TY - GEN
T1 - Inferring System and Optimal Control Parameters of Closed-Loop Systems from Partial Observations
AU - Geadah, Victor
AU - Arbelaiz, Juncal
AU - Ritz, Harrison
AU - Daw, Nathaniel D.
AU - Cohen, Jonathan D.
AU - Pillow, Jonathan W.
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - We consider the joint problem of system identification and inverse optimal control for discrete-time stochastic Linear Quadratic Regulators. We analyze finite and infinite time horizons in a partially observed setting, where the state is observed noisily. To recover closed-loop system parameters, we develop inference methods based on probabilistic statespace model (SSM) techniques. First, we show that the system parameters exhibit non-identifiability in the infinite-horizon from closed-loop measurements, and we provide exact and numerical methods to disentangle the parameters. Second, to improve parameter identifiability, we show that we can further enhance recovery by either (1) incorporating additional partial measurements of the control signals or (2) moving to the finitehorizon setting. We further illustrate the performance of our methodology through numerical examples.
AB - We consider the joint problem of system identification and inverse optimal control for discrete-time stochastic Linear Quadratic Regulators. We analyze finite and infinite time horizons in a partially observed setting, where the state is observed noisily. To recover closed-loop system parameters, we develop inference methods based on probabilistic statespace model (SSM) techniques. First, we show that the system parameters exhibit non-identifiability in the infinite-horizon from closed-loop measurements, and we provide exact and numerical methods to disentangle the parameters. Second, to improve parameter identifiability, we show that we can further enhance recovery by either (1) incorporating additional partial measurements of the control signals or (2) moving to the finitehorizon setting. We further illustrate the performance of our methodology through numerical examples.
UR - http://www.scopus.com/inward/record.url?scp=86000586412&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=86000586412&partnerID=8YFLogxK
U2 - 10.1109/CDC56724.2024.10886179
DO - 10.1109/CDC56724.2024.10886179
M3 - Conference contribution
AN - SCOPUS:86000586412
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 8006
EP - 8013
BT - 2024 IEEE 63rd Conference on Decision and Control, CDC 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 63rd IEEE Conference on Decision and Control, CDC 2024
Y2 - 16 December 2024 through 19 December 2024
ER -