Results 71 to 80 of about 13,618 (231)
Estimation of Power System Inertia Using Nonlinear Koopman Modes
, 2018 We report a new approach to estimating power system inertia directly from time-series data on power system dynamics. The approach is based on the so-called Koopman Mode Decomposition (KMD) of such dynamic data, which is a nonlinear generalization of ...
Hamasaki, Ryo +2 more
core +1 more source
On Gaussian Process Based Koopman Operators
IFAC-PapersOnLine, 2020 Abstract Enabling analysis of non-linear systems in linear form, the Koopman operator has been shown to be a powerful tool for system identification and controller design. However, current data-driven methods cannot provide quantification of model uncertainty given the learnt model.
Yingzhao Lian, Colin N. Jones
openaire +1 more source
System norm regularization methods for Koopman operator approximation
Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2022 Approximating the Koopman operator from data is numerically challenging when many lifting functions are considered. Even low-dimensional systems can yield unstable or ill-conditioned results in a high-dimensional lifted space. In this paper, Extended Dynamic Mode Decomposition (DMD) and DMD with control, two methods for approximating the ...
Steven Dahdah, James R. Forbes
openaire +2 more sources
Data‐Driven Modeling of Forces Exerted by Pneumatic Actuators for a Pediatric Exosuit
Advanced Intelligent Systems, EarlyView.This work presents the experimental analysis and data‐driven modeling of the interaction forces between soft pneumatic actuators designed to assist upper‐extremity motion in a pediatric exosuit and an engineered test rig, across different experimental conditions: (A) force profiling of shoulder actuators, with varying actuator anchoring points and ...
Mehrnoosh Ayazi +4 more
wiley +1 more source
On the anti-missile interception technique of unpowered phase based on data-driven theory
Mechanics & Industry, 2022 . The anti-missile interception technique of unpowered phase is of much importance in the military field, which depends on the prediction of the missile trajectory and the establishment of the missile model.
Huang Yong, Li Yang
doaj +1 more source
Data-Driven Approximation of Transfer Operators: Naturally Structured Dynamic Mode Decomposition
, 2017 In this paper, we provide a new algorithm for the finite dimensional approximation of the linear transfer Koopman and Perron-Frobenius operator from time series data.
froyland, klus, lasota, mezi?, surana
core +1 more source
Risk‐aware safe reinforcement learning for control of stochastic linear systems
Asian Journal of Control, EarlyView.Abstract This paper presents a risk‐aware safe reinforcement learning (RL) control design for stochastic discrete‐time linear systems. Rather than using a safety certifier to myopically intervene with the RL controller, a risk‐informed safe controller is also learned besides the RL controller, and the RL and safe controllers are combined together ...
Babak Esmaeili +2 more
wiley +1 more source
Data-Driven Dynamic State Estimation Framework Using a Koopman Operator-Based Linear Predictor
IEEE AccessDynamic state estimation (DSE) is a fundamental task in many fields, including control systems, robotics, and signal processing. Traditional DSE methods, which rely on mathematical models to describe system dynamics, are often limited in their ...
Deyou Yang +4 more
doaj +1 more source
Maximizing Neurovascular Outcomes of Facial Transplantation: A Comprehensive Review
Clinical Anatomy, EarlyView.ABSTRACT Facial transplantation is a division of reconstructive surgery which aims to improve the function and appearance of a face that has endured severe disfigurement. Currently, the face transplant procedure uses allogenic tissue, harvested from a brain‐dead donor, to replace damaged facial components.
Olivia A. James, Faye Bennett
wiley +1 more source
Physics Informed Fully Embedded Koopman Operator-based Optimal Control of Two-Link Robotic System [PDF]
Modeling, Identification and ControlThis paper presents a data-driven framework utilising neural networks to approximate the Koopman operator for a discrete-time representation of an electrically actuated two-link mechanical system, enabling the application of linear control techniques.
Jeppe H. Andersen +3 more
doaj +1 more source