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

机译:持续激励的沉思提示新加权最小二乘Sysid基于非线性微分方程的系统

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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.
机译:控制界严重依赖于良好的系统识别(Sysid)来查找开发良好控制器所需的工厂模型。但随着时间的推移,Sysid进程和控制器开发过程一定是一般的活动。这是一个原因是Sysid和控制在他们的基本性中存在。对于良好的Sysid,人们面临着持续兴奋的植物动态的挑战;虽然良好的控制系统试图用所需的动态来限制或抑制植物的大部分自然动态。这是解决两种实践的固有冲突。但对于许多植物来说,他们固有的不稳定性使轨迹集合困难,因此希望在某种简单的控制形式下执行数据收集。尽管如此,为了执行实心的Sysid,必须采样一个可能需要抑制的动态;那么这可以实现如何实现?本文将探讨持久激励的符号,其与相空间轨迹的关系,以及如何在基于线性化的控制区域下剩余的时,如何恢复最大的非线性动力学信息 - 那些动态最抑制的地方的位置。

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