Systems and Control
From Natural Language to Certified H-infinity Controllers: Integrating LLM Agents with LMI-Based Synthesis
Shihao Li, Jiachen Li, Jiamin Xu et al. (+1 more)
We present S2C (Specification-to-Certified-Controller), a multi-
agent framework that maps natural-
language requirements to certified $\mathcal{H}_\infty$ state-feedback controllers via LMI synthesis. S2C coordinates five roles – SpecInt (spec extraction), Solv (bounded-real lemma (BRL) LMI), Tester (Monte Carlo and frequency-domain checks), Adapt (spec refinement), and CodeGen (deployable code). The loop is stabilized by a severity- and iteration-aware $\gamma$-floor guardrail and a decay-rate region constraint enforcing $\Re\lambda(A{+}BK)<-\alpha$ with $\alpha=3.9/T_s$ derived from settling-time targets. For state feedback, verification reports disturbance rejection $\big\|C\,(sI-(A{+}BK))^{-1}E\big\|_\infty$ alongside time-domain statistics; discrete
benchmarks are converted to continuous time via a Tustin (bilinear) transform when needed. On 14 COMPleib problems, S2C attains 100% synthesis success and 100% convergence within six iterations, with strong decay-rate satisfaction and near-target certified $\mathcal{H}_\infty$ levels; it improves robustness metrics relative to single-shot BRL and BRL+$\alpha$ baselines. An ablation over LLM backbones (GPT-5, GPT-5 mini, DeepSeek-V3, Qwen-2.5-72B, Llama-4 Maverick) shows the pipeline is robust across models, while stronger models yield the highest effectiveness. These results indicate that LLM
agents can integrate certificate-bearing control synthesis from high-level intent, enabling rapid end-to-end prototyping without sacrificing formal guarantees.
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All authors Shihao Li, Jiachen Li, Jiamin Xu, Dongmei Chen
Systems and Control
Optimisation of Power Modulation for Hall-Héroult Cells: Process Operability and Constraints as Virtual Energy Storage
Choon-Jie Wong, Adam A. Larkin, Jie Bao et al. (+5 more)
Aluminium is manufactured through the
Hall-Héroult process, which is very energy intensive. Power modulation, as an industrial-scale demand-side power management approach, allows aluminium smelters to operate with variable power consumption rates and as such be powered by renewable energy sources. In this way, aluminium smelting
cells can be used as a large virtual energy storage to balance power demand-supply and stabilise electrical grids. This paper studies the potential optimal power modulation operating conditions, including time-varying line current and anode-cathode distance (ACD) profiles to maximise the aluminium reduction
cell profitability subject to constraints on the
cell thermal balance. To deal with the complex
cell dynamics which are spatially distributed and multi-timescale, a novel optimisation approach that utilises both reduced-order and detailed models is developed. The results yield insight into the optimal line current and ACD profiles for different power modulation scenarios including the time of use electricity tariff and spot price. These results can form the foundation for further studies into online control policies of aluminium reduction
cells.
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All authors Choon-Jie Wong, Adam A. Larkin, Jie Bao, Maria Skyllas-Kazacos, Barry J. Welch, Nadia Ahli, Maitha Faraj, Mohamed Mahmoud
Signal Processing
Toward Adaptive BCIs: Enhancing Decoding Stability via User State-Aware EEG Filtering
Yeon-Woo Choi, Hye-Bin Shin and Dan Li
Brain-computer interfaces (BCIs) often suffer from limited robustness and poor long-term adaptability. Model performance rapidly degrades when user
attention fluctuates,
brain states shift over time, or irregular artifacts appear during interaction. To mitigate these issues, we introduce a user state-aware electroencephalogram (
EEG) filtering framework that refines neural representations before decoding user intentions. The proposed method continuously estimates the user's cognitive state (e.g., focus or distraction) from
EEG features and filters unreliable segments by applying adaptive weighting based on the estimated
attention level. This filtering stage suppresses noisy or out-of-focus epochs, thereby reducing distributional drift and improving the consistency of subsequent decoding. Experiments on multiple
EEG datasets that emulate real BCI scenarios demonstrate that the proposed state-aware filtering enhances classification accuracy and stability across different user states and sessions compared with conventional preprocessing pipelines. These findings highlight that leveraging
brain-derived state information–even without additional user labels–can substantially improve the reliability of practical
EEG-based BCIs.
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Signal Processing
Conical Localization via Modified Polar Representation: A Unified Framework for Robust 3-D Positioning with 1-D Sensor Arrays
Ehsan Alamdari and Rouhollah Amiri
This paper presents a unified framework for robust three-dimensional (3-D) source localization using a network of sensors equipped with one-dimensional (1-D) linear arrays. While such arrays offer practical advantages in terms of cost and size, existing localization methods suffer from a fundamental limitation: their performance degrades significantly as the source moves into the far-field, a common challenge known as the thresholding effect. To address this issue, we reformulate the localization problem in the modified polar representation (MPR) coordinate system, which parameterizes the source location using its azimuth, elevation, and inverse-range. We have developed a constrained weighted least squares (CWLS)
estimator, which is subsequently transformed into a tight semidefinite programming (SDP) problem via semidefinite relaxation, enhanced with additional constraints to improve accuracy. Simulation results demonstrate that the proposed
estimator attains the Cramer-Rao lower bound (CRLB) for both angle and inverse-range estimation in near-field scenarios. More importantly, it maintains this optimal performance in the far-field, substantially outperforming state-of-the-art methods, which exhibit significant error at large ranges. The proposed solution thus provides a reliable, unified localization system that is effective irrespective of the source range.
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Systems and Control · Cross-listed from cs.LG
Algorithm-Relative Trajectory Valuation in Policy Gradient Control
Shihao Li, Jiachen Li, Jiamin Xu et al. (+3 more)
We study how trajectory value depends on the learning
algorithm in policy-gradient control. Using Trajectory Shapley in an uncertain LQR, we find a negative correlation between Persistence of Excitation (PE) and marginal value under vanilla REINFORCE ($r\approx-0.38$). We prove a variance-mediated mechanism: (i) for fixed energy, higher PE yields lower gradient variance; (ii) near saddles, higher variance increases escape probability, raising marginal contribution. When stabilized (state whitening or Fisher preconditioning), this variance channel is neutralized and information content dominates, flipping the correlation positive ($r\approx+0.29$). Hence, trajectory value is
algorithm-relative. Experiments validate the mechanism and show decision-aligned scores (Leave-One-Out) complement Shapley for
pruning, while Shapley identifies toxic subsets.
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All authors Shihao Li, Jiachen Li, Jiamin Xu, Christopher Martin, Wei Li, Dongmei Chen
Signal Processing
MA-Aided Hierarchical Hybrid Beamforming for Multi-User Wideband Beam Squint Mitigation
Cixiao Zhang, Yin Xu, Xinghao Guo et al. (+3 more)
In wideband near-field arrays, frequency-dependent array responses cause wavefronts at different frequencies to deviate from that at the center frequency, producing beam squint and degrading multi-user performance. True-time-delay (TTD)
circuits can realign the frequency dependence but require large delay ranges and intricate calibration, limiting scalability. Another line of work explores one- and two-dimensional array geometries, including linear, circular, and concentric circular, that exhibit distinct broadband behaviors such as different beam-squint sensitivities and focusing characteristics. These observations motivate adapting the array layout to enable wideband-friendly focusing and enhance multi-user performance without TTD networks. We propose a movable
antenna (MA) aided architecture based on hierarchical sub-connected hybrid beamforming (HSC-HBF) in which
antennas are grouped into tiles and only the tile centers are repositioned, providing slow
geometric degrees of freedom that emulate TTD-like broadband focusing while keeping hardware and optimization complexity low. We show that the steering vector is inherently frequency dependent and that reconfiguring tile locations improves broadband focusing. Simulations across wideband near-field scenarios demonstrate robust squint suppression and consistent gains over fixed-layout arrays, achieving up to 5% higher sum rate, with the maximum improvement exceeding 140%.
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All authors Cixiao Zhang, Yin Xu, Xinghao Guo, Xiaowu Ou, Dazhi He, Wenjun Zhang
Systems and Control
Comparative Study of Q-Learning for State-Feedback LQG Control with an Unknown Model
Mingxiang Liu, Damián Marelli, Minyue Fu et al. (+1 more)
We study the problem of designing a state feedback linear quadratic Gaussian (LQG) controller for a system in which the system
matrices as well as the process noise covariance are unknown. We do a rigorous comparison between two approaches. The first is the classic one in which a system identification stage is used to estimate the unknown parameters, which are then used in a state-feedback LQG (SF-LQG) controller design. The second approach is a recently proposed one using a reinforcement learning paradigm called Q-learning. We do the comparison in terms of complexity and accuracy of the resulting controller. We show that the classic approach asymptotically efficient, giving virtually no room for improvement in terms of accuracy. We also propose a novel Q-learning-based method which we show asymptotically achieves the optimal controller design. We complement our proposed method with a numerically efficient algorithmic implementation aiming at making it competitive in terms of computations. Nevertheless, our complexity analysis shows that the classic approach is still numerically more efficient than this Q-learning-based alternative. We then conclude that the classic approach remains being the best choice for addressing the SF-LQG design in the case of unknown parameters.
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All authors Mingxiang Liu, Damián Marelli, Minyue Fu, Qianqian Cai
Systems and Control · Cross-listed from cond-mat.other
A Dual-Memory Ferroelectric Transistor Emulating Synaptic Metaplasticity for High-Speed Reservoir Computing
Yifan Wang, Muhammad Sakib Shahriar, Salma Soliman et al. (+6 more)
The exponential growth of edge artificial intelligence demands material-focused solutions to overcome energy consumption and latency limitations when processing real-time temporal data. Physical reservoir computing (PRC) offers an energy-efficient paradigm but faces challenges due to limited device scalability and reconfigurability. Additionally, reservoir and readout layers require
memory of different timescales, short-term and long-term respectively - a material challenge hindering CMOS-compatible implementations. This work demonstrates a CMOS-compatible ferroelectric transistor using hafnium-zirconium-
oxide (HZO) and silicon, enabling dual-
memory operation. This system exhibits non-volatile long-term
memory (LTM) from ferroelectric HZO polarization and volatile short-term
memory (STM) from engineered non-quasi-static (NQS) channel-charge relaxation driven by gate-source/drain overlap capacitance. Ferroelectric polarization acts as non-volatile programming of volatile dynamics: by modulating threshold voltage, the ferroelectric state deterministically switches the NQS time constant and computational behavior between paired-pulse facilitation (PPF) and depression (PPD). This establishes a generalizable material-design principle applicable to diverse ferroelectric-
semiconductor heterostructures, extending beyond silicon to
oxide semiconductors and heterogeneously-integrated systems. The device solves second-order nonlinear tasks with 3.69 x 10^-3 normalized error using only 16 reservoir states - ~5x reduction - achieving 20 us response time (~1000x faster) and 1.5 x 10^-7 J energy consumption, providing an immediately manufacturable pathway for neuromorphic hardware and energy-efficient edge intelligence.
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All authors Yifan Wang, Muhammad Sakib Shahriar, Salma Soliman, Noah Vaillancourt, Lance Fernandes, Andrea Padovani, Asif Islam Khan, Md Sakib Hasan, Raisul Islam
Image and Video Processing
Deep Learning Analysis of Prenatal Ultrasound for Identification of Ventriculomegaly
Youssef Megahed, Inok Lee, Robin Ducharme et al. (+7 more)
The proposed study aimed to develop a deep learning model capable of detecting ventriculomegaly on prenatal ultrasound images. Ventriculomegaly is a prenatal condition characterized by dilated cerebral ventricles of the fetal
brain and is important to diagnose early, as it can be associated with an increased risk for fetal aneuploidies and/or underlying genetic syndromes. An Ultrasound Self-Supervised Foundation Model with Masked Autoencoding (USF-MAE), recently developed by our group, was fine-tuned for a binary classification task to distinguish fetal
brain ultrasound images as either normal or showing ventriculomegaly. The USF-MAE incorporates a
Vision Transformer encoder pretrained on more than 370,000 ultrasound images from the OpenUS-46 corpus. For this study, the pretrained encoder was adapted and fine-tuned on a curated
dataset of fetal
brain ultrasound images to optimize its performance for ventriculomegaly detection. Model evaluation was conducted using 5-fold cross-validation and an independent test cohort, and performance was quantified using accuracy, precision, recall, specificity, F1-score, and area under the receiver operating characteristic curve (AUC). The proposed USF-MAE model reached an F1-score of 91.76% on the 5-fold cross-validation and 91.78% on the independent test set, with much higher scores than those obtained by the baseline models by 19.37% and 16.15% compared to VGG-19, 2.31% and 2.56% compared to ResNet-50, and 5.03% and 11.93% compared to ViT-B/16, respectively. The model also showed a high mean test precision of 94.47% and an accuracy of 97.24%. The Eigen-CAM (Eigen Class Activation Map) heatmaps showed that the model was focusing on the ventricle area for the diagnosis of ventriculomegaly, which has explainability and clinical plausibility.
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All authors Youssef Megahed, Inok Lee, Robin Ducharme, Aylin Erman, Olivier X. Miguel, Kevin Dick, Adrian D. C. Chan, Steven Hawken, Mark Walker, Felipe Moretti
Systems and Control · Cross-listed from cs.RO
Occlusion-Aware Ground Target Search by a UAV in an Urban Environment
Collin Hague and Artur Wolek
This paper considers the problem of searching for a point of interest (POI) moving along an urban road network with an uncrewed aerial vehicle (
UAV). The
UAV is modeled as a variable-speed Dubins vehicle with a line-of-sight sensor in an urban environment that may occlude the sensor's view of the POI. A search strategy is proposed that exploits a probabilistic visibility volume (VV) to plan its future motion with iterative deepening $A^\ast$. The probabilistic VV is a time-varying three-dimensional representation of the sensing constraints for a particular distribution of the POI's state. To find the path most likely to view the POI, the planner uses a heuristic to optimistically estimate the probability of viewing the POI over a time horizon. The probabilistic VV is max-pooled to create a variable-timestep planner that reduces the search space and balances long-term and short-term planning. The proposed path planning method is compared to prior work with a Monte-Carlo simulation and is shown to outperform the baseline methods in cluttered environments when the
UAV's sensor has a higher false alarm probability.
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Systems and Control · Cross-listed from cs.RO
SONIC: Supersizing Motion Tracking for Natural Humanoid Whole-Body Control
Zhengyi Luo, Ye Yuan, Tingwu Wang et al. (+25 more)
Despite the rise of billion-parameter foundation models trained across thousands of graphical processing units (GPUs), similar scaling gains have not been shown for
humanoid control. Current neural controllers for
humanoids remain modest in size, target a limited set of behaviors, and are trained on a handful of GPUs. We show that scaling model capacity, data, and compute yields a generalist
humanoid controller capable of natural, robust whole-body movements. We position motion tracking as a scalable task for
humanoid control, leveraging dense supervision from diverse motion-capture data to acquire human motion priors without manual reward engineering. We build a foundation model for motion tracking by scaling along three axes: network size (1.2M to 42M parameters),
dataset volume (100M+ frames from 700 hours of motion capture), and compute (21k GPU hours). Beyond demonstrating the benefits of scale, we further show downstream utility through a real-time kinematic planner that bridges motion tracking to tasks such as
navigation, enabling natural and interactive control, as well as a unified
token space that supports virtual reality (VR) teleoperation and
vision-
language-action (VLA) models with a single policy. Through this interface, we demonstrate autonomous VLA-driven whole-body loco-manipulation requiring coordinated hand and foot placement. Scaling motion tracking exhibits favorable properties: performance improves steadily with compute and data diversity, and learned policies generalize to unseen motions, establishing motion tracking at scale as a practical foundation for
humanoid control.
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All authors Zhengyi Luo, Ye Yuan, Tingwu Wang, Chenran Li, Fernando Castañeda, Sirui Chen, Zi-Ang Cao, Jiefeng Li, David Minor, Qingwei Ben, Jinhyung Park, David Sami, Zi Wang, Xingye Da, Runyu Ding, Cyrus Hogg, Lina Song, Edy Lim, Eugene Jeong, Tairan He, Haoru Xue, Wenli Xiao, Simon Yuen, Jan Kautz, Yan Chang, Umar Iqbal, Linxi "Jim" Fan, Yuke Zhu
Systems and Control
Experimental Evaluation of Fuzzy-Integral and Classical controls for Power Management in a 24 GHz mmWave 5G Transceiver
Karel Walter Gomez Orellana, Berthyn Rodrigo Tiñini Chuquimia, Juan Carlos Paredes Condori et al. (+2 more)
The deployment of 5G millimeter-wave (mmWave) systems poses significant challenges in maintaining power amplifier linearity and efficiency under varying conditions, such as temperature-induced gain variations that degrade error vector magnitude (EVM). This paper presents a comparative study of three control strategies-PID, pure integral, and fuzzy-integral (FI)-for adaptive power management in a 24 GHz mmWave transceiver. The FI controller integrates fuzzy logic for handling nonlinearities with integral action for zero steady-state error. Experimental results show the FI controller outperforms others in settling time, stability, and EVM minimization.
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All authors Karel Walter Gomez Orellana, Berthyn Rodrigo Tiñini Chuquimia, Juan Carlos Paredes Condori, Rodrigo Apaza Huanca, Hugo Orlando Condori Quispe
Systems and Control · Cross-listed from math.DS
Beyond Carleman Linearization of Nonlinear Dynamical System: Insights from a Case Study
Panpan Chen, Nader Motee and Qiyu Sun
Nonlinear dynamical systems are widely encountered in various scientific and engineering fields. Despite significant advances in theoretical understanding, developing complete and integrated frameworks for analyzing and designing these systems remains challenging, which underscores the importance of efficient linearization methods. In this paper, we introduce a general linearization framework with emphasis on Carleman linearization and Carleman-
Fourier linearization. A detailed case study on finite-section approximation to the lifted infinite-dimensional dynamical system is provided for the dynamical system with its governing function being a trigonometric
polynomial of degree one.
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Signal Processing
Deep-Learning-based Frequency-Domain Watermarking for Energy System Time Series Data Asset Protection
Zhenghao Zhou, Yiyan Li, Xinjie Yu et al. (+4 more)
Data has been regarded as a valuable asset with the fast development of artificial intelligence technologies. In this paper, we introduce deep-learning neural network-based frequency-domain watermarking for protecting energy system time series data assets and secure data authenticity when being shared or traded across communities. First, the concept and desired watermarking characteristics are introduced. Second, a deep-learning neural network-based watermarking model with specially designed loss functions and network structure is proposed to embed watermarks into the original
dataset. Third, a frequency-domain data preprocessing method is proposed to eliminate the frequency bias of neural networks when learning time series
datasets to enhance the model performances. Last, a comprehensive watermarking performance evaluation framework is designed for measuring its invisibility, restorability, robustness, secrecy, false-positive detection, generalization, and capacity. Case studies based on practical load and photovoltaic time series
datasets demonstrate the effectiveness of the proposed method.
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All authors Zhenghao Zhou, Yiyan Li, Xinjie Yu, Jian Ping, Xiaoyuan Xu, Zheng Yan, Mohammad Shahidehpour
Image and Video Processing
Deep generative priors for robust and efficient electron ptychography
Arthur R. C. McCray, Stephanie M. Ribet, Georgios Varnavides et al. (+1 more)
Electron ptychography enables dose-efficient atomic-resolution imaging, but conventional reconstruction
algorithms suffer from noise sensitivity, slow convergence, and extensive manual hyperparameter tuning for regularization, especially in three-dimensional multislice reconstructions. We introduce a deep generative prior (DGP) framework for electron ptychography that uses the implicit regularization of convolutional neural networks to address these challenges. Two DGPs parameterize the complex-valued sample and probe within an automatic-differentiation mixed-state multislice forward model. Compared to pixel-based reconstructions, DGPs offer four key advantages: (i) greater noise robustness and improved information limits at low dose; (ii) markedly faster convergence, especially at low spatial frequencies; (iii) improved depth regularization; and (iv) minimal user-specified regularization. The DGP framework promotes spatial coherence and suppresses high-frequency noise without extensive tuning, and a pre-training strategy stabilizes reconstructions. Our results establish DGP-enabled ptychography as a robust approach that reduces expertise barriers and computational cost, delivering robust, high-resolution imaging across diverse materials and biological systems.
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All authors Arthur R. C. McCray, Stephanie M. Ribet, Georgios Varnavides, Colin Ophus
Signal Processing
Generative Decoding of Compressed CSI for MIMO Precoding Design
Hao Luo, Saeed R. Khosravirad and Ahmed Alkhateeb
Massive
MIMO systems can enhance spectral and energy efficiency, but they require accurate channel state information (CSI), which becomes costly as the number of
antennas increases. While machine learning (ML) autoencoders show promise for CSI reconstruction and reducing feedback overhead, they introduce new challenges with standardization, interoperability, and backward compatibility. Also, the significant data collection needed for training makes real-world deployment difficult. To overcome these drawbacks, we propose an ML-based, decoder-only solution for compressed CSI. Our approach uses a standardized encoder for CSI compression on the user side and a site-specific generative decoder at the base station to refine the compressed CSI using environmental knowledge. We introduce two training schemes for the generative decoder: An end-to-end method and a two-stage method, both utilizing a goal-oriented loss function. Furthermore, we reduce the data collection overhead by using a site-specific digital twin to generate synthetic CSI data for training. Our simulations highlight the effectiveness of this solution across various feedback overhead regimes.
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Signal Processing
A Causal-Guided Multimodal Large Language Model for Generalized Power System Time-Series Data Analytics
Zhenghao Zhou, Yiyan Li, Xinjie Yu et al. (+6 more)
Power system time series analytics is critical in understanding the system operation conditions and predicting the future trends. Despite the wide adoption of Artificial Intelligence (AI) tools, many AI-based time series analytical models suffer from task-specificity (i.e. one model for one task) and structural
rigidity (i.e. the input-output format is fixed), leading to limited model performances and resource wastes. In this paper, we propose a Causal-Guided
Multimodal Large
Language Model (CM-LLM) that can solve heterogeneous power system time-series analysis tasks. First, we introduce a physics-statistics combined causal discovery mechanism to capture the causal relationship, which is represented by graph, among power system variables. Second, we propose a
multimodal data preprocessing framework that can encode and fuse text, graph and time series to enhance the model performance. Last, we formulate a generic "mask-and-reconstruct" paradigm and design a dynamic input-output padding mechanism to enable CM-LLM adaptive to heterogeneous time-series analysis tasks with varying sample lengths. Simulation results based on open-source LLM Qwen and real-world
dataset demonstrate that, after simple fine-tuning, the proposed CM-LLM can achieve satisfying accuracy and efficiency on three heterogeneous time-series analytics tasks: missing data imputation,
forecasting and super resolution.
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All authors Zhenghao Zhou, Yiyan Li, Xinjie Yu, Runlong Liu, Zelin Guo, Zheng Yan, Mo-Yuen Chow, Yuqi Yang, Yang Xu
Systems and Control
AURORA: Autonomous Updating of ROM and Controller via Recursive Adaptation
Jiachen Li, Shihao Li and Dongmei Chen
Real time model based control of high dimensional nonlinear systems presents severe computational challenges. Conventional reduced order model control relies heavily on expert tuning or parameter adaptation and seldom offers mechanisms for online supervised reconstruction. We introduce AURORA, Autonomous Updating of ROM and Controller via Recursive Adaptation, a supervisory framework that automates ROM based controller design and augments it with diagnostic triggered structural adaptation. Five specialized
agents collaborate through iterative generate judge revise cycles, while an Evaluation
Agent classifies performance degradation into three operationally distinct categories, subspace inadequacy, parametric drift, and control inadequacy, and routes corrective action to the responsible
agent. For linear ROMs, we analytically prove that this classification is correct under mild assumptions and that the supervisory switching cycle preserves exponential stability subject to a dwell time condition. For nonlinear systems, the absence of a universal Lyapunov construction for autonomously discovered ROM structures precludes analogous analytical guarantees, so we validate the same classification empirically. Experiments on eight
benchmark systems with state dimensions up to 5177 compare AURORA against expert tuned baselines, gain scheduled control, and online RLS adaptive alternatives. Controlled fault injection experiments confirm 91 percent diagnostic routing accuracy. AURORA achieves 6 to 12 percent tracking improvement over expert baselines and 4 to 5 percent over classical adaptive alternatives.
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Signal Processing · Cross-listed from cs.LG
Global Optimization on Graph-Structured Data via Gaussian Processes with Spectral Representations
Shu Hong, Yongsheng Mei, Mahdi Imani et al. (+1 more)
Bayesian optimization (BO) is a powerful framework for optimizing expensive black-box objectives, yet extending it to graph-structured domains remains challenging due to the discrete and combinatorial nature of graphs. Existing approaches often rely either on full graph
topology, which is impractical for large or partially observed graphs, or on incremental local exploration, which can lead to slow convergence. We introduce a scalable framework for global optimization over graphs that builds Gaussian process (GP) surrogates from low-rank spectral representations inferred from sparse structural observations. The method fits a low-rank graph surrogate to the observed connections and uses its spectrum to define a kernel over all nodes, so that function evaluations and structural observations jointly refine a global model, enabling efficient global search and principled uncertainty estimation even with limited data. We further show how a limited budget of structural observations can be allocated according to its expected value for the optimization. We also provide theoretical analysis establishing conditions under which the surrogate geometry is identified under different sampling regimes, and bounding the optimization error incurred under an estimated geometry. Experiments on synthetic and real-world
datasets demonstrate that our approach achieves lower regret and improved optimization performance compared to prior methods.
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All authors Shu Hong, Yongsheng Mei, Mahdi Imani, Tian Lan
Systems and Control
Synergistic Development of Cybersecurity and Functional Safety for Smart Electric Vehicles
Siddhesh Pimpale
The introduction of Smart Electric Vehicles (SEVs) represents an increasingly disruption on automotive area, once integrates advanced computer and communication technologies to highly electrical cars, which come with high performances, environment friendly and user friendly characteristics . But the increasing complexity of SEVs prompted by greater dependence on interconnected systems, autonomous capabilities and electrification, presents new challenges in cybersecurity as well as functional safety. The safety and reliability of such vehicles is paramount, as unsafe or unreliable operation in either case represents an unacceptable risk in terms of the performance of the vehicle and safety of the passenger. This paper investigates the integrated development of cybersecurity and functional safety for SEVs, emphasizing the requirement for the parallel development of these domains as components that are not treated separately. In SEVs, cybersecurity is quite crucial in order to prevent the threats of hacking, data breaches and unauthorized access to vehicle systems. Functional safety ensures that important vehicle functions (braking, steering, battery control, etc.) keep working even if some part fails. This convergence of functional safety issues with cybersecurity issues is becoming more crucial, since a security incident can result in a failure of catastrophic consequences for a functional safety system and, conversely. This paper reports the current state of cybersecurity and functional safety standards for SEVs, highlighting challenges that include the weaknesses of communication networks, the potential security threats of over-the-air updates, and the demand for real-time responsive systems for failure.
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Systems and Control · Cross-listed from math.OC
Geometric Conditions for Lossless Convexification in Linear Optimal Control with Discrete-Valued Inputs: Real-Time Implementation for Spacecraft Rendezvous
Felipe Arenas-Uribe, Hasan A. Poonawala and Jesse B. Hoagg
Optimal control problems with discrete-valued inputs are inherently challenging due to their mixed-integer nature, rendering them generally intractable for real-time, safety-critical aerospace applications. Lossless convexification offers a powerful alternative by reformulating these mixed-integer programs into computationally efficient convex programs. This paper develops a lossless convexification framework for the optimal control of linear time-varying systems with discrete-valued inputs. We extend existing theoretical results by demonstrating that system normality is preserved when reformulating Lagrange-form problems into Mayer-form via an epigraph transformation. Furthermore, we establish that under simple
geometric conditions on the input set, the solution to the relaxed convex problem strictly satisfies the original non-convex input constraints. This framework enables the real-time computation of optimal discrete-valued controls without resorting to mixed-integer optimization. The proposed
algorithm is validated on a spacecraft rendezvous maneuver utilizing discrete-valued reaction thrusters in an elliptical orbit. Numerical results from Monte Carlo simulations confirm that the
algorithm consistently yields exact discrete-valued control inputs with computational timelines compatible with safety-critical, on-board applications.
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