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cs.SY

cs.SY: explore 409 source-linked works published from 2017 to 2026, with original documents and citations.

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Includes records with this source-supplied label or an explicit phrase match in their metadata. Matches indicate a mention, not proof that a paper uses a method or tests a material. Source versions are consolidated by DOI.

Sources: arxiv. Collection updated 2026-09-16. Counts describe this index, not the complete source archives.

arXiv2026

Effective Range and Optimal Frequency of Through-the-Earth Magnetic Induction Communication

Magnetic induction communication (MIC) is a promising technology for through-the-earth (TTE) communication. Previous studies on the MIC range have often overlooked the impact of eddy losses caused by underground materials. For TTE MIC, significant eddy losses complicate the analysis of the effective MIC range, which is vital for optimizing performance but has never been addressed in the literature. Accounting for the conductivity and permittivity of the underground medium, this paper derives the effective MIC range in TTE MIC, along with a closed-from expression that predicts the optimal carrier frequency to maximize this range. Finite element simulations validate the analysis, demonstrating that the optimal carrier frequency can significantly enhance the MIC range. It is also revealed that optimizing the antenna radius is effective in extending the MIC range for TTE and vehicle MIC applications.

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arXiv2026

Physics-based Online Adaptive Koopman Model Predictive Attitude Control for Combined Spacecraft with Dynamic Uncertainties

This paper proposes a physics-based adaptive Koopman Model Predictive Control (MPC) strategy for combined spacecraft attitude stabilization under inertia uncertainties and active target maneuverability. A novel, quaternion-based Koopman model is constructed from a set of analytical lifting functions derived from the quaternion kinematics, which provides a more compact and physically interpretable linear representation of the nonlinear dynamics compared with the conventional black-box EDMD and higher-dimensional DCM-based model. Leveraging the linear structure of this nominal model, a gradient descent-based update law is employed to efficiently identify time-varying inertial uncertainties from real-time input/output data. By integrating this adaptive linear model into the MPC framework, the optimal control problem reduces to a computationally efficient Quadratic Program (QP), thereby significantly lowering the online computational burden compared to nonlinear adaptive MPC. Recursive feasibility and regional input-to-state stability are formally established through the design of terminal ingredients for the MPC. The effectiveness and superiority of the proposed strategy are validated through comparative simulations of an attitude stabilization task for combined spacecraft in a high-fidelity 3D simulator.

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arXiv2026

Pre-Lane-change Signal in Transitional Autonomous Vehicles: Results from Controlled Experiments

This paper investigates how a production transitional autonomous vehicle (tAV) develops and executes mandatory lane-change decisions. Using 150 controlled mandatory lane changes from the NC-tALC experiments, the study examines whether the eventual target gap is observable before lateral movement begins and how the tAV progresses longitudinally from that pre-lane-change state to lane-change start. Signal time (SigT) is defined as an operational pre-lane-change-start reference point. A Firth logistic regression predicts whether the tAV eventually merges in front of or behind its nearest target-lane vehicle using relative position and relative speed at SigT. Longitudinal progression from SigT to lane-change start is then examined separately for in-position and repositioning cases. The traffic state at SigT contains substantial information about eventual target-gap choice and provides meaningful lead time before lateral movement begins. The proposed formulation predicts whether the tAV remains with its current gap or repositions to a neighboring gap by moving forward or dropping back, including cases with longitudinal overlap and ambiguous current-gap geometry. The model achieves an average five-fold cross-validated accuracy of 0.89. Results also provide preliminary evidence that in-position and repositioning cases follow different longitudinal pathways from SigT to lane-change start. These findings support a two-stage conjecture of the observable lane-change process: longitudinal preparation from SigT to lane-change start, followed by lateral maneuver execution. The formulation applies to in-position, repositioning, and longitudinally overlapping cases, and can support lane-change models that distinguish target-gap choice from lateral-onset timing while representing longitudinal preparation before lateral movement begins.

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arXiv2026

Zonotope-Based Active Exposure of Stealthy Deception Attacks in Sensor-Fusion Systems

This paper investigates the stealthy attack detection for sensor-fusion cyber-physical systems with unknown-but-bounded noises through the control channel. The detection framework is particularly applicable to sensor-fusion scenarios in which multiple suspicious sensors contributing to the fused estimate may be compromised simultaneously. First, we construct an admissible output set using secure sensors and an attack output set for each attack hypothesis. Then, we introduce a receding-horizon optimization framework to design exposure inputs, namely bounded auxiliary control perturbations injected through the control channel, so as to enlarge the separation between the admissible output set and the attack output sets according to the separation tendency. A sufficient detection condition is further derived, showing that set separation guarantees detectability of the compromised sensors. Moreover, an offline exposure budget guidance is developed to support budget selection before online exposure starts. Simulations on a UAV navigation system under stealthy GNSS and LiDAR attacks validate the proposed method.

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arXiv2026

Economic Model Predictive Control with Policy-Guided Terminal Ingredients

Conventional designs for model predictive control typically rely on terminal costs and constraints derived from a steady state to guarantee closed-loop stability and performance. However, this dependence on a steady-state assumption limits the applicability of this control method to systems in which such a fixed operating point is either not available or not desirable. This work introduces a novel framework, termed policy-guided MPC, to address this limitation. Our approach constructs terminal costs and constraints using a known sub-optimal control policy. Specifically, the terminal region is defined around a center determined by a rollout of the policy, and a penalty on deviation from this center is used to define the terminal cost. This method obviates the need for a steady state or reference trajectory. Closed-loop performance guarantees are established relative to the guiding policy, for both finite and infinite horizon problems. The effectiveness of the proposed framework is demonstrated through numerical simulations on an energy management example.

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arXiv2026

A Dry-Contact Ear-EEG System With Continuous Electrode-Skin Impedance Mismatch Monitoring for Motion Artifact Cancellation Using DRL Stimulus

Dry-contact ear-electroencephalography (Ear-EEG) enables wearable neural monitoring. However, motion induced electrode-skin impedance (ESI) mismatches between electrodes can severely degrade signal quality. To the best of our knowledge, this paper presents the first proof-of-concept dry-contact EarEEG system that uses a driven-right-leg (DRL) stimulus for continuous ESI mismatch monitoring, enabling online adaptive motion artifact cancellation. A 1 kHz sinusoidal stimulus is injected through the DRL electrode. The resulting response to the injected carrier is separated from the EEG using bandpass filtering and demodulation, and then used to extract the ESI mismatch information as the reference input for a normalized least-mean-square adaptive filter followed by a Hampel filtering stage. To evaluate artifact suppression and preservation of neural activity, alpha-band EEG activity was analyzed involving four healthy participants performing head nodding, electrode tapping, and jaw clenching. The system achieved artifact power reductions of 6.5, 12.6, and 9.0 dB (77.6%, 92.8%, and 86.4%, respectively) while alpha-band modulation remained clearly observable after processing. This demonstrates the feasibility of DRL-stimulusbased ESI mismatch monitoring for motion artifact cancellation in wearable dry-contact Ear-EEG.

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arXiv2026

Guidelines for the implementation of power oscillation damping controllers in power converters

One of the most effective ways to damp electromechanical oscillations in power systems is by means of supplementary controllers attached to the different devices in the power system is by means of power system stabilizers (PSS) in synchronous machines or by means of power oscillation damping (POD) controllers in facilities with power converters. In the recent years, the use of POD controllers in voltage source converters (VSCs) with grid-following (GFL) control has been investigated. Although the potential of POD controllers to help to damp inter-area oscillations in power systems is enormous, their correct implementation is not trivial, because their effectiveness is strongly linked to their settings. This paper provides guidelines for the implementation of POD controllers in power converters for application in real-world power systems. The paper proposes compliance criteria for POD controllers using a synthetic test system and a systematic methodology used in Spanish technical standard for monitoring compliance (NTS), considering practical considerations. The paper also includes numerical examples to illustrate compliance criteria for POD controllers in a synthetic test system. A generic power converter with grid-following (GFL) control is used for the analysis by simulation and POD controllers using modulation of active-power injection (POD-P), reactive-power injection (POD-Q) or both simultaneously (POD-PQ) will be analysed. Results were validated in a large-scale power system. The paper concludes that by using appropriate synthetic systems, methodologies and compliance criteria, POD controllers in power converters could be effective to damp electromechanical oscillation in large-scale power systems.

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arXiv2026

Tests on the POD-P controller of INELFE Spain-France VSC-HVDC interconnector

INELFE interconnector consists of a 2x1000 MW high voltage direct current system based on voltage source converters (VSC-HVDC) interconnecting France and Spain. INELFE VSC-HVDC link is embedded into the high voltage alternating current (HVAC) system. Electromechanical oscillations, also known as power oscillations, are a major concern worldwide. INELFE VSC-HVDC link has specific controllers to damp power oscillations by modulating active (P)- and reactive (Q)-power injections of the VSC converters (POD-P and POD-Q controllers, respectively). The Spanish and French Transmission System Operators (TSOs) carried out a join task force to - Increase the gain of POD-P controller of INELFE VSC-HVDC link. - Make it possible to use the POD-P controller together with angle difference control (ADC) of INELFE VSC-HVDC interconnector. The objective of these modifications is to improve the effectiveness of POD-P controller and, therefore, to increase its contribution to the damping of inter-area oscillations in the Continental Europe (CE) power system. Such changes require (a) extensive simulation studies and (b) extensive tests in different operation modes, in order to ensure the correct behavior of the system. This paper presents simulation studies and field tests on the POD-P controller of INELFE VSC-HVDC interconnector in different modes of operation.

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arXiv2026

Hosting Capacity Assessment of Data Centers with Voltage Ride-Through Capability in Power Systems

Large data centers are emerging as concentrated, power-electronic grid loads whose abrupt disconnection or transfer to on-site backup supply during voltage disturbances can remove large demand from the power system, and may create a system-level stability problem. Their interconnection feasibility therefore depends not only on steady-state thermal and voltage limits, but also on whether internal power-conditioning systems can maintain IT service while limiting customer-initiated load reduction. This paper presents a voltage ride-through (VRT)-aware data center and grid co-planning framework that couples transmission-level fault simulation with an internal data center ride-through model. Python-based dynamic simulations generate point-of-interconnection (POI) voltage trajectories under selected network faults, and the resulting waveforms drive an internal model incorporating IT and cooling-load dynamics, DC-link, Uninterruptible Power Supply (UPS) response, and converter apparent power limits. The IEEE 118-bus case study shows that internal VRT capability can become a binding interconnection constraint: steady-state planning alone can overestimate feasible data center capacity, whereas increased UPS converter headroom progressively restores hosting capacity. Under the reduced-order response models studied, the grid-forming mode provides greater ride-through margin than the current-limited grid-following mode under the same network fault conditions. The results further show that VRT constraints can materially change both the total hosting capacity of data centers and its spatial allocation across candidate interconnection buses.

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arXiv2026

Dynamic Operational Reserve Margin Assessment from Risk-Constrained Unit Commitment States

We propose Dynamic Reserve Margin (DRM) as a time-varying operational adequacy metric derived from risk-constrained unit commitment (RCUC) states. DRM quantifies reserve adequacy using the additional generation capacity that committed generators can provide within a 5-minute response window relative to uncertainty and contingency reserve requirements. We introduce a complementary Reserve Risk Envelope (RRE) metric to quantify operational reserve headroom in directly interpretable MW terms. A low-margin duration metric is further developed to quantify the persistence of reserve stress over an operating horizon. We use an IEEE 14-bus example to illustrate these concepts, followed by large-scale RCUC case studies under multiple operating scenarios. Results demonstrate that reserve requirements and ramp-accessible reserve capability can vary substantially across operating conditions, and that commitment decisions adapt to maintain reserve adequacy under changing system conditions. The proposed DRM and RRE metrics provide an interpretable operational characterization of reserve adequacy, reveal reserve accessibility and stress persistence that are not captured by conventional reserve margin metrics.

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arXiv2026

GPU-Accelerated Astrodynamics World Models for Spacecraft Rendezvous and Proximity Operations

World models are an emerging paradigm in representation learning in which an agent jointly learns state-action dynamics and observation models from offline trajectory data, enabling multi-step planning and trajectory prediction with uncertainty estimates. They have shown strong results in robotics and game environments, but, to the best of our knowledge, have not previously been applied to the space domain. This paper introduces a world model-based approach to cooperative and non-cooperative spacecraft rendezvous and proximity operations. First, we introduce an open-source, JAX-based International Space Station (ISS) docking environment supporting parallel GPU simulation of spacecraft orbit and attitude dynamics, generating the thousands of state-action transitions that world model training requires. Second, we introduce Out-of-this-World-Model, a transformer-based world model that encodes relative kinematic states and body-fixed camera imagery into a latent state and predicts its evolution under commanded thrusts and torques using one-step flow matching. It produces a distribution over future observations, capturing stochastic dynamics and per-timestep uncertainty, and outperforms DreamerV3-style posterior-correction baselines with fewer trainable parameters and hyperparameters. Third, we apply the approach to a capsule autonomously docking with the ISS under keep-out-zone constraints, demonstrating improved sample efficiency and task performance over reinforcement learning baselines (53% versus 29% docking success across ports), better out-of-distribution generalization (on held-out ports the world model more than doubles baseline success, 40% versus 17%), and detection of anomalous objects encountered during approach with 98% classification accuracy. We open-source the simulation environment and model architecture to enable further study of this paradigm.

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arXiv2026

Finite-Sample Limits of Entropy-Based Structure Identification in Discretized Nonlinear Systems

Discretization fundamentally limits structure identification in stochastic systems. When system stochasticity exceeds the discretization resolution, entropy-based methods lose their ability to distinguish which input drives the output. We study this in Fuzzy Inductive Reasoning (FIR), a nonparametric framework for learning dynamical systems from discretized measurements, where the choice of input variables determines both predictive accuracy and the interpretability of the learned input--output relationships. Entropy-based selection targets explainability, i.e., identifying which variables causally drive the output, while mean-squared-error-based selection targets prediction. We introduce a resolution-stochasticity ratio that governs when entropy-based selection is reliable. Three results follow. First, entropy-based selection is consistent below this threshold but loses discriminative power above it, regardless of sample size. Second, using the entropy-selected variables for prediction instead of the MSE-selected ones incurs a closed-form excess prediction risk that grows with input complexity and shrinks with sample size. Third, reliable identification of the causally relevant inputs requires data that scales with the number of input combinations and inversely with the strength of the entropy signal. The theory is validated on a two-state Markov model and demonstrated on a distribution grid reliability dataset analyzing the impact of infrastructure investment, where the goal is to explain which investments drive reliability improvements rather than merely predict outcomes.

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arXiv2026

Structural Compatibility and Uniform Stability of Temporally Degenerate Parabolic Systems

Modern feedback design for distributed parameter systems presupposes that the closed-loop dynamics define a well-posed evolution problem. This presupposition becomes nontrivial for temporally degenerate parabolic systems, where temporal degeneracy affects not only the analytical properties of the evolution equation but also the mathematical formulation of the feedback interconnection itself. It is shown that admissible feedback interconnections for temporally degenerate parabolic systems are completely characterized by an operator compatibility condition linking the singular reaction operator with the actuator and observation operators. This characterization removes the singular component of the closed-loop dynamics and reduces the degenerate evolution equation to a regular evolution equation. Building upon this regularized formulation, a critical--residual decomposition yields a uniform exponential stability certificate, which is subsequently extended to the original infinite-dimensional evolution through a finite-to-infinite lifting theorem. A constructive static output feedback synthesis is finally obtained as a consequence of these results. Numerical experiments illustrate the regularization mechanism, validate the stability certificate, and confirm the finite-to-infinite lifting.

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arXiv2026

A Reconfigurable Hybrid Convolutional-Fully Connected Neuromorphic Core for Biomedical Edge Inference

This work presents a programmable FPGA-based architecture for spiking convolutional neural network (SCNN) inference, with real-time hypoxia classification serving as a biomedical edge application. The architecture implements a hybrid spiking convolutional-fully connected (CNN-FC) topology on a programmable, quantized, layer-based neuromorphic hardware core. Early layers perform spiking convolution using receptive-field connectivity with support for multi-channel kernels and stride, while deeper layers use fully connected spiking layers for classification. A PyTorch-based hardware-software co-design flow enables deployment of trained parameters with quantization and configurability support. The design is first validated on MNIST and Fashion-MNIST, achieving hardware accuracies of up to 98% and 86%, respectively, at 16-bit precision. It is then applied to hypoxia classification using red and infrared photoplethysmography (PPG) signals acquired from a shoulder-mounted sensor, with skin tone included as an additional input channel. The resulting classifier achieves an average hardware accuracy of 88.26% across five folds at 16-bit precision while consuming 1.455 W of dynamic power, demonstrating the feasibility of low-power neuromorphic biomedical classification at the edge.

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arXiv2026

Real-Time Shape Control of Multi-Segment Soft Robotic Arms Using Koopman Operators with Global and Local Observables

Multi-segment soft robotic arms can continuously reconfigure their body shapes for safe interaction, but tip control alone is insufficient for constrained-space tasks. Therefore, shape control is a more important task for multi-segment soft arms than tip control, but remains challenging due to the high dimensionality and nonlinear dynamics of continuum deformation. In existing work, shape control accuracy is defined by the error in the global frame (global shape error). For multi-segment soft arms, using only global shape error as the control objective is insufficient, as segment coupling, gravity-induced loading, and inertial effects become more significant. This difficulty increases with the number of segments. In this paper, we present a Koopman-based model predictive control framework that combines global and local observables, enabling real-time shape control on multi-segment soft robotic arms. The framework is evaluated through numerical and physical experiments. Numerical experiments demonstrate the scalability of the proposed controller by achieving shape control on robots with up to 10 independently actuated segments. The physical experiments demonstrate that the controller is capable of (1) real-time shape control of 3- and 5-segment robotic arms with tip speeds up to 0.6 m/s, (2) robust tracking without retraining, including distal payloads up to 400~g and recovery from a 7~N lateral disturbance, and (3) the potential for future inspection applications through a confined-space demonstration. These results demonstrate that the proposed framework enables dynamic, scalable, and accurate real-time shape control on multi-segment soft robotic arms.

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arXiv2026

Generalized Regret Analysis of Thompson Sampling using Fractional Posteriors

Thompson sampling (TS) is one of the most popular and earliest algorithms to solve stochastic multi-armed bandit problems. We consider a variant of TS, named $α$-TS, where we use a fractional or $α$-posterior ($α\in(0,1)$) instead of the standard posterior distribution. To compute an $α$-posterior, the likelihood in the definition of the standard posterior is tempered with a factor $α$. For $α$-TS we obtain both instance-dependent $\mathcal{O}\left(\sum_{k \neq i^*} Δ_k\left(\frac{\log(T)}{C(α)Δ_k^2} + \frac{1}{2} \right)\right)$ and instance-independent $\mathcal{O}(\sqrt{KT\log K})$ frequentist regret bounds under very mild conditions on the prior and reward distributions, where $Δ_k$ is the gap between the true mean rewards of the $k^{th}$ and the best arms, and $C(α)$ is a known constant. Both the sub-Gaussian and exponential family models satisfy our general conditions on the reward distribution. Our conditions on the prior distribution can be easily satisfied by a density that is positive, continuous, and bounded. We also establish another instance-dependent regret upper bound that matches (up to constants) to that of improved UCB [Auer and Ortner, 2010]. Our regret analysis carefully adapts and combines recent theoretical developments in the non-asymptotic concentration analysis and Bernstein-von Mises type results for the $α$-posterior distribution. Moreover, our analysis does not require additional structural properties such as closed-form posteriors or conjugate priors.

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arXiv2026

A Feedback Linearized Model Predictive Control Strategy for Input-Constrained Self-Driving Cars

This paper proposes a novel real-time affordable solution to the trajectory tracking control problem for self-driving cars subject to longitudinal and steering angular velocity constraints. To this end, we develop a dual-mode Model Predictive Control (MPC) solution starting from an input-output feedback linearized description of the vehicle kinematics. First, we derive the state-dependent input constraints acting on the linearized model and characterize their worst-case time-invariant inner approximation. Then, a dual-mode MPC is derived to be real-time affordable and ensuring, by design, constraints fulfillment, recursive feasibility, and uniformly ultimate boundedness of the tracking error in an ad-hoc built robust control invariant region. The approach's effectiveness and performance are experimentally validated via laboratory experiments on a Quanser Qcar. The obtained results show that the proposed solution is computationally affordable and with tracking capabilities that outperform two alternative control schemes.

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arXiv2026

D4orm: Multi-Robot Trajectories with Dynamics-aware Diffusion Denoised Deformations

This work presents an optimization method for generating kinodynamically feasible and collision-free multi-robot trajectories that exploits an incremental denoising scheme in diffusion models. Our key insight is that high-quality trajectories can be discovered merely by denoising noisy trajectories sampled from a distribution. This approach has no learning component, relying instead on only two ingredients: a dynamical model of the robots to obtain feasible trajectories via rollout, and a fitness function to guide denoising with Monte Carlo gradient approximation. The proposed framework iteratively optimizes a deformation for the previous trajectory with the current denoising process, allows anytime refinement as time permits, supports different dynamics, and benefits from GPU acceleration. Our evaluations for differential-drive and holonomic teams with up to 16 robots in 2D and 3D worlds show its ability to discover high-quality solutions faster than other black-box optimization methods such as MPPI. In a 2D holonomic case with 16 robots, it is almost twice as fast. As evidence for feasibility, we demonstrate zero-shot deployment of the planned trajectories on eight multirotors. Code and video: https://github.com/proroklab/d4orm

cs.RO↗

Compare source metadata on this page

Work	Published	Source identifier	Source
Effective Range and Optimal Frequency of Through-the-Earth Magnetic Induction Communication	2026-09-02	2609.02485	arxiv
Physics-based Online Adaptive Koopman Model Predictive Attitude Control for Combined Spacecraft with Dynamic Uncertainties	2026-09-02	2609.02534	arxiv
Pre-Lane-change Signal in Transitional Autonomous Vehicles: Results from Controlled Experiments	2026-09-02	2609.02575	arxiv
Zonotope-Based Active Exposure of Stealthy Deception Attacks in Sensor-Fusion Systems	2026-09-02	2609.02587	arxiv
Economic Model Predictive Control with Policy-Guided Terminal Ingredients	2026-09-02	2609.02628	arxiv
A Dry-Contact Ear-EEG System With Continuous Electrode-Skin Impedance Mismatch Monitoring for Motion Artifact Cancellation Using DRL Stimulus	2026-09-02	2609.02777	arxiv
Guidelines for the implementation of power oscillation damping controllers in power converters	2026-09-02	2609.02810	arxiv
Tests on the POD-P controller of INELFE Spain-France VSC-HVDC interconnector	2026-09-02	2609.02838	arxiv
Hosting Capacity Assessment of Data Centers with Voltage Ride-Through Capability in Power Systems	2026-09-02	2609.03030	arxiv
Dynamic Operational Reserve Margin Assessment from Risk-Constrained Unit Commitment States	2026-09-02	2609.03066	arxiv
GPU-Accelerated Astrodynamics World Models for Spacecraft Rendezvous and Proximity Operations	2026-09-02	2609.03067	arxiv
Finite-Sample Limits of Entropy-Based Structure Identification in Discretized Nonlinear Systems	2026-09-02	2609.03074	arxiv
Structural Compatibility and Uniform Stability of Temporally Degenerate Parabolic Systems	2026-09-02	2609.03136	arxiv
A Reconfigurable Hybrid Convolutional-Fully Connected Neuromorphic Core for Biomedical Edge Inference	2026-09-02	2609.03174	arxiv
Real-Time Shape Control of Multi-Segment Soft Robotic Arms Using Koopman Operators with Global and Local Observables	2026-09-02	2609.03175	arxiv
Generalized Regret Analysis of Thompson Sampling using Fractional Posteriors	2026-09-01	2309.06349	arxiv
A Feedback Linearized Model Predictive Control Strategy for Input-Constrained Self-Driving Cars	2026-09-01	2405.01753	arxiv
D4orm: Multi-Robot Trajectories with Dynamics-aware Diffusion Denoised Deformations	2026-09-01	2503.12204	arxiv

These are bibliographic comparisons, not experimental rankings. Follow the original document for methods and conditions.