Systems and Control
Taylor-Lagrange Control for Safety-Critical Systems
Wei Xiao and Anni Li
This paper proposes a novel Taylor-Lagrange Control (TLC) method for nonlinear control systems to ensure the safety and stability through Taylor's
theorem with Lagrange remainder. To achieve this, we expand a safety or stability function with respect to time along the system dynamics using the Lie derivative and Taylor's
theorem. This expansion enables the control input to appear in the Taylor series at an order equivalent to the relative degree of the function. We show that the proposed TLC provides necessary and sufficient conditions for system safety and is applicable to systems and constraints of arbitrary relative degree. The TLC exhibits connections with existing Control Barrier Function (CBF) and Control Lyapunov Function (CLF) methods, and it further extends the CBF and CLF methods to the complex domain, especially for higher order cases. Compared to High-Order CBFs (HOCBFs), TLC is less restrictive as it does not require forward invariance of the intersection of a set of safe sets while HOCBFs do. We employ TLC to reformulate a constrained optimal control problem as a sequence of quadratic programs with a zero-order hold implementation method, and demonstrate the safety of zero-order hold TLC using an event-triggered control method to address inter-sampling effects. Finally, we illustrate the effectiveness of the proposed TLC method through an adaptive cruise control system and a
robot control problem, and compare it with existing CBF methods.
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Systems and Control
Pivot-Only Azimuthal Control and Attitude Estimation of Balloon-borne Payloads
Philippe Voyer, Simon Tartakovsky, Steven J. Benton et al. (+1 more)
This paper presents an attitude estimation and yaw-rate control framework for balloon-borne payloads using pivot-only actuation, motivated by the Taurus experiment. Taurus is a long-duration balloon instrument designed for rapid azimuthal scanning at approximately 30 deg/s using a motorized pivot at the flight-train connection, without a reaction wheel. We model the gondola as a rigid body subject to realistic disturbances and sensing limitations, and implement a Multiplicative Extended Kalman Filter (MEKF) that estimates attitude and gyroscope bias by fusing inertial and vector-camera measurements. A simple PI controller uses the estimated states to regulate yaw rate. Numerical simulations incorporating representative disturbance and measurement noise levels are used to evaluate closed-loop control performance and MEKF behavior under flight-like conditions. Experimental tests on the Taurus gondola validate the pivot-only approach, demonstrating stable high-rate tracking under realistic hardware constraints. The close agreement between simulation and experiment indicates that the simplified rigid-body model captures the dominant dynamics relevant for controller design and integrated estimation-and-control development.
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All authors Philippe Voyer, Simon Tartakovsky, Steven J. Benton, William C. Jones
Systems and Control
Toward a Decision Support System for Energy-Efficient Ferry Operation on Lake Constance based on Optimal Control
Hannes Homburger, Bastian Jäckl, Stefan Wirtensohn et al. (+5 more)
The maritime sector is undergoing a disruptive technological change driven by three main factors: autonomy, decarbonization, and digital transformation. Addressing these factors necessitates a reassessment of inland vessel operations. This paper presents the design and development of a decision support system for ferry operations based on a shrinking-horizon optimal control framework. The problem formulation incorporates a mathematical model of the ferry's dynamics and environmental disturbances, specifically water currents and wind, which can significantly influence the dynamics. Real-world data and illustrative scenarios demonstrate the potential of the proposed system to effectively support ferry crews by providing real-time guidance. This enables enhanced operational efficiency while maintaining predefined maneuver durations. The findings suggest that optimal control applications hold substantial promise for advancing future ferry operations on inland waters. A
video of the real-world ferry MS Insel Mainau operating on Lake Constance is available at: https://youtu.be/i1MjCdbEQyE
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All authors Hannes Homburger, Bastian Jäckl, Stefan Wirtensohn, Christian Stopp, Maximilian T. Fischer, Moritz Diehl, Daniel A. Keim, Johannes Reuter
Systems and Control
LUCID: Learning-Enabled Uncertainty-Aware Certification of Stochastic Dynamical Systems
Ernesto Casablanca, Oliver Schön, Paolo Zuliani et al. (+1 more)
Ensuring the safety of AI-enabled systems, particularly in high-stakes domains such as autonomous driving and healthcare, has become increasingly critical. Traditional formal verification tools fall short when faced with systems that embed both opaque, black-box AI components and complex
stochastic dynamics. To address these challenges, we introduce LUCID (Learning-enabled Uncertainty-aware Certification of
stochastIc Dynamical systems), a verification engine for certifying safety of black-box
stochastic dynamical systems from a finite
dataset of random state transitions. As such, LUCID is the first known tool capable of establishing quantified safety guarantees for such systems. Thanks to its modular architecture and extensive documentation, LUCID is designed for easy extensibility. LUCID employs a data-driven methodology rooted in control barrier certificates, which are learned directly from system transition data, to ensure formal safety guarantees. We use conditional mean
embeddings to embed data into a reproducing kernel
Hilbert space (RKHS), where an RKHS ambiguity set is constructed that can be inflated to robustify the result to out-of-distribution behavior. A key innovation within LUCID is its use of a finite
Fourier kernel expansion to reformulate a semi-infinite non-convex optimization problem into a tractable linear program. The resulting spectral barrier allows us to leverage the fast
Fourier transform to generate the relaxed problem efficiently, offering a scalable yet distributionally robust framework for verifying safety. LUCID thus offers a robust and efficient verification framework, able to handle the complexities of modern black-box systems while providing formal guarantees of safety. These unique capabilities are demonstrated on challenging
benchmarks.
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All authors Ernesto Casablanca, Oliver Schön, Paolo Zuliani, Sadegh Soudjani
Image and Video Processing
mViSE: A Visual Search Engine for Analyzing Multiplex IHC Brain Tissue Images
Liqiang Huang, Rachel W. Mills, Saikiran Mandula et al. (+7 more)
Whole-slide multiplex imaging of
brain tissue generates massive information-dense images that are challenging to analyze and require custom software. We present an alternative query-driven programming-free strategy using a multiplex visual search engine (mViSE) that learns the multifaceted
brain tissue chemoarchitecture, cytoarchitecture, and myeloarchitecture. Our divide-and-conquer strategy organizes the data into panels of related molecular markers and uses self-supervised learning to train a multiplex encoder for each panel with explicit visual confirmation of successful learning. Multiple panels can be combined to process visual queries for retrieving similar communities of individual
cells or multicellular niches using information-theoretic methods. The
retrievals can be used for diverse purposes including tissue exploration, delineating
brain regions and cortical
cell layers, profiling and comparing
brain regions without computer programming. We validated mViSE's ability to retrieve single
cells, proximal
cell pairs, tissue patches, delineate cortical layers,
brain regions and sub-regions. mViSE is provided as an open-source QuPath plug-in.
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All authors Liqiang Huang, Rachel W. Mills, Saikiran Mandula, Lin Bai, Mahtab Jeyhani, John Redell, Hien Van Nguyen, Saurabh Prasad, Dragan Maric, Badrinath Roysam
Systems and Control
Model Error Resonance: The Geometric Nature of Error Dynamics
Yuntao Dai
This paper introduces a
geometric theory of model error, treating true and model dynamics as geodesic flows generated by distinct affine connections on a smooth
manifold. When these connections differ, the resulting trajectory discrepancy–termed the Latent Error Dynamic Response (LEDR)–acquires an intrinsic dynamical structure governed by
curvature. We show that the LEDR satisfies a Jacobi-type equation, where
curvature mismatch acts as an explicit forcing term. In the important case of a flat model connection, the LEDR reduces to a classical Jacobi field on the true
manifold, causing Model Error Resonance (MER) to emerge under positive sectional
curvature. The theory is extended to a discrete-time analogue, establishing that this
geometric structure and its resonant behavior persist in sampled systems. A closed-form analysis of a sphere–plane example demonstrates that
curvature can be inferred directly from the LEDR
evolution. This framework provides a unified
geometric interpretation of structured error dynamics and offers foundational tools for
curvature-informed model validation.
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Image and Video Processing · Cross-listed from cs.CV
EditMGT: Unleashing Potentials of Masked Generative Transformers in Image Editing
Wei Chow, Linfeng Li, Lingdong Kong et al. (+13 more)
Recent advances in
diffusion models (DMs) have achieved exceptional visual quality in image editing tasks. However, the global denoising dynamics of DMs inherently conflate local editing targets with the full-image context, leading to unintended modifications in non-target regions. In this paper, we shift our
attention beyond DMs and turn to Masked Generative
Transformers (MGTs) as an alternative approach to tackle this challenge. By predicting multiple masked
tokens rather than holistic refinement, MGTs exhibit a localized decoding paradigm that endows them with the inherent capacity to explicitly preserve non-relevant regions during the editing process. Building upon this insight, we introduce the first MGT-based image editing framework, termed EditMGT. We first demonstrate that MGT's cross-
attention maps provide informative localization signals for localizing edit-relevant regions and devise a multi-layer
attention consolidation scheme that refines these maps to achieve fine-grained and precise localization. On top of these adaptive localization results, we introduce region-hold sampling, which restricts
token flipping within low-
attention areas to suppress spurious edits, thereby confining modifications to the intended target regions and preserving the integrity of surrounding non-target areas. To train EditMGT, we construct CrispEdit-2M, a high-resolution
dataset spanning seven diverse editing categories. Without introducing additional parameters, we adapt a pre-trained text-to-image MGT into an image editing model through
attention injection. Extensive experiments across four standard
benchmarks demonstrate that, with fewer than 1B parameters, our model achieves similarity performance while enabling 6 times faster editing. Moreover, it delivers comparable or superior editing quality, with improvements of 3.6% and 17.6% on style change and style transfer tasks, respectively.
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All authors Wei Chow, Linfeng Li, Lingdong Kong, Zefeng Li, Qi Xu, Hang Song, Tian Ye, Xian Wang, Jinbin Bai, Shilin Xu, Xiangtai Li, Junting Pan, Shaoteng Liu, Ran Zhou, Tianshu Yang, Songhua Liu
Systems and Control
Two-dimensional Decompositions of High-dimensional Configurations for Efficient Multi-vehicle Coordination at Intelligent Intersections
Amirreza Akbari and Johan Thunberg
For multi-vehicle complex traffic scenarios in shared spaces such as intelligent intersections, safe coordination and trajectory planning is challenging due to computational complexity. To meet this challenge, we introduce a computationally efficient method for generating collision-free trajectories along predefined vehicle paths. We reformulate a constrained minimum-time trajectory planning problem as a problem in a high-dimensional configuration space, where conflict zones are modeled by high-dimensional polyhedra constructed from two-dimensional rectangles. Still, in such a formulation, as the number of vehicles involved increases, the computational complexity increases significantly. To address this, we propose two
algorithms for near-optimal local optimization that significantly reduce the computational complexity by decomposing the high-dimensional problem into a sequence of 2D graph search problems. The resulting trajectories are then incorporated into a Nonlinear Model Predictive Control (NMPC) framework to ensure safe and smooth vehicle motion. We furthermore show in numerical evaluation that this approach significantly outperforms existing MILP-based time-scheduling; both in terms of objective-value and computational time.
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Systems and Control · Cross-listed from cs.LG
High-Dimensional Surrogate Modeling for Closed-Loop Learning of Neural-Network-Parameterized Model Predictive Control
Sebastian Hirt, Valentinus Suwanto, Hendrik Alsmeier et al. (+2 more)
Learning controller parameters from closed-loop data has been shown to improve closed-loop performance.
Bayesian optimization, a widely used black-box and sample-efficient learning method, constructs a probabilistic surrogate of the closed-loop performance from few experiments and uses it to select informative controller parameters. However, it typically struggles with dense high-dimensional controller parameterizations, as they may appear, for example, in tuning model predictive controllers, because standard surrogate models fail to capture the structure of such spaces. This work suggests that the use of
Bayesian neural networks as surrogate models may help to mitigate this limitation. Through a comparison between Gaussian processes with Matern kernels, finite-width
Bayesian neural networks, and infinite-width
Bayesian neural networks on a cart-pole task, we find that
Bayesian neural network surrogate models achieve faster and more reliable convergence of the closed-loop cost and enable successful optimization of parameterizations with hundreds of dimensions. Infinite-width
Bayesian neural networks also maintain performance in settings with more than one thousand parameters, whereas Matern-kernel Gaussian processes rapidly lose effectiveness. These results indicate that
Bayesian neural network surrogate models may be suitable for learning dense high-dimensional controller parameterizations and offer practical guidance for selecting surrogate models in learning-based controller design.
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All authors Sebastian Hirt, Valentinus Suwanto, Hendrik Alsmeier, Maik Pfefferkorn, Rolf Findeisen
Image and Video Processing · Cross-listed from physics.flu-dyn
Particle Image Velocimetry Refinement via Consensus ADMM for Active Fluid Control
Alan Bonomi, Francesco Banelli and Antonio Terpin
Particle Image Velocimetry (PIV) is among the central modalities for measuring flow fields across laboratory, industrial and environmental setting. Traditional PIV approaches typically depend on tuning parameters specific to the imaging setup, making the performance sensitive to variations in illumination, flow conditions, and seeding density. Similarly, state-of-the-art machine learning methods for flow quantification are fragile outside their training set. In our experiments, we observed that flow quantification would improve if different tunings (or
algorithms) were applied to different regions of the same image pair. Motivated by this observation, we thus pose flow quantification as a multi-
estimator fusion problem: several heterogeneous
algorithms process the same image pair in parallel, and their dense flow fields are treated as complementary estimates. To fuse them, we adopt a consensus framework based on the alternating direction method of multipliers, incorporating priors such as smoothness and incompressibility. We perform several numerical experiments to demonstrate the benefits of this approach. For instance, we achieve a decrease in end-point-error of up to 20% of a dense-inverse-search
estimator at an inference rate of 60Hz, and we show how performance can be increased with outlier rejection. Our method is implemented in JAX and integrated into Flow Gym, enabling reproducible comparisons with the state of the art and systematic evaluation across different base
algorithms. Finally, we demonstrate successful deployment of our method in the same real-world active-fluids-control setup of Terpin and D'Andrea [1], where a reinforcement-learning
agent uses our flow estimates to learn to minimize drag (down by 36%) or maximize it (up to 32%) with only two minutes of real-world interaction. Hardware and software are made available at ActiveFluidControl.com.
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Signal Processing
PaddleSat Optical Charging Station in Space
Anish Nair, Austin O' Connell, Shalomi Arulpragasam et al. (+1 more)
This work investigates the feasibility and design trade-offs for a companion spacecraft, or PaddleSat, to charge a host spacecraft by wirelessly transmitting power using a directional
laser system. The primary goal of the PaddleSat is to supplement power on a host spacecraft to reduce the requirements for onboard power systems of the host spacecraft or extend mission lifetimes. System performance estimates, link budget calculations, optical transmission hardware and link analysis, design tradeoffs between beam divergence, optical efficiency, and relative orbital control requirements are examined.
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All authors Anish Nair, Austin O' Connell, Shalomi Arulpragasam, Noah Kim
Systems and Control · Cross-listed from cs.RO
Architecting Large Action Models for Human-in-the-Loop Intelligent Robots
Kanisorn Sangchai, Methasit Boonpun, Withawin Kraipetchara et al. (+1 more)
The realization of intelligent
robots, operating autonomously and interacting with other intelligent
agents, human or artificial, requires the integration of environment perception,
reasoning, and action. Classic Artificial Intelligence techniques for this purpose, focusing on symbolic approaches, have long-ago hit the scalability wall on compute and
memory costs. Advances in Large
Language Models in the past decade (neural approaches) have resulted in unprecedented displays of capability, at the cost of control, explainability, and interpretability. Large Action Models aim at extending Large
Language Models to encompass the full perception,
reasoning, and action cycle; however, they typically require substantially more comprehensive training and suffer from the same deficiencies in reliability. Here, we show it is possible to build competent Large Action Models by composing off-the-shelf foundation models, and that their control, interpretability, and explainability can be effected by incorporating symbolic wrappers and associated verification on their outputs, achieving verifiable neuro-symbolic solutions for intelligent
robots. Our experiments on a multi-modal
robot demonstrate that Large Action Model intelligence does not require massive end-to-end training, but can be achieved by integrating efficient perception models with a logic-driven core. We find that driving action execution through the generation of Planning Domain Definition
Language (PDDL) code enables a human-in-the-loop verification stage that effectively mitigates action
hallucinations. These results can support practitioners in the design and development of robotic Large Action Models across novel industries, and shed light on the ongoing challenges that must be addressed to ensure safety in the field.
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All authors Kanisorn Sangchai, Methasit Boonpun, Withawin Kraipetchara, Paulo Garcia
Image and Video Processing · Cross-listed from cs.CV
Embodied Image Compression
Chunyi Li, Rui Qing, Jianbo Zhang et al. (+6 more)
Image Compression for Machines (ICM) has emerged as a pivotal research direction in the field of visual data compression. However, with the rapid
evolution of machine intelligence, the target of compression has shifted from task-specific virtual models to
Embodied agents operating in real-world environments. To address the communication constraints of
Embodied AI in multi-
agent systems and ensure real-time task execution, this paper introduces, for the first time, the scientific problem of
Embodied Image Compression. We establish a standardized
benchmark, EmbodiedComp, to facilitate systematic evaluation under ultra-low bitrate conditions in a closed-loop setting. Through extensive empirical studies in both simulated and real-world settings, we demonstrate that existing
Vision-
Language-Action models (VLAs) fail to reliably perform even simple manipulation tasks when compressed below the
Embodied bitrate threshold. We anticipate that EmbodiedComp will catalyze the development of domain-specific compression tailored for
Embodied agents , thereby accelerating the
Embodied AI deployment in the Real-world.
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All authors Chunyi Li, Rui Qing, Jianbo Zhang, Yuan Tian, Xiangyang Zhu, Zicheng Zhang, Xiaohong Liu, Weisi Lin, Guangtao Zhai
Systems and Control
A Modeling and Optimization Framework for Fostering Modal Shift through the Integration of Tradable Credits and Demand-Responsive Autonomous Shuttles
Zenghao Hou and Ludovic Leclercq
Tradable Credit Schemes (TCS) promote the use of public and shared transport by capping private car usage while maintaining fair welfare outcomes by allowing credit trading. However, most existing studies assume unlimited public transit capacity or a fixed occupancy of shared modes, often neglecting waiting time and oversimplifying time-based costs by depending solely on in-vehicle travel time. These assumptions can overstate the system's performance with TCS regulation, especially when there are insufficient public or shared transport supplies. To address this, we develop a dynamic
multimodal equilibrium model to capture operation constraints and induced waiting times under TCS regulation. The model integrates travelers' mode choices, credit trading, traffic dynamics, and waiting time, which depend on key operational features of service vehicles such as fleet size and capacity. Besides, most TCS studies assume fixed transport supply, overlooking supply-side responses triggered by demand shifts. Therefore, we further propose integrating adaptive supply management through the deployment of Demand-Responsive Autonomous Shuttles (DRAS) and developing a bi-level optimization framework that incorporates the
equilibrium model to jointly optimize TCS design and operational strategies for the DRAS. We apply the framework to a section of the A10 highway near Paris, France, to examine demand-supply interactions and assess the potential benefits of jointly implementing TCS and DRAS. Numerical results demonstrate the importance of modeling operational features within
multimodal equilibrium and incorporating flexible supply in TCS policies for mitigating overall generalized cost.
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Systems and Control · Cross-listed from math.OC
Safe Bayesian optimization across noise models via scenario programming
Abdullah Tokmak, Thomas B. Schön and Dominik Baumann
Safe
Bayesian optimization (BO) with Gaussian processes is an effective tool for tuning control policies in safety-critical real-world systems, specifically due to its sample efficiency and safety guarantees. However, most safe BO
algorithms assume homoscedastic sub-Gaussian measurement noise, an assumption that does not hold in many relevant applications. In this article, we propose a straightforward yet rigorous approach for safe BO across noise models, including homoscedastic sub-Gaussian and heteroscedastic heavy-tailed distributions. We provide a high-probability bound on the measurement noise via the scenario approach, integrate these bounds into high probability confidence intervals, and prove safety and optimality for our proposed safe BO
algorithm. We deploy our
algorithm in synthetic examples and in tuning a controller for the Franka Emika manipulator in simulation.
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Signal Processing
ACCOR: Attention-Enhanced Complex-Valued Contrastive Learning for Occluded Object Classification Using mmWave Radar IQ Signals
Stefan Hägele, Adam Misik, Constantin Patsch et al. (+1 more)
Millimeter-wave (mmWave) radar provides robust sensing under adverse conditions and can penetrate thin materials for non-visual perception in industrial and robotic settings. Recent work with
MIMO mmWave radar has demonstrated its ability to penetrate cardboard packaging for occluded object classification. However, existing models leave room for improvement and extensions across different sensing frequencies. Building on recent work with
MIMO radar for occluded object classification, we propose ACCOR, an
attention-enhanced complex-valued contrastive learning approach for radar, enabling robust occluded object classification. ACCOR processes complex-valued IQ radar signals via a complex-valued CNN backbone, a multi-head
attention layer and a hybrid loss. The hybrid loss combines a weighted cross-
entropy term with a supervised contrastive term. We extend an existing 64 GHz
dataset with a new 67 GHz subset and evaluate performance across both bands. ACCOR achieves 96.60 % accuracy at 64 GHz and 93.59 % at 67 GHz on 10 objects, surpassing prior radar-specific and adapted image models. Results demonstrate the benefits of integrating complex-valued deep learning,
attention, and contrastive learning for mmWave radar-based occluded object classification.
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All authors Stefan Hägele, Adam Misik, Constantin Patsch, Eckehard Steinbach
Audio and Speech Processing
All-in-One ASR: Unifying Encoder-Decoder Models of CTC, Attention, and Transducer in Dual-Mode ASR
Takafumi Moriya, Masato Mimura, Tomohiro Tanaka et al. (+3 more)
This paper proposes a unified framework, All-in-One ASR, that allows a single model to support multiple automatic
speech recognition (ASR) paradigms, including connectionist temporal classification (CTC),
attention-based encoder-decoder (AED), and Transducer, in both offline and streaming modes. While each ASR architecture offers distinct advantages and trade-offs depending on the application, maintaining separate models for each scenario incurs substantial development and deployment costs. To address this issue, we introduce a multi-mode joiner that enables seamless integration of various ASR modes within a single unified model. Experiments show that All-in-One ASR significantly reduces the total model footprint while matching or even surpassing the recognition performance of individually optimized ASR models. Furthermore, joint decoding leverages the complementary strengths of different ASR modes, yielding additional improvements in recognition accuracy.
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All authors Takafumi Moriya, Masato Mimura, Tomohiro Tanaka, Hiroshi Sato, Ryo Masumura, Atsunori Ogawa
Signal Processing
RadarFuseNet: Phase-Weighted Complex-Valued Cross-Attention Fusion for Radar Signal Classification
Stefan Hägele, Adam Gorriahn, Philipp Wolters et al. (+1 more)
Millimeter-wave (mmWave) radar is a compact sensing technology that is particularly well suited for perception tasks in situations where
vision-based sensors are limited, such as under adverse environmental conditions or occlusion. The complex-valued and nonlinear nature of mmWave radar IQ signals makes complex-valued deep learning a natural choice for extracting relevant information from in-phase and quadrature (IQ) data. However, progress in IQ-based deep learning is limited by the scarcity of annotated radar IQ
datasets. In this paper, we propose RadarFuseNet, a bidirectional complex-valued cross-
attention fusion network with phase-aware weighting inside the
attention mechanism, combining IQ and FFT-derived features extracted by two complex-valued CNN feature extractors. To the best of our knowledge, RadarFuseNet is among the first complex-valued dual-domain fusion frameworks to employ phase-weighted bidirectional cross-
attention for radar object classification. Evaluated on our own custom complex-valued IQ radar
dataset of occluded objects, RadarFuseNet achieves classification accuracies of 97.70% at 64GHz center frequency and 94.70% at 67GHz center frequency, outperforming all comparison and ablation models.
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All authors Stefan Hägele, Adam Gorriahn, Philipp Wolters, Eckehard Steinbach
Systems and Control
Data-driven control-oriented modelling for MPC-based control of urban drainage systems
Luis Romero-Ben, Bernat Joseph-Duran, David Sunyer et al. (+4 more)
This article presents a data-driven, control-oriented modelling methodology for urban drainage systems (UDS). The proposed framework requires three main key components: input-output data from the element to be modelled, expert knowledge to define the model structure, and data-fitting techniques to obtain optimal parameters. The methodology is evaluated using a realistic
benchmark from an UDS in Madrid, Spain. The results show high model accuracy and improved performance within a MPC scheme, reducing discharge and increasing treatment facilities utilization.
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All authors Luis Romero-Ben, Bernat Joseph-Duran, David Sunyer, Gabriela Cembrano, Jordi Meseguer, Vicenç Puig, Alejandro Carrasco
Systems and Control
Shared Situational Awareness Using Hybrid Zonotopes with Confidence Metric
Vandana Narri, Jonah J. Glunt, Joshua A. Robbins et al. (+3 more)
Situational awareness for connected and automated vehicles describes the ability to perceive and predict the behavior of other road-users in the near surroundings. However, pedestrians can become occluded by vehicles or infrastructure, creating significant safety risks due to limited visibility. Vehicle-to-everything communication enables the sharing of perception data between connected road-users, allowing for a more comprehensive awareness. The main challenge is how to fuse perception data when measurements are inconsistent with the true locations of pedestrians. Inconsistent measurements can occur due to sensor noise, false positives, or unmodeled disturbances. This paper employs set-based estimation with constrained zonotopes to compute a confidence metric for the measurement set from each sensor. Estimated sets and their confidences are then fused using hybrid zonotopes. This method can account for inconsistent measurements, enabling reliable and robust fusion of the sensor data. The effectiveness of the proposed method is demonstrated in both simulation and real experiments.
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All authors Vandana Narri, Jonah J. Glunt, Joshua A. Robbins, Jonas Mårtensson, Herschel C. Pangborn, Karl H. Johansson
Systems and Control
Optimal Delay Compensation in Networked Predictive Control
Severin Beger, Yihui Lin, Katarina Stanojevic et al. (+1 more)
Networked Predictive Control is widely used to mitigate the effect of delays and dropouts in Networked Control Systems, particularly when these exceed the sampling time. A key design choice of these methods is the delay bound, which determines the prediction horizon and the robustness to information loss. This work develops a systematic method to select the optimal bound by quantifying the trade-off between prediction errors and open-loop operation caused by communication losses. Simulation studies demonstrate the performance gains achieved with the optimal bound.
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All authors Severin Beger, Yihui Lin, Katarina Stanojevic, Sandra Hirche
Systems and Control
A Robust Model Predictive Control Method for Networked Control Systems
Severin Beger and Sandra Hirche
Robustly compensating network constraints such as delays and packet dropouts in networked control systems is crucial for remotely controlling dynamical systems. This work proposes a novel prediction consistent method to cope with delays and packet losses as encountered in UDP-type communication systems. The augmented control system preserves all properties of the original model predictive control method under the network constraints. Furthermore, we propose to use linear tube MPC with the novel method and show that the system converges robustly to the origin under mild conditions. We illustrate this with simulation examples of a cart pole and a continuous stirred tank reactor.
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Systems and Control
An Input-Output Data-Driven Dissipativity Approach for Compositional Stability Certification of Interconnected LTI MIMO Systems
Alejandra Sandoval-Carranza, Juan E. Machado and Johannes Schiffer
We propose an input-output data-driven framework for certifying the stability of interconnected multiple-input-multiple-output linear time-invariant discrete-time systems via QSR-dissipativity. That is, by using measured input-output trajectories of each subsystem, we verify dissipative properties and extract local passivity indices without requiring an explicit model identification. These passivity indices are then used to derive conditions under which the
equilibrium of the interconnected system is stable. In particular, the framework identifies how the lack of passivity in some subsystems can be compensated by surpluses in others. The proposed approach enables a compositional stability analysis by combining subsystem-level conditions into a criterion valid for the overall interconnected system. We illustrate via a numerical case study, how to compute channel-wise passivity indices and infer stability guarantees directly from data with the proposed method.
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Signal Processing
STAR-RIS-Aided Secure Communications:Analytical Insights and Performance Comparison
Taissir Y. Elganimi, Mahmoud Aldababsa, Ali A. Nasir et al. (+1 more)
Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (
STAR-RISs) have emerged as a promising technology for enabling full-space signal manipulation and enhancing wireless network coverage and capacity. In this article, we present a comprehensive analytical comparison of
STAR-RIS-assisted systems with single-input single-output (SISO), conventional RISs, and decode-and-forward (DF) relaying schemes, including both half-duplex (HD) and full-duplex (FD) modes. Closed-form expressions are derived for the achievable secrecy rates of
STAR-RIS-aided communications under both the absence and presence of eavesdroppers. Unlike most existing works, the direct source destination link is incorporated in all considered schemes, and optimal transmit power allocation is investigated for HD and FD-DF relaying. Furthermore, we provide the conditions under which
STAR-RIS outperforms HD- and FD-DF relaying and quantify the minimum number of
STAR-RIS elements required to achieve superior rates. The impacts of key system parameters including transmit power, number of elements, reflection-to-transmission power ratio, element-splitting factor, and deployment positions on both achievable and secrecy performance are investigated. The results reveal that
STAR-RIS systems can achieve superior rates and secrecy rates compared to all
benchmark schemes.
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All authors Taissir Y. Elganimi, Mahmoud Aldababsa, Ali A. Nasir, Khaled M. Rabie
Signal Processing · Cross-listed from quant-ph
Processing through encoding: Quantum circuit approaches for point-wise multiplication and convolution
Andreas Papageorgiou, Paulo Vitor Itaborai, Kostas Blekos et al. (+1 more)
This paper introduces
quantum circuit methodologies for pointwise multiplication and convolution of complex functions, conceptualized as "processing through encoding". Leveraging known techniques, we describe an approach where multiple complex functions are encoded onto auxiliary
qubits. Applying the proposed scheme for two functions $f$ and $g$, their pointwise product $f(x)g(x)$ is shown to naturally form as the coefficients of part of the resulting
quantum state. Adhering to the convolution
theorem, we then demonstrate how the convolution $f*g$ can be constructed. Similarly to related work, this involves the encoding of the
Fourier coefficients $\mathcal{F}[f]$ and $\mathcal{F}[g]$, which facilitates their pointwise multiplication, followed by the inverse
Quantum Fourier Transform. We discuss the simulation of these techniques, their integration into an extended \verb|quantumaudio| package for audio signal processing, and present initial experimental validations. This work offers a promising avenue for
quantum signal processing, with potential applications in areas such as
quantum-enhanced audio manipulation and synthesis.
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All authors Andreas Papageorgiou, Paulo Vitor Itaborai, Kostas Blekos, Karl Jansen
Signal Processing
Point Target Near-Field Bistatic Imaging: Chirp-Based Aliasing Analysis
Baptiste Sambon, Gilles Monnoyer, Claude Oestges et al. (+1 more)
This paper presents a chirp-based framework for characterising aliasing in a bistatic Near-Field (NF) imaging system equipped with multidimensional
antenna arrays. Extending monostatic formulations, we derive closed-form expressions for the maximum spatial frequency, enabling the analytical derivations of the conditions for aliasing-free image reconstruction. The framework also provides a
geometric interpretation of aliasing based on the
antenna array geometry, target position, and
antenna element spacing. Numerical results corroborate theoretical findings and show that the aliasing-free region enlarges with smaller
antenna spacing, greater target range, lower array dimensionality, and smaller arrays. These results enable more effective design of bistatic NF imaging systems.
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All authors Baptiste Sambon, Gilles Monnoyer, Claude Oestges, Luc Vandendorpe
Signal Processing
Source Localization and Power Estimation through RISs: Performance Analysis and Prototype Validations
Fuhai Wang, Tiebin Mi, Chun Wang et al. (+3 more)
This paper investigates the capabilities and effectiveness of backward localization centered on reconfigurable intelligent surfaces (RISs). In the backward sensing paradigm, the region of interest (RoI) is illuminated using a set of diverse radiation patterns. These patterns encode spatial information into a sequence of measurements, which are subsequently processed to reconstruct the RoI. We show that a single RIS can estimate the direction of arrival of incident waves by leveraging configurational diversity, and that the spatial diversity provided by multiple RISs further improves the accuracy of source localization and power estimation. The underlying structure of the sensing operator in the multi-snapshot measurement process is clarified. For single-RIS localization, the sensing operator is decomposed into a product of structured
matrices, each corresponding to a specific physical process: wave propagation to and from the RIS, the relative phase offsets of elements with respect to the reference point, and the applied phase configuration of each element. A unified framework for identifying key performance indicators is established by analyzing the conditioning of the sensing operators. In the multi-RIS setting, we derive–via rank analysis–the governing law among the RoI size, the number of elements, and the number of measurements. Upper bounds on the relative error of the least squares reconstruction
algorithm are derived. These bounds clarify how key performance indicators affect estimation error and provide valuable guidance for system-level optimization. Numerical experiments confirm that the trend of the relative error is consistent with the theoretical bounds.
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All authors Fuhai Wang, Tiebin Mi, Chun Wang, Rujing Xiong, Zhengyu Wang, Robert Caiming Qiu
Systems and Control · Cross-listed from math.DS
Results for Global Attractivity of Interior Equilibrium Points for Lotka-Volterra Systems
Iasson Karafyllis
This paper provides global attractivity results for the interior
equilibrium point of a general Lotka-Volterra system with no restriction on the dimension of the system and with no special structure or properties of the interaction
matrix. The main result contains as special cases all known general results, including the Volterra-Lyapunov
theorem and the recently proposed eigenvector conditions. Moreover, global attractivity of the interior
equilibrium point is shown for a three-dimensional example, where none of the existing general results can be applied.
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Systems and Control · Cross-listed from cs.RO
Incremental Validation of Automated Driving Functions using Generic Volumes in Micro- Operational Design Domains
Steffen Schäfer and Martin Cichon
The validation of highly automated, perception-based driving systems must ensure that they function correctly under the full range of real-world conditions. Scenario-based testing is a prominent approach to addressing this challenge, as it involves the systematic simulation of objects and environments. Operational Design Domains (ODDs) are usually described using a taxonomy of qualitative designations for individual objects. However, the process of transitioning from taxonomy to concrete test cases remains unstructured, and completeness is theoretical. This paper introduces a structured method of subdividing the ODD into manageable sections, termed micro-ODDs (mODDs), and deriving test cases with abstract object representations. This concept is demonstrated using a one-dimensional, laterally guided manoeuvre involving a shunting locomotive within a constrained ODD. In this example, mODDs are defined and refined into narrow taxonomies that enable test case generation. Obstacles are represented as generic cubes of varying sizes, providing a simplified yet robust means of evaluating perception performance. A series of tests were conducted in a closed-loop, co-simulated virtual environment featuring photorealistic rendering and simulated
LiDAR, GNSS and camera sensors. The results demonstrate how edge cases in obstacle detection can be systematically explored and how perception quality can be evaluated based on observed vehicle behaviour, using crash versus safe stop as the outcome metrics. These findings support the development of a standardised framework for safety argumentation and offer a practical step towards the validation and authorisation of automated driving functions.
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Systems and Control
Controlled Evolution-Based Day-Ahead Robust Dispatch Considering Frequency Security with Frequency Regulation Loads and Curtailable Loads
Kai Kang, Xiaoyu Peng, Kui Luo et al. (+2 more)
With the extensive integration of volatile and uncertain renewable energy, power systems face significant challenges in primary frequency regulation due to instantaneous power fluctuations. However, the maximum frequency deviation constraint is inherently non-convex, and commonly used two-stage dispatch methods overlook causality, potentially resulting in infeasible day-ahead decisions. This paper presents a controlled
evolution-based day-ahead robust dispatch method to address these issues. First, we suggest the convex relaxation technique to transform the maximum frequency deviation constraint to facilitate optimization. Then, an
evolution-based robust dispatch framework is introduced to align day-ahead decisions with intraday strategies, ensuring both frequency security and power supply reliability. Additionally, a novel controlled
evolution-based
algorithm is developed to solve this framework efficiently. Case studies on a modified IEEE 14-bus system demonstrate the superiority of the proposed method in enhancing frequency security and system reliability.
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All authors Kai Kang, Xiaoyu Peng, Kui Luo, Xi Ru, Feng Liu