPV) system is an integral part of many analysis and robust control design procedures. Most prior work has focused on efficiently computing upper bounds for the induced L2 norm. The conditions for upper bounds are typically based on scaled small-gain theorems with dynamic multipliers or dissipation inequalities with parameter dependent Lyapunov functions. This paper presents a complementary algorithm to compute lower bounds for the induced L2 norm. The proposed approach computes a lower bound on the gain by restricting the parameter trajectory to be a periodic signal. This restriction enables the use of recent results for exact calculation of the L2 norm for a periodic linear time varying system. The proposed lower bound algorithm has two benefits. First, the lower bound complements standard upper bound techniques. Specifically, a small gap between the bounds indicates that further computation, e.g. upper bounds with more complex Lyapunov functions, is unnecessary. Second, the lower bound algorithm returns a bad parameter trajectory for the LPV system that can be further analyzed to provide insight into the system performance.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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