非线性参数变化嵌入用于非线性状态估计及其在两连杆机器人操作臂上的应用
Nonlinear parameter-varying embeddings for nonlinear state estimation with application to a two-link robot manipulator
AI总结：
本文提出非线性参数变化嵌入方法设计观测器，在保留非线性结构的同时减少调度参数，并在两连杆机器人上验证，优于扩展卡尔曼滤波和移动horizon估计。
AI中文摘要：
非线性系统的观测器设计是系统与控制设计中的一个相关且具有挑战性的任务。在本工作中，我们遵循将系统嵌入到非线性参数变化系统类中的思路，以受益于标准LPV嵌入中的线性结构，同时保留一些非线性结构，从而减少表示中的调度参数数量。我们阐述了针对一般非线性系统的NLPV观测器设计流程，提出了若干改进，并以一个两臂机器人模型为例展示了应用。在一项数值研究中，我们将NLPV设计的性能与既有的标准非线性方法（如扩展卡尔曼滤波器和移动horizon估计）进行了比较。
英文摘要：
Observer design for nonlinear systems is a relevant and challenging task in systems and control design. In this work, we follow the idea of embedding the system in the class of nonlinear parameter-varying systems to benefit from linear structures as in a standard LPV embedding while keeping some nonlinear structures and, thus, reducing the numbers of scheduling-parameters in the representation. We lay out the NLPV observer design procedure for general nonlinear systems, propose a number of improvements, and exemplify the application for a two-arm robot model. In a numerical study, we compare the performance of the NLPV design to established standard nonlinear approaches such as the \emph{extended Kalman filter} and the \emph{moving horizon estimation}.
