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Found 7 papers, 1 papers with code
State-Robust Observability Measures for Sensor Selection in Nonlinear Dynamic Systems
The approach is performed by discretizing the system's dynamics using the implicit Runge-Kutta method and by introducing a state-averaged observability measure.
Data-driven Estimation, Tracking, and System Identification of Deterministic and Stochastic Optical Spot Dynamics
Stabilization, disturbance rejection, and control of optical beams and optical spots are ubiquitous problems that are crucial for the development of optical systems for ground and space telescopes, free-space optical communication terminals, precise beam steering systems, and other types of optical systems.
A novel method for adaptive control of deformable mirrors
Our approach combines a simple open-loop control method with a recursive least squares method for dynamically updating the DM model.
Dual-Update Data-Driven Control of Deformable Mirrors Using Walsh Basis Functions
The developed method updates both the DM model and DM control actions that produce desired mirror surface shapes.
Joint Sensor Node Selection and State Estimation for Nonlinear Networks and Systems
State estimation and sensor selection problems for nonlinear networks and systems are ubiquitous problems that are important for the control, monitoring, analysis, and prediction of a large number of engineered and physical systems.
Steady-State Control and Machine Learning of Large-Scale Deformable Mirror Models
The estimated models reproduce the input-output behavior of Vector AutoRegressive with eXogenous (VARX) input models and can be used for the design of high-performance AO systems.
Subspace Identification of Temperature Dynamics
Our extensive experimental results show that the temperature dynamics of the experimental setup can be relatively accurately estimated by low-order models.
