Cover Image

Random Features Approximation for Control-Affine Systems

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Kazemian, Kimia, Sattar, Yahya, Dean, Sarah
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Systems and Control Optimization and Control
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Learning Linear Dynamics from Bilinear Observations
by: Sattar, Yahya, et al.
Published: (2024)

Finite Sample Identification of Partially Observed Bilinear Dynamical Systems
by: Sattar, Yahya, et al.
Published: (2025)

Dual Control of Linear Systems from Bilinear Observations with Belief Space Model Predictive Control
by: Cao, Daniel, et al.
Published: (2026)

Sub-optimality of the Separation Principle for Quadratic Control from Bilinear Observations
by: Sattar, Yahya, et al.
Published: (2025)

Explore-then-Commit for Nonstationary Linear Bandits with Latent Dynamics
by: Choi, Sunmook, et al.
Published: (2025)

Identification and Adaptive Control of Markov Jump Systems: Sample Complexity and Regret Bounds
by: Sattar, Yahya, et al.
Published: (2021)

Approximation with Random Shallow ReLU Networks with Applications to Model Reference Adaptive Control
by: Lamperski, Andrew, et al.
Published: (2024)

Control Occupation Kernel Regression for Nonlinear Control-Affine Systems
by: Abudia, Moad, et al.
Published: (2021)

Reinforcement Learning-based Control via Y-wise Affine Neural Networks (YANNs)
by: Braniff, Austin, et al.
Published: (2025)

Reinforcement Learning-based Control via Y-wise Affine Neural Networks: Comparative Case Studies for Chemical Processes
by: Braniff, Austin, et al.
Published: (2026)

Function Gradient Approximation with Random Shallow ReLU Networks with Control Applications
by: Lamperski, Andrew, et al.
Published: (2024)

Randomized Transport Plans via Hierarchical Fully Probabilistic Design
by: Y., Sarah Boufelja, et al.
Published: (2024)

Predictor-Based Output-Feedback Control of Linear Systems with Time-Varying Input and Measurement Delays via Neural-Approximated Prediction Horizons
by: Bhan, Luke, et al.
Published: (2026)

Parameter-Adaptive Approximate MPC: Tuning Neural-Network Controllers without Retraining
by: Hose, Henrik, et al.
Published: (2024)

Universal Approximation Power of Deep Residual Neural Networks via Nonlinear Control Theory
by: Tabuada, Paulo, et al.
Published: (2020)

Forward Invariance in Neural Network Controlled Systems
by: Harapanahalli, Akash, et al.
Published: (2023)

Online Control of Linear Systems under Unbounded Noise
by: Ito, Kaito, et al.
Published: (2024)

A New Approach to Controlling Linear Dynamical Systems
by: Brahmbhatt, Anand, et al.
Published: (2025)

Neural Network-assisted Interval Reachability for Systems with Control Barrier Function-Based Safe Controllers
by: Ajeyemi, Damola, et al.
Published: (2025)

Mutual Information Optimal Control of Discrete-Time Linear Systems
by: Enami, Shoju, et al.
Published: (2025)

Data-Driven Adversarial Online Control for Unknown Linear Systems
by: Liu, Zishun, et al.
Published: (2023)

Provably-Stable Neural Network-Based Control of Nonlinear Systems
by: Li, Anran, et al.
Published: (2025)

On Policy Stochasticity in Mutual Information Optimal Control of Linear Systems
by: Enami, Shoju, et al.
Published: (2025)

RHYME-XT: A Neural Operator for Spatiotemporal Control Systems
by: Ruiter, Marijn, et al.
Published: (2026)

Efficient Spectral Control of Partially Observed Linear Dynamical Systems
by: Brahmbhatt, Anand, et al.
Published: (2025)

Quantitative Flow Approximation Properties of Narrow Neural ODEs
by: Elamvazhuthi, Karthik
Published: (2025)

Neural Approximators for Low-Thrust Trajectory Transfer Cost and Reachability
by: Zhang, Zhong, et al.
Published: (2025)

A PAC-Bayes Approach for Controlling Unknown Linear Discrete-time Systems
by: Luo, Yujia, et al.
Published: (2026)

Distributed Control of Network Systems in the Space of Stabilizing Graph Neural Network Policies
by: Cao, John, et al.
Published: (2025)

Negative Imaginary Neural ODEs: Learning to Control Mechanical Systems with Stability Guarantees
by: Shi, Kanghong, et al.
Published: (2025)

Learning-Based Optimal Control with Performance Guarantees for Unknown Systems with Latent States
by: Lefringhausen, Robert, et al.
Published: (2023)

Contraction-Guided Adaptive Partitioning for Reachability Analysis of Neural Network Controlled Systems
by: Harapanahalli, Akash, et al.
Published: (2023)

Stochastic Approximation with Unbounded Markovian Noise: A General-Purpose Theorem
by: Haque, Shaan Ul, et al.
Published: (2024)

Quasi-Newton Compatible Actor-Critic for Deterministic Policies
by: Kordabad, Arash Bahari, et al.
Published: (2025)

A Simple Finite-Time Analysis of TD Learning with Linear Function Approximation
by: Mitra, Aritra
Published: (2024)

Approximate non-linear model predictive control with safety-augmented neural networks
by: Hose, Henrik, et al.
Published: (2023)

Risk-Averse Ensemble Control for Control-Affine Systems
by: Scagliotti, Alessandro, et al.
Published: (2026)

Mutual Information Optimal Density Control of Linear Systems and Generalized Schrödinger Bridges with Reference Refinement
by: Enami, Shoju, et al.
Published: (2026)

Discrete-time Contraction-based Control of Nonlinear Systems with Parametric Uncertainties using Neural Networks
by: Wei, Lai, et al.
Published: (2021)

Two-Timescale Linear Stochastic Approximation: Constant Stepsizes Go a Long Way
by: Kwon, Jeongyeol, et al.
Published: (2024)