Cover Image

Data-Driven Adaptive PID Control Based on Physics-Informed Neural Networks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Ito, Junsei, Wasa, Yasuaki
Format:	Preprint
Published:	2025
Subjects:	Systems and Control Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Model Predictive Path Integral PID Control for Learning-Based Path Following
by: Kato, Teruki, et al.
Published: (2026)

Adaptive Model-Predictive Control of a Soft Continuum Robot Using a Physics-Informed Neural Network Based on Cosserat Rod Theory
by: Licher, Johann, et al.
Published: (2025)

Formally Verified Physics-Informed Neural Control Lyapunov Functions
by: Liu, Jun, et al.
Published: (2024)

A Physics-Informed Neural Networks-Based Model Predictive Control Framework for $SIR$ Epidemics
by: Zhong, Aiping, et al.
Published: (2025)

Bayesian Physics Informed Neural Networks for Reliable Transformer Prognostics
by: Ramirez, Ibai, et al.
Published: (2025)

Scalable Physics-Informed Neural Differential Equations and Data-Driven Algorithms for HVAC Systems
by: Zhai, Hanfeng, et al.
Published: (2026)

RobustNeuralNetworks.jl: a Package for Machine Learning and Data-Driven Control with Certified Robustness
by: Barbara, Nicholas H., et al.
Published: (2023)

Physics-Informed Graph Neural Network for Dynamic Reconfiguration of Power Systems
by: Authier, Jules, et al.
Published: (2023)

Physics-Informed Neural Networks for Accelerating Power System State Estimation
by: Falas, Solon, et al.
Published: (2023)

Physics-Informed Heterogeneous Graph Neural Networks for DC Blocker Placement
by: Jin, Hongwei, et al.
Published: (2024)

Physics-Informed Neural Network Lyapunov Functions: PDE Characterization, Learning, and Verification
by: Liu, Jun, et al.
Published: (2023)

Optimal Sensor Placement in Power Transformers Using Physics-Informed Neural Networks
by: Li, Sirui, et al.
Published: (2025)

Robust Power System State Estimation using Physics-Informed Neural Networks
by: Falas, Solon, et al.
Published: (2025)

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

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

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

Verifiable Error Bounds for Physics-Informed Neural Network Solutions of Lyapunov and Hamilton-Jacobi-Bellman Equations
by: Liu, Jun
Published: (2026)

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

Physics-Informed Deep B-Spline Networks
by: Wang, Zhuoyuan, et al.
Published: (2025)

Usage-Specific Survival Modeling Based on Operational Data and Neural Networks
by: Holmer, Olov, et al.
Published: (2024)

Verifiable Error Bounds for Physics-Informed Neural KKL Observers
by: Berin-Costain, Hannah, et al.
Published: (2026)

Adaptive Path Integral Diffusion: AdaPID
by: Chertkov, Michael, et al.
Published: (2025)

Symptom-Driven Personalized Proton Pump Inhibitors Therapy Using Bayesian Neural Networks and Model Predictive Control
by: Li, Yutong, et al.
Published: (2025)

Autonomous Vehicle Lateral Control Using Deep Reinforcement Learning with MPC-PID Demonstration
by: Wu, Chengdong, et al.
Published: (2025)

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

Learning Transferable Friction Models and LuGre Identification Via Physics-Informed Neural Networks
by: Ozmen, Asutay, et al.
Published: (2025)

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

A Digital Twin for Diesel Engines: Operator-infused Physics-Informed Neural Networks with Transfer Learning for Engine Health Monitoring
by: Nath, Kamaljyoti, et al.
Published: (2024)

Graph Neural Network-Based Distributed Optimal Control for Linear Networked Systems: An Online Distributed Training Approach
by: Song, Zihao, et al.
Published: (2025)

Pontryagin Optimal Control via Neural Networks
by: Gu, Chengyang, et al.
Published: (2022)

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

Component-Aware Pruning Framework for Neural Network Controllers via Gradient-Based Importance Estimation
by: Sundaram, Ganesh, et al.
Published: (2026)

PID Accelerated Temporal Difference Algorithms
by: Bedaywi, Mark, et al.
Published: (2024)

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

Physics Informed Reinforcement Learning with Gibbs Priors for Topology Control in Power Grids
by: Dogoulis, Pantelis, 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)

FPGA-Based Neural Thrust Controller for UAVs
by: Azem, Sharif, et al.
Published: (2024)

Inverse Modeling of Dielectric Response in Time Domain using Physics-Informed Neural Networks
by: Esenov, Emir, et al.
Published: (2025)

PINNSim: A Simulator for Power System Dynamics based on Physics-Informed Neural Networks
by: Stiasny, Jochen, et al.
Published: (2023)

Direct Data Driven Control Using Noisy Measurements
by: Esmzad, Ramin, et al.
Published: (2025)