A globally stable high performance adaptive robust control algorithm下载

weixin_39820535 2019-09-28 09:00:28

自适应控制 adaptive robust control algorithm ；：y为负载位移；m表示惯性负载；PL=P1-P2为负载压力，P1和P2分别为两腔的压力；A为液压缸内腔的有效工作面积；B代表黏性摩擦系数；Af、 Sf、 f 分别为可建模的库仑摩擦幅值、连续的近似库仑摩擦形状函数、系统外干扰及未建模动态，如未建模的摩擦非线性特性等。
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自适应控制 adaptive robust control algorithm ；：y为负载位移；m表示惯性负载；PL=P1-P2为负载压力，P1和P2分别为两腔的压力；A为液压缸内腔的有效工作面积；B代表黏性摩擦系数；Af、 Sf、 f 分别为可建模的库仑摩擦幅值、连续的近似库仑摩擦形状函数、系统外干扰及未建模动态，如未建模的摩擦非线性特性等。

In this paper, we consider the robust face recognition problem via iterative re-constrained group sparse classifier with adaptive weights learning (IRGSC). Specifically, we propose a group sparse representation classification (GSRC) approach in which weighted features and groups are collaboratively adopted to encode more structure information and discriminative information than other regression based methods. In addition, we derive an efficient algorithm to optimize the proposed objective function, and theoretically prove the convergence. There are several appealing aspects associated with IRGSC. First, adaptively learned weights can be seamlessly incorporated into the GSRC framework. This integrates the locality structure of the data and validity information of the features into l2;p-norm regularization to form a unified formulation. Second, IRGSC is very flexible to different size of training set as well as feature dimension thanks to the l2;p-norm regularization. Third, the derived solution is proved to be a stationary point (globally optimal if p≥1). Comprehensive experiments on representative datasets demonstrate that IRGSC is a robust discriminative classifier which significantly improves the performance and efficiency compared with the state-of-theart methods in dealing with face occlusion, corruption, and illumination changes, etc.

Topics Active Perception in Robotics Adaptive Robotic Systems Aerial Robotics Aerial Robotics: Control Aerial Robotics: Design and Mechanism Aerial Robotics: Detection Aerial Robotics: Learning and Adaptive Systems Aerial Robotics: Mechanics and Control A

参考链接 icml 2017 acceptedPaper Paperlist Relative Fisher Information and Natural Gradient for Learning Large Modular Models Priv’IT: Private and Sample Efficient Identity Testing Being Robust (in High...

基于深度网络的热力图回归方法已成为定位面部关键点的主流方法之一。但是，目前对热力图回归的损失函数的研究却很少。本文对人脸对齐问题中热图回归的理想损失函数性质进行分析。然后，我们提出了一种新的损失函数，称为 Adaptive Wing Loss，它能够使其形状适应不同类型热力图像素的ground truth。这种适应性对前景像素的损失更大，而对背景像素的损失更少。为了解决前景和背景像素之间的不平衡问题，我们还提出了加权损失图（Weighted Loss Map），它在前景和困难的背景像素上分配高权重，以帮助训

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