Koopman-based feedback design with stability guarantees
We present a method to design a state-feedback controller ensuring exponential stability for
nonlinear systems using only measurement data. Our approach relies on Koopman-operator …
nonlinear systems using only measurement data. Our approach relies on Koopman-operator …
Robust and kernelized data-enabled predictive control for nonlinear systems
This article presents a robust and kernelized data-enabled predictive control (RoKDeePC)
algorithm to perform model-free optimal control for nonlinear systems using only input and …
algorithm to perform model-free optimal control for nonlinear systems using only input and …
Representing turbulent statistics with partitions of state space. Part 1. Theory and methodology
AN Souza - Journal of Fluid Mechanics, 2024 - cambridge.org
This is the first of a two-part paper. We formulate a data-driven method for constructing finite-
volume discretizations of an arbitrary dynamical system's underlying Liouville/Fokker …
volume discretizations of an arbitrary dynamical system's underlying Liouville/Fokker …
[HTML][HTML] Efficient data-driven predictive control of nonlinear systems: A review and perspectives
Abstract Model predictive control (MPC) has become a key tool for optimizing real-time
operations in industrial systems and processes, particularly to enhance performance, safety …
operations in industrial systems and processes, particularly to enhance performance, safety …
Equivariance and partial observations in Koopman operator theory for partial differential equations
The Koopman operator has become an essential tool for datadriven analysis, prediction,
and control of complex systems. The main reason is the enormous potential of identifying …
and control of complex systems. The main reason is the enormous potential of identifying …
Partial observations, coarse graining and equivariance in Koopman operator theory for large-scale dynamical systems
The Koopman operator has become an essential tool for data-driven analysis, prediction
and control of complex systems, the main reason being the enormous potential of identifying …
and control of complex systems, the main reason being the enormous potential of identifying …
Learning Noise-Robust Stable Koopman Operator for Control with Hankel DMD
SA Sakib, S Pan - arxiv preprint arxiv:2408.06607, 2024 - arxiv.org
We propose a noise-robust learning framework for the Koopman operator of nonlinear
dynamical systems, ensuring long-term stability and robustness to noise. Unlike some …
dynamical systems, ensuring long-term stability and robustness to noise. Unlike some …
Dictionary-free Koopman model predictive control with nonlinear input transformation
This paper introduces a method for data-driven control based on the Koopman operator
model predictive control. Unlike existing approaches, the method does not require a …
model predictive control. Unlike existing approaches, the method does not require a …
Enhancing predictive capabilities in data-driven dynamical modeling with automatic differentiation: Koopman and neural ODE approaches
Data-driven approximations of the Koopman operator are promising for predicting the time
evolution of systems characterized by complex dynamics. Among these methods, the …
evolution of systems characterized by complex dynamics. Among these methods, the …
Resolvent-Type Data-Driven Learning of Generators for Unknown Continuous-Time Dynamical Systems
A semigroup characterization, or equivalently, a characterization by the generator, is a
classical technique used to describe continuous-time nonlinear dynamical systems. In the …
classical technique used to describe continuous-time nonlinear dynamical systems. In the …