Musings on persistent excitation prompts new weighted least squares SysID method for nonlinear differential equation based systems

C. Tolle
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Abstract

The Control community relies heavily on good System Identification (SysID) for finding the plant models needed to develop a good controller. However over time the SysID process and controller development process have remained generally separate activities. One reason for this is that SysID and Control are disparate in their fundamental nature. For good SysID, one is faced with the challenge of persistently exciting plant dynamics; while a good control system attempts to constrain or suppress much of a plant's natural dynamics with desired dynamics. It is this inherent conflict that separates the two practices. But for many plants, their inherent instabilities makes trajectory collection difficult, thus there is a desire to perform data collection while under some simple form of control. Nevertheless, in order to perform solid SysID one must sample the very dynamics one might need to suppress; how then can this be achieved? This paper will explore the notation of persistent excitation, its relationship to phase space trajectories, and how one might recover the most nonlinear dynamics information for SysID while remaining under the linearizing based control region - the very place that those dynamics are most suppressed.
对持续激励的思考,提出了一种新的基于非线性微分方程系统的加权最小二乘SysID方法
控制社区在很大程度上依赖于良好的系统识别(SysID)来寻找开发良好控制器所需的工厂模型。然而,随着时间的推移,SysID过程和控制器开发过程通常保持独立的活动。其中一个原因是SysID和Control在其基本性质上是不同的。对于SysID来说,人们面临着持续令人兴奋的植物动态的挑战;而一个好的控制系统试图用期望的动态来约束或抑制植物的自然动态。正是这种内在的冲突将这两种做法分开。但对于许多植物来说,它们固有的不稳定性使得轨迹收集变得困难,因此人们希望在某种简单的控制形式下进行数据收集。然而,为了执行可靠的SysID,必须对可能需要抑制的动态进行采样;那么如何才能做到这一点呢?本文将探讨持续激励的符号,它与相空间轨迹的关系,以及如何恢复SysID的最非线性动力学信息,同时保持在基于线性化的控制区域下-这些动力学最受抑制的地方。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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