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Found 9 papers, 0 papers with code
Carleman Lifting for Nonlinear System Identification with Guaranteed Error Bounds
This paper concerns identification of uncontrolled or closed loop nonlinear systems using a set of trajectories that are generated by the system in a domain of attraction.
Singular Dynamic Mode Decompositions
This manuscript is aimed at addressing several long standing limitations of dynamic mode decompositions in the application of Koopman analysis.
The kernel perspective on dynamic mode decomposition
This manuscript revisits theoretical assumptions concerning dynamic mode decomposition (DMD) of Koopman operators, including the existence of lattices of eigenfunctions, common eigenfunctions between Koopman operators, and boundedness and compactness of Koopman operators.
Control Occupation Kernel Regression for Nonlinear Control-Affine Systems
As the fundamental basis elements leveraged in approximation, higher order control occupation kernels represent iterated integration after multiplication by a given controller in a vector valued reproducing kernel Hilbert space.
Occupation Kernel Hilbert Spaces for Fractional Order Liouville Operators and Dynamic Mode Decomposition
This manuscript gives a theoretical framework for a new Hilbert space of functions, the so called occupation kernel Hilbert space (OKHS), that operate on collections of signals rather than real or complex numbers.
Motion Tomography via Occupation Kernels
The goal of motion tomography is to recover the description of a vector flow field using information about the trajectory of a sensing unit.
Optimization and Control Functional Analysis 93-08, 46E22
Theoretical Foundations for the Dynamic Mode Decomposition of High Order Dynamical Systems
Conventionally, data driven identification and control problems for higher order dynamical systems are solved by augmenting the system state by the derivatives of the output to formulate first order dynamical systems in higher dimensions.
Optimization and Control Functional Analysis 93-08, 46E22
Dynamic Mode Decomposition with Control Liouville Operators
A given feedback controller is represented through a multiplication operator and a composition of the control Liouville operator and the multiplication operator is used to express the nonlinear closed-loop system as a linear total derivative operator on RKHSs.
Optimization and Control Functional Analysis 37N35, 93B30
Efficient model-based reinforcement learning for approximate online optimal
In this paper the infinite horizon optimal regulation problem is solved online for a deterministic control-affine nonlinear dynamical system using the state following (StaF) kernel method to approximate the value function.
Model-based Reinforcement Learning
reinforcement-learning
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