Neural Network Control

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29 papers5 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Learning Direct Control from Optimization for Dynamic Target Tracking
Oct 26, 2026Bing Sun, Yue Lin, Canhui Wen +4Robot Policy Learning Neural Network Control
Certified Neural Approximation of Robust Nonlinear Model Predictive Control
Oct 21, 2026Hendrik Alsmeier, Maik Pfefferkorn, Rolf FindeisenNeural Network Control Nonlinear MPC
Control and Cyber-Physical Systems Laboratory, Technical University of Darmstadt, Landgraf-Georg-Str. 4, 64283 Darmstadt, Germany
Distributed Performance Boosting through Neural Control for Plug-and-Play Networks
Oct 16, 2026Danilo Saccani, Leonardo Massai, Giancarlo Ferrari-TrecateNeural Network Control Distributed Control
Ecole polytechnique federale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland.
History, Adaptation, and Hidden Gain: Two Prospective Tests of Learned Predictive Control in a Synthetic Neural Population
Oct 9, 2026Mahesh Reddy PagadalaNeural Network Control Adaptive Control
Aston University, Birmingham, UK
Micro Neural Policies for Safe Real-Time Robotic Control
Oct 6, 2026Hongpeng Cao, Riccardo Curcio, Daniele Ottaviano +1Robotic Control Statistical Model Checking
School of Engineering and Design, Technical University of Munich, Boltzmannstraße 15, 85748 Garching b. München, Germany · Department of Computer Science, Sapienza University of Rome, Via Salaria 113, 00198 Rome, Italy
Interleaved Projected Gradient Descent for Safe Imitation Learning
Oct 5, 2026Shengfan Cao, Francesco BorrelliGradient Descent Imitation Learning
University of California, Berkeley
Learning Neural Feedback Linearization for Data-driven Systems via Augmented Lagrangian
Sep 21, 2026Lakshmi Priya P. K., Andreas SchwungData-Driven Control Feedback Control
Department of Automation Technology and Learning Systems, South Westphalia University of Applied Sciences,Lübecker Ring 2, Soest, 59494, Germany
Scalable Incremental Robustness Analysis of Neural Network Feedback Systems
Sep 18, 2026Zichen Wang, Peter Seiler, Geir Dullerud +1Neural Network Control Neural Network Robustness
Coordinated Science Laboratory and the Department of Electrical and Computer Engineering, University of Illinois Urbana–Champaign · Department of Electrical Engineering and Computer Science, at the University of Michigan · Electrical and Computer Engineering Department at the University of Minnesota
Learning the Right Abstraction: Neural Reduced Dynamics for Complex Robot Control
Aug 19, 2026Harry Zhang, Dan NegrutRobotic Control Robot Dynamics Learning
Department of Mechanical Engineering, University of Wisconsin–Madison, Madison, 53706, WI, USA
Conditional Invertible Neural Networks for Data-Driven UAV Control: A 2-D Proof of Concept
Jul 15, 2026Christian Wittke, Stephan Myschik, Oliver NiggemannInvertible Neural Networks Quadrotor Control
Computer Science in Mechanical Engineering, Helmut Schmidt University, Hamburg, Germany. · Institute for Aeronautical Engineering, University of the Bundeswehr Munich, Bavaria, Germany
Implicit Neural Networks as Static Controllers: Certificates and Performance Separation
Jul 13, 2026Giuseppe C. Calafiore, Laurent El GhaouiImplicit Neural Representations Neural Network Control
Neural-ESO: A Dual-Pathway Architecture for Provably Robust Learning-Based Control
Jul 7, 2026Fan Zhang, Richie Suganda, Jinfeng Chen +4Robust Control Neural Network Control
Department of Engineering Technology, University of Houston, USA · Department of Electrical and Computer Engineering, University of Houston, USA · Department of Electrical Engineering, Rice University, USA
ShardNet: Training Neural Controllers with Hard, Non-Convex Constraints
Jun 29, 2026Long Kiu Chung, Shreyas KousikNeural Network Verification Safety-Critical Control
Department of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA.
Neural Architectures as Functional Priors in Physics-Informed Control Problems
Jun 11, 2026Sonia Rubio Herranz, Fernando Carlos López Hernández, Antonio López MontesDynamical Systems Ordinary Differential Equations
Department of Statistics and Operations Research (EIO), Universidad Complutense de Madrid, 28040 Madrid, Spain · Department of Software Engineering and Artificial Intelligence, Universidad Complutense de Madrid, 28040 Madrid, Spain · Department of Analysis and Applied Mathematics, Universidad Complutense de Madrid, 28040 Madrid, Spain
Amortized Nonlinear Model Predictive Control
Jun 4, 2026Francesco Pillitteri, Alberto BemporadRobotic Control Nonlinear MPC
IMT School for Advanced Studies, Lucca, Italy
Bridging Control with Neural Network Verifier alpha-beta-CROWN: A Tutorial
May 26, 2026Haoyu Li, Xiangru Zhong, Hao Cheng +2Neural Network Verification Neural Network Control
Department of Computer Science · Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
Parallel Differentiable Reachability for Learning and Planning with Certified Neural Dynamics and Controllers
May 25, 2026Keyi Shen, Glen ChouRobotic Control Neural Network Verification
Georgia Institute of Technology, Atlanta, GA 30308
Bilinear Mamba-Koopman Neural MPC for Varying Dynamics
May 6, 2026Matan Pagi, Zohar SorekOptimal Control Dynamical Systems
Psistar AI
Neural Control: Adjoint Learning Through Equilibrium Constraints
May 5, 2026Dezhong Tong, Jiawen Wang, Hengyi Zhou +3Robotic Control Dynamical Systems
The Robotics Department, University of Michigan, Ann Arbor, US · The Department of Mechanical and Aerospace Engineering, University of California, Los Angeles, US
Neural Network-Based Adaptive Event-Triggered Control for Dual-Arm Unmanned Aerial Manipulator Systems
Apr 18, 2026Yang Wang, Hai Yu, Wei He +3Robot Trajectory Tracking Aerial Robotics
Institute of Robotics and Automatic Information System, College of Artificial Intelligence, and Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin 300350, China. · Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Shenzhen 518083, China.
A Nonlinear Separation Principle via Contraction Theory: Applications to Neural Networks, Control, and Learning
Apr 16, 2026Anand Gokhale, Anton V. Proskurnikov, Yu Kawano +1Dynamical Systems Contraction Theory
Center for Control, Dynamical Systems, and Computation, UC Santa Barbara, Santa Barbara, CA 93106 USA · Department of Electronics and Telecommunications, Politecnico di Torino, Italy, 10129 · Graduate School of Advanced Science and Engineering, Hiroshima University, Higashi-Hiroshima 739-8527, Japan
Neural Operators for Multi-Task Control and Adaptation
Apr 3, 2026David Sewell, Xingjian Li, Stepan Tretiakov +2Neural Network Control Optimal Control
The University of Texas at Austin
Neural Robust Control on Lie Groups Using Contraction Methods (Extended Version)
Apr 1, 2026Yi Lok Lo, Longhao Qian, Hugh H. T. LiuNeural Network Control Robust Control
The authors are with the Institute for Aerospace Studies, University of Toronto, 4925 Dufferin St, North York, ON M3H 5T5, Canada
Hebbian Attractor Networks for Robot Locomotion
Mar 23, 2026Alexander Dittrich, Fuda van Diggelen, Dario FloreanoHebbian Learning Neural Network Training Dynamics
Laboratory of Intelligent Systems, EPFL, Switzerland
Stability-Preserving Online Adaptation of Neural Closed-loop Maps
Mar 23, 2026Danilo Saccani, Luca Furieri, Giancarlo Ferrari-TrecateNeural Network Control Feedback Control
Institute of Mechanical Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland. · Department of Engineering Science, University of Oxford, United Kingdom
Generalizable Optimal Control with Transformers: One Policy Across Diverse Systems
Mar 16, 2026Turki Bin Mohaya, Maitham F. AL-Sunni, John M. Dolan +1Optimal Control Neural Network Control
Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA
Data-Driven Physics Embedded Dynamics with Predictive Control and Reinforcement Learning for Quadrupeds
Mar 15, 2026Prakrut Kotecha, Aditya Shirwatkar, Shishir KolathayaLagrangian Neural Networks RL for Legged Locomotion
the Robert Bosch Center for Cyber-Physical Systems, Indian Institute of Science, Bengaluru. · the Robert Bosch Center for Cyber-Physical Systems and the Department of Computer Science & Automation, Indian Institute of Science, Bengaluru.
Learn2Drive: A neural network-based framework for socially compliant automated vehicle control
Sep 30, 2025Yuhui Liu, Samannita Halder, Shian Wang +1Intelligent Transportation Systems Autonomous Driving
Department of Civil Engineering Saint Louis University · Department of Electrical and Computer Engineering Saint Louis University · Department of Civil, Environmental, and Architectural Engineering The University of Kansas
Certified Robust Invariant Polytope Training in Neural Controlled ODEs
Aug 2, 2024Akash Harapanahalli, Samuel CooganNeural Controlled Differential Equations Robust Control
School of Electrical and Computer Engineering, Georgia Institute of Technology

Oct 26, 2026J/K move · Enter open · S save

Learning Direct Control from Optimization for Dynamic Target Tracking

Bing Sun, Yue Lin, Canhui Wen, Xin Chen+3

Robot Policy Learning Neural Network Control

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Dynamic target tracking requires a mobile robot to maintain target visibility while navigating obstacles with smooth, feasible controls. Online trajectory optimization explicitly encodes these objectives but requires repeated numerical solving, whereas waypoint-based neural policies separate geometric prediction from control execution. We introduce LECO, a framework that transfers tracking optimization into offline learning of a direct-control policy. LECO predicts control sequences and maps them to trajectories through a differentiable kinematic model, allowing trajectory-level objectives to directly guide control learning. A decaying expert-guidance curriculum initializes effective tracking behavior and progressively shifts learning toward these task objectives. At deployment, a predictive safety lock checks short-horizon obstacle clearance and invokes a classical planning and control fallback when the predicted motion is unsafe or the target prediction is invalid. Across three simulated obstacle densities, LECO reduces root-mean-square linear jerk by 14.1--31.2% relative to the best competing method for this metric and achieves the highest tracking success rate, with per-cycle computation times of 4.25--5.63,ms. Experiments on two robot platforms further demonstrate tracking through indoor narrow passages and outdoor forest environments. These results demonstrate that learning direct control from optimization enables smooth and robust dynamic target tracking.