Ellipsotopes: Uniting Ellipsoids and Zonotopes for Reachability Analysis and Fault Detection

被引:24
作者
Kousik, Shreyas [1 ]
Dai, Adam [2 ]
Gao, Grace Xingxin [1 ]
机构
[1] Stanford Univ, Dept Aeronaut & Astronaut, Stanford, CA 94305 USA
[2] Stanford Univ, Dept Elect Engn, Stanford, CA 94305 USA
关键词
Ellipsoids; Fault detection; Reachability analysis; Generators; Collision avoidance; Uncertainty; Task analysis; reachability analysis; set representations;
D O I
10.1109/TAC.2022.3191750
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Ellipsoids are a common representation for reachability analysis, because they can be transformed efficiently under affine maps, and they allow conservative approximation of Minkowski sums, which let one incorporate uncertainty and linearization error in a dynamical system by expanding the size of the reachable set. Zonotopes, a type of symmetric, convex polytope, are similarly frequently used, because they allow efficient numerical implementations of affine maps and exact Minkowski sums. Both of these representations also enable efficient, convex collision detection for fault detection or formal verification tasks, wherein one checks if the reachable set of a system collides (i.e., intersects) with an unsafe set. However, both representations often result in conservative representations for reachable sets of arbitrary systems, and neither is closed under intersection. Recently, representations, such as constrained zonotopes and constrained polynomial zonotopes, have been shown to overcome some of these conservativeness challenges, and are closed under intersection. However, constrained zonotopes cannot represent shapes with smooth boundaries, such as ellipsoids, and constrained polynomial zonotopes can require solving a nonconvex program for collision checking or fault detection. This article introduces ellipsotopes, a set representation that is closed under affine maps, Minkowski sums, and intersections. Ellipsotopes combine the advantages of ellipsoids and zonotopes while ensuring convex collision checking. The utility of this representation is demonstrated on several examples.
引用
收藏
页码:3440 / 3452
页数:13
相关论文
共 50 条
[31]   Process Fault Detection and Reconstruction by Principal Component Analysis [J].
Qi, Ruosen ;
Zhang, Jie .
2019 24TH INTERNATIONAL CONFERENCE ON METHODS AND MODELS IN AUTOMATION AND ROBOTICS (MMAR), 2019, :594-599
[32]   Second-order component analysis for fault detection [J].
Peng, Jingchao ;
Zhao, Haitao ;
Hu, Zhengwei .
JOURNAL OF PROCESS CONTROL, 2021, 108 :25-39
[33]   Discriminant analysis and control chart for the fault detection and identification [J].
Pei, Xudong ;
Yamashita, Yoshiyuki ;
Yoshida, Masatoshi ;
Matsumoto, Shigeru .
16TH EUROPEAN SYMPOSIUM ON COMPUTER AIDED PROCESS ENGINEERING AND 9TH INTERNATIONAL SYMPOSIUM ON PROCESS SYSTEMS ENGINEERING, 2006, 21 :1281-1286
[34]   Interval analysis for neural networks with application to fault detection [J].
Wang, Zhenhua ;
Ma, Youdao ;
Zhu, Song ;
Dinh, Thach Ngoc ;
Shen, Yi .
SCIENCE CHINA-INFORMATION SCIENCES, 2024, 67 (11)
[35]   Analysis of Statistical Features for Fault Detection in Ball Bearing [J].
Shukla, Sanyam ;
Yadav, R. N. ;
Sharma, Jivitesh ;
Khare, Shankul .
2015 IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND COMPUTING RESEARCH (ICCIC), 2015, :589-595
[36]   A symbolic dynamic analysis of flywheel system for fault detection [J].
Wang R. ;
Gong X. ;
Xu M. ;
Li Y. .
Harbin Gongye Daxue Xuebao/Journal of Harbin Institute of Technology, 2016, 48 (10) :31-38
[37]   Improved Nonlinear Fault Detection Technique and Statistical Analysis [J].
Zhang, Yingwei ;
Qin, S. Joe .
AICHE JOURNAL, 2008, 54 (12) :3207-3220
[38]   Analysis of an adaptive parameter estimation and fault detection technique [J].
Chowdhury, FN ;
Aravena, JL .
CONTROL APPLICATIONS OF OPTIMISATION 2003, 2003, :9-14
[39]   WAVELET ANALYSIS FOR STATOR FAULT DETECTION IN INDUCTION MACHINES [J].
Giaccone, Santiago J. ;
Bossio, Guillermo R. ;
Garcia, Guillermo O. ;
Solsona, Jorge A. .
INTERNATIONAL JOURNAL OF WAVELETS MULTIRESOLUTION AND INFORMATION PROCESSING, 2011, 9 (03) :361-374
[40]   Kernel Canonical Variate Dissimilarity Analysis for Fault Detection [J].
Xiao, Shujun .
PROCEEDINGS OF THE 38TH CHINESE CONTROL CONFERENCE (CCC), 2019, :6871-6876