Cover Image

A Framework for Adaptive Stabilisation of Nonlinear Stochastic Systems

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Siriya, Seth, Zhu, Jingge, Nešić, Dragan, Pu, Ye
Format:	Preprint
Published:	2025
Subjects:	Systems and Control Machine Learning Optimization and Control
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Non-Asymptotic Bounds for Closed-Loop Identification of Unstable Nonlinear Stochastic Systems
by: Siriya, Seth, et al.
Published: (2024)

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

Towards Fast and Safety-Guaranteed Trajectory Planning and Tracking for Time-Varying Systems
by: Siriya, Seth, et al.
Published: (2024)

Sufficient Conditions for Detectability of Approximately Discretized Nonlinear Systems
by: Siriya, Seth, et al.
Published: (2025)

Learning to Sparsify Stochastic Linear Bandits
by: Wang, Zhengmiao, et al.
Published: (2026)

Hierarchical Reinforcement Learning Framework for Stochastic Spaceflight Campaign Design
by: Takubo, Yuji, et al.
Published: (2021)

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)

A Control Theoretic Framework for Adaptive Gradient Optimizers in Machine Learning
by: Chakrabarti, Kushal, et al.
Published: (2022)

Estimation Sample Complexity of a Class of Nonlinear Continuous-time Systems
by: Kuang, Simon, et al.
Published: (2023)

An optimistic planning algorithm for switched discrete-time LQR
by: Granzotto, Mathieu, et al.
Published: (2025)

Neural Contraction Metrics with Formal Guarantees for Discrete-Time Nonlinear Dynamical Systems
by: Li, Haoyu, et al.
Published: (2025)

Hierarchical Decentralized Stochastic Control for Cyber-Physical Systems
by: Kaza, Kesav, et al.
Published: (2025)

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

Neural Operators for Predictor Feedback Control of Nonlinear Delay Systems
by: Bhan, Luke, et al.
Published: (2024)

Denoising Diffusion-Based Control of Nonlinear Systems
by: Elamvazhuthi, Karthik, et al.
Published: (2024)

Data-Driven Motion Planning for Uncertain Nonlinear Systems
by: Esmaeili, Babak, et al.
Published: (2025)

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

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

On the Stability of Nonlinear Receding Horizon Control: A Geometric Perspective
by: Westenbroek, Tyler, et al.
Published: (2021)

Communication-Efficient Stochastic Distributed Learning
by: Ren, Xiaoxing, et al.
Published: (2025)

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

Score Matching Diffusion Based Feedback Control and Planning of Nonlinear Systems
by: Elamvazhuthi, Karthik, et al.
Published: (2025)

Risk-Aware Safe Reinforcement Learning for Control of Stochastic Linear Systems
by: Esmaeili, Babak, et al.
Published: (2025)

Decision-Dependent Stochastic Optimization: The Role of Distribution Dynamics
by: He, Zhiyu, et al.
Published: (2025)

Gradient Estimation and Variance Reduction in Stochastic and Deterministic Models
by: Keane, Ronan
Published: (2024)

A Variance-Reduced Stochastic Gradient Tracking Algorithm for Decentralized Optimization with Orthogonality Constraints
by: Wang, Lei, et al.
Published: (2022)

Probabilistic Safety Guarantee for Stochastic Control Systems Using Average Reward MDPs
by: Omidi, Saber, et al.
Published: (2025)

Optimal Control-Based Baseline for Guided Exploration in Policy Gradient Methods
by: Lyu, Xubo, et al.
Published: (2020)

A Nonlinear Separation Principle via Contraction Theory: Applications to Neural Networks, Control, and Learning
by: Gokhale, Anand, et al.
Published: (2026)

Heavy-Tailed and Long-Range Dependent Noise in Stochastic Approximation: A Finite-Time Analysis
by: Chandak, Siddharth, et al.
Published: (2026)

Evaluation of Prosumer Networks for Peak Load Management in Iran: A Distributed Contextual Stochastic Optimization Approach
by: Noori, Amir, et al.
Published: (2024)

Data-Driven Stochastic Optimal Control in Reproducing Kernel Hilbert Spaces
by: Hoischen, Nicolas, et al.
Published: (2024)

Sampling-Horizon Neural Operator Predictors for Nonlinear Control under Delayed Inputs
by: Bhan, Luke, et al.
Published: (2026)

Adversarially Robust Multitask Adaptive Control
by: Fallah, Kasra, et al.
Published: (2025)

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

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

Non-Parametric Learning of Stochastic Differential Equations with Non-asymptotic Fast Rates of Convergence
by: Bonalli, Riccardo, et al.
Published: (2023)

Almost Surely $\sqrt{T}$ Regret for Adaptive LQR
by: Lu, Yiwen, et al.
Published: (2023)

Online Optimization Perspective on First-Order and Zero-Order Decentralized Nonsmooth Nonconvex Stochastic Optimization
by: Sahinoglu, Emre, et al.
Published: (2024)