Discrete-time nonlinear feedback linearization via physics-informed machine learning
From MaRDI portal
Publication:6078485
DOI10.1016/j.jcp.2023.112408arXiv2303.08884OpenAlexW4385605323MaRDI QIDQ6078485
Ioannis G. Kevrekidis, Nikolaos Kazantzis, Gianluca Fabiani, Hector Vargas Alvarez, Constantinos I. Siettos
Publication date: 27 September 2023
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2303.08884
feedback linearizationnonlinear discrete time systemsphysics-informed machine learninggreedy training
Artificial intelligence (68Txx) Model systems in control theory (93Cxx) Controllability, observability, and system structure (93Bxx)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Nonlinear model predictive control. Theory and algorithms.
- Remarks on linearization of discrete-time autonomous systems and nonlinear observer design
- Semiglobal stabilization of nonlinear systems using fuzzy control and singular perturbation methods
- Feedback linearization of discrete-time systems
- Automatic differentiation: techniques and applications
- Adaptive nonlinear control without overparametrization
- Nonlinear control systems.
- Approximate feedback linearization of discrete-time non-linear systems using virtual input direct design
- Feedback linearization using neural networks
- Numerical bifurcation analysis of PDEs from lattice Boltzmann model simulations: a parsimonious machine learning approach
- Numerical solution and bifurcation analysis of nonlinear partial differential equations with extreme learning machines
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Discrete-time online learning control for a class of unknown nonaffine nonlinear systems using reinforcement learning
- Uncertainty quantification in scientific machine learning: methods, metrics, and comparisons
- Neural network architectures using min-plus algebra for solving certain high-dimensional optimal control problems and Hamilton-Jacobi PDEs
- Data-driven control of agent-based models: an equation/variable-free machine learning approach
- Applied Koopmanism
- A state space embedding approach to approximate feedback linearization of single input nonlinear control systems
- AN EQUATION-FREE APPROACH TO NONLINEAR CONTROL: COARSE FEEDBACK LINEARIZATION WITH POLE-PLACEMENT
- The immersion under feedback of a multidimensional discrete-time non-linear system into a linear system
- Approximate and local linearizability of non-linear discrete-time systems
- Linearization of discrete-time nonlinear systems and a canonical structure
- Linearization of Discrete-Time Systems
- Time‐steppers and ‘coarse’ control of distributed microscopic processes
- COARSE BIFURCATION DIAGRAMS VIA MICROSCOPIC SIMULATORS: A STATE-FEEDBACK CONTROL-BASED APPROACH
- On Matching, and Even Rectifying, Dynamical Systems through Koopman Operator Eigenfunctions
- State-space realizations of linear differential-algebraic-equation systems with control-dependent state space
- Single-step full-state feedback control design for nonlinear hyperbolic PDEs
- Computing Invariant Manifolds by Integrating Fat Trajectories
- Nonlinear Stabilization via System Immersion and Manifold Invariance: Survey and New Results
- A functional equations approach to nonlinear discrete-time feedback stabilization through pole-placement
This page was built for publication: Discrete-time nonlinear feedback linearization via physics-informed machine learning