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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 19
Self-Repairing Recurrent Ensembles for Real-Time Recovery from Distribution Shift
Deploying a pretrained controller exposes it to conditions that are absent from its training data.Compiling Together: High-Throughput Distributed Quantum Computing via Multi-Compilation
Quantum computing is a promising paradigm for problems that are challenging for classical machines, but realizing that promise requires far more qubits than a single processor can offer.Single or Multiple Policies for Phase-Structured Reinforcement Learning?
Many reinforcement-learning (RL) problems are non-stationary yet structured and can be decomposed into phases, each with its own transition probabilities and reward functions.DR-IPC: Disturbance-Resilient Integrated Planning and Control for LiDAR-Based Quadrotor Navigation
LiDAR-based quadrotor navigation in cluttered environments remains challenging under external disturbances, particularly when obstacle-aware motion generation and disturbance-rejection control are…LLA-MPC on Embedded Hardware: Rapid Adaptive Control with Thousands of Parallel Models
We present a generalized implementation of Look-Back and Look-Ahead Adaptive Model Predictive Control (LLA-MPC), a learning-free framework for real-time, rapid adaptive system identification and…Barrier-Shaped Recurrent Reinforcement Learning for Autonomous Landing on a Heaving Ship Deck
This paper addresses autonomous landing of unmanned aerial vehicle (UAV) rotorcrafts on a heaving ship deck using a recurrent policy trained via asymmetric actor-critic reinforcement learning: the…One-Cycle Fault Classification and Faulted-Line Identification on the PROTECT-90 Dataset: An Initial Application Benchmark
Open electromagnetic-transient datasets are beginning to make reproducible learning-based protection studies possible, but the practical use of these datasets still requires application-level…Learning from Unreliable Trajectories: Adversarially-Robust Federated Q-Learning
We study federated reinforcement learning in which multiple agents interact with a common Markov decision process and communicate through a central server to collaboratively learn the optimal…Closed-Loop Refinement and Execution for Learned Driving Planners
Learning-based driving planners are usually trained and evaluated in open loop against logged trajectories.Fully Online Decentralized Learning in Stochastic Games with Unknown Independent Chains
We consider stochastic games with independent controlled chains and unknown transition kernels, where players observe only their local states and realized payoffs.PROMO: Preference-conditioned Multi-Objective Reinforcement Learning for Quadrupedal Robots
Quadrupedal locomotion requires balancing conflicting objectives such as command tracking, stability, and energy efficiency, yet conventional reinforcement learning (RL) hardcodes these priorities…Reachability-Informed Reinforcement Learning for Multi-Impulse Interplanetary Transfers
Reinforcement learning offers the prospect of a reusable sequential decision-making mechanism for spacecraft trajectory design, motivating policy interfaces that connect learned decisions to the…Port-Hamiltonian Neural Networks for Systems with Multiple Asymptotically Stable Equilibria
Stable port-Hamiltonian neural networks certify asymptotic stability by construction.LLM-Driven Multi-Agent Control for Skill-Based Smart Manufacturing
Factories are shifting toward smaller lot sizes with high product customization, requiring frequent re-programming of flexible and reconfigurable automation systems.Finite-Data Safety Informativity Under Dynamic Asymmetric Actuation
When the system model is not fully known, measurement error and limited excitation can leave several models consistent with the same finite data.Interactive Power Flow in the Browser
This paper introduces tellegen, an open source framework for interactive power flow (PF) and optimal power flow (OPF) studies that run in the browser.Decentralized Power-Optimal Coordination for Spacecraft Swarms Using Time-Varying Magnetorquer Actuation
This paper presents a decentralized power-optimal coordination framework for magnetically actuated spacecraft swarms.Reconstruct, Practice, Go Real: Guided Self-Improvement for Embodied Agents
Building reliable robot capabilities across diverse tasks requires substantial human effort to develop and maintain skills, design rewards, and integrate perception with control.SD-DPC: Sparse Dictionary Differentiable Predictive Control
We present sparse dictionary differentiable predictive control (SD-DPC), a framework for learning sparse, interpretable feedback policies for nonlinear systems from data.Residual Wrench Certification and Margin-Aware Control Synthesis for Aerial Physical Interaction
We develop a task-relative framework for certifying residual wrench authority after hover and contact loading in multirotors with bounded actuators.