TY - JOUR
T1 - On separating points for ensemble controllability
AU - Li, Jr Shin
AU - Zhang, Wei
AU - Tie, Lin
N1 - Funding Information:
∗Received by the editors August 1, 2019; accepted for publication (in revised form) July 5, 2020; published electronically September 9, 2020. https://doi.org/10.1137/19M1278648 Funding: This work was supported in part by the National Science Foundation under awards CMMI-1462796, ECCS-1509342, and ECCS-1810202 and by the Air Force Office of Scientific Research under award FA9550-17-1-0166. †Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO 63130 ([email protected], [email protected]). ‡School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100083, China ([email protected]).
Publisher Copyright:
© 2020 Society for Industrial and Applied Mathematics
PY - 2020
Y1 - 2020
N2 - Recent years have witnessed a wave of research activities in systems science toward the study of population systems. The driving force behind this shift was geared by numerous emerging and ever-changing technologies in life and physical sciences and engineering, from neuroscience, biology, and quantum physics to robotics, where many control-enabled applications involve manipulating a large ensemble of structurally identical dynamic units, or agents. Analyzing fundamental properties of ensemble control systems in turn plays a foundational and critical role in enabling and, further, advancing these applications, and the analysis is largely beyond the capability of classical control techniques. In this paper, we consider an ensemble of time-invariant linear systems evolving on an infinite-dimensional space of continuous functions. We exploit the notion of separating points and techniques of polynomial approximation to develop necessary and sufficient ensemble controllability conditions. In particular, we introduce an extended notion of controllability matrix, called the ensemble controllability matrix. This enables the characterization of ensemble controllability through evaluating controllability of each individual system in the ensemble. As a result, the work provides a unified framework with a systematic procedure for analyzing control systems defined on an infinite-dimensional space by a finite-dimensional approach.
AB - Recent years have witnessed a wave of research activities in systems science toward the study of population systems. The driving force behind this shift was geared by numerous emerging and ever-changing technologies in life and physical sciences and engineering, from neuroscience, biology, and quantum physics to robotics, where many control-enabled applications involve manipulating a large ensemble of structurally identical dynamic units, or agents. Analyzing fundamental properties of ensemble control systems in turn plays a foundational and critical role in enabling and, further, advancing these applications, and the analysis is largely beyond the capability of classical control techniques. In this paper, we consider an ensemble of time-invariant linear systems evolving on an infinite-dimensional space of continuous functions. We exploit the notion of separating points and techniques of polynomial approximation to develop necessary and sufficient ensemble controllability conditions. In particular, we introduce an extended notion of controllability matrix, called the ensemble controllability matrix. This enables the characterization of ensemble controllability through evaluating controllability of each individual system in the ensemble. As a result, the work provides a unified framework with a systematic procedure for analyzing control systems defined on an infinite-dimensional space by a finite-dimensional approach.
KW - Ensemble control
KW - Lie algebra
KW - Parameter-dependent systems
KW - Polynomial approximation
KW - Separating points
UR - http://www.scopus.com/inward/record.url?scp=85091992906&partnerID=8YFLogxK
U2 - 10.1137/19M1278648
DO - 10.1137/19M1278648
M3 - Article
AN - SCOPUS:85091992906
SN - 0363-0129
VL - 58
SP - 2740
EP - 2764
JO - SIAM Journal on Control and Optimization
JF - SIAM Journal on Control and Optimization
IS - 5
ER -