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Study of input space for state estimation of high‐rate dynamics
Author(s) -
Hong Jonathan,
Laflamme Simon,
Dodson Jacob
Publication year - 2018
Publication title -
structural control and health monitoring
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.587
H-Index - 62
eISSN - 1545-2263
pISSN - 1545-2255
DOI - 10.1002/stc.2159
Subject(s) - estimator , control theory (sociology) , computer science , observer (physics) , system dynamics , rate of convergence , state space , estimation , misrepresentation , state (computer science) , process (computing) , state observer , control engineering , simulation , engineering , mathematics , artificial intelligence , algorithm , key (lock) , statistics , control (management) , nonlinear system , physics , systems engineering , operating system , computer security , quantum mechanics , law , political science
Summary High‐rate dynamic systems are defined as systems being exposed to highly dynamic environments that comprise high‐rate and high‐amplitude events. Examples of such systems include civil structures exposed to blast, space shuttles prone to debris strikes, and aerial vehicles experiencing in‐flight changes. The high‐rate dynamic characteristics of these systems provides several possibilities for state estimators to improve performance, including a high potential to reduce injuries and save lives. In this paper, opportunities and challenges that are specific to state estimation of high‐rate dynamic systems are presented and discussed. It is argued that a possible path to design of state estimators for high‐rate dynamics is the utilization of adaptive data‐based observers but that further research needs to be conducted to increase their convergence rate. An adaptive neuro‐observer is designed to examine the particular challenges in selecting an appropriate input space in high‐rate state estimation. It is found that the choice of inputs has a significant influence on the observer performance for high‐rate dynamics when compared against a low‐rate environment. Additionally, misrepresentation of a system dynamics through incorrect input spaces produces large errors in the estimation, which could potentially trick the decision‐making process in a closed‐loop system in making bad judgments.