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Systems & Control
The newest papers in Systems & Control that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
703 papers, from 31 August 2026 to 5 November 2026: 644 arXiv preprints and 59 OpenAlex records.
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Newest papers, page 22
Fully Decentralized and Safety-Aware Multi-Agent Reinforcement Learning for Control on Networks
This paper develops a safe and fully decentralized multi-agent reinforcement learning (MARL) algorithm to solve a class of discrete-time control problems on networks, including the persistent…Bilinear World Models: Learning Representations with Structured Dynamics for Efficient Control
World models jointly learn latent representations and dynamics that predict how high-dimensional observations evolve under actions.Notes on Generative Modeling for Feedback Control and Planning
In these notes, we view control as a dynamically constrained sampling problem on the state-space of a control system.Feedback Makes Perfect: A Closed-Loop Framework for NL-to-STL Translation
Signal Temporal Logic (STL) enables rigorous verification and control of cyber-physical systems, but writing correct specifications requires expertise that most requirement holders lack.A Disk-Shaped Magnetoelastic Torque Sensor for Robotic Joints Using Permanent Magnetization
Direct torque sensing is a growing need in the robotics community to enable precise control and interactions where torque estimation from motor current is not sufficient.lapanda: A Matrix-Free Differentiable Solver for Nonconvex Constrained Optimization Layers
Differentiable optimization brings the structural guarantees of mathematical optimization to network pipelines, allowing them to be trained end-to-end.Hierarchical Multi-agent Reinforcement Learning for Warehouse Robot Coordination under Communication Loss
In this paper, we propose a hierarchical multi-agent reinforcement learning framework for coordinating robot teams in warehouse environments under communication loss.Concurrent Coded Signal-Multiplexing Ranging for Half-Duplex Asynchronous Networks
Signal-multiplexing network ranging (SM-NR) shares broadcasts across node pairs, but its sequential operation leads to a ranging cycle that grows linearly with network size.Residual Learning-Based Control of Vehicle Platoons with $\ell_2$ Stability Guarantees via Recurrent Equilibrium Networks
This paper proposes a residual learning-based control framework for heterogeneous vehicle platoons subject to parametric uncertainty and external disturbances.Uniqueness of convex central configurations in the planar four-body problem
We prove that for every choice of four positive masses and every cyclic ordering of the bodies there is exactly one strictly convex planar central configuration with that ordering, up to similarity.Robust Game-theoretic Motion Planning over Extended Time Horizons
This work presents a solution to nonconvex, game-theoretic motion planning problems subject to disturbances over long time horizons.AI Harness: Certification under Proposal-Conditioned Information for Foundation-Model Agents
Foundation-model agents are often modeled as policies over an observed state.Neural ODEs Meet Concurrent Learning: Stable Online Learning with Lyapunov Guarantees
Neural ODEs learn dynamics from trajectory losses, but their adjoint gradients lack the regressor-times-parameter-error structure on which Lyapunov analyses of online adaptation rest, so training on…All On-Board: Fully On-Chip Neuromorphic Q-Learning with Embedded CartPole Simulation
As AI models grow in size and usage, their energy demands increase dramatically, raising sustainability and economic concerns.What Do Latent Predictive Vehicle Representations Retain? Measuring State, Geometry, and Local Response
Models of vehicle dynamics learned from logged states and commands complement physics-based models, and latent world models, which predict in a learned representation, are used to plan and train…Schur-Neural KF: Learned Schur-Consistent Corrections to the Extended Kalman Filter
We present Schur-Neural KF (SN-KF), a learning-based correction to the extended Kalman filter (EKF) that preserves the probabilistic conditioning interpretation of the EKF.Learning Vision-Based Agile Gap Traversal: Differentiable Simulation with a Warm-Started Critic
Traversing narrow gaps is challenging for autonomous quadrotors, especially when control commands come directly from high-dimensional visual observations.Moving Horizon Estimation for Quadrotors: An $\mathcal{L}_1$ Adaptive Optimizer Approach
Moving Horizon Estimation (MHE) is a state estimation method based on finite-horizon optimization that can offer higher accuracy at the cost of increased computation compared to Kalman filter-based…SeA-RVINS: Semantic-Aware Tightly Coupled RTK-Visual-Inertial System with Correlation-Preserving Robust Estimation for Urban Navigation
Reliable absolute pose estimation in urban environments is undermined by outlier measurements and incorrect temporal associations that can persist in tightly coupled estimators.AC Power Flow Contingency Analysis Using a Single Deep Neural Network
Contingency analysis using the AC power flow (AC-PF) model is a critical tool for accurate grid security assessment, but its computational burden increases with the number of operating scenarios and…