Signal Processing
Modelling Real-Life Cycling Decisions in Real Urban Settings Through Psychophysiology and LLM-Derived Contextual Data
Maximiliano Rosadio Z., Angel Jimenez-Molina, Bastián Henríquez et al. (+5 more)
Measuring emotional states in transportation contexts is an emerging field. Methods based on self-reported
emotions are limited by their low granularity and their susceptibility to
memory bias. In contrast, methods based on physiological indicators provide continuous data, enabling researchers to measure changes in emotional states with high detail and accuracy. Not only are
emotions important in the analysis, but understanding what triggers emotional changes is equally important. Uncontrolled variables such as traffic conditions, pedestrian interactions, and infrastructure remain a significant challenge, as they can have a great impact on emotional states. Explaining the reasons behind these emotional states requires gathering sufficient and proper contextual data, which can be extremely difficult in real-world environments. This paper addresses these challenges by applying an innovative approach, extracting contextual data (expert annotator level) from recorded multimedia using large
language models (LLMs). In this paper, data are collected from an urban cycling case study of the City of Santiago, Chile. The applied models focus on understanding how different environments and traffic situations affect the emotional states and behaviors of the participants using physiological data. Sequences of images, extracted from the recorded
videos, are processed by LLMs to obtain semantic descriptions of the environment. These discrete, although dense and detailed, contextual data are integrated into a hybrid model, where fatigue and arousal serve as latent variables influencing observed cycling behaviors (inferred from GPS data) like waiting, accelerating, braking, etc. The study confirms that cycling decisions are influenced by stress-related
emotions and highlights the strong impact of urban characteristics and traffic conditions on cyclist behavior.
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All authors Maximiliano Rosadio Z., Angel Jimenez-Molina, Bastián Henríquez, Paulina Leiva, Ricardo Hurtubia, Ricardo De La Paz Guala, Leandro Gayozo, C. Angelo Guevara
Systems and Control · Cross-listed from physics.app-ph
Robust interpretation of electrochemical impedance spectra using numerical complex analysis
Jithin D. George, Willa Brenneis, Vinod K. Sangwan et al. (+9 more)
Electrochemical Impedance Spectroscopy (EIS) is a non-invasive technique widely used for understanding charge transfer and charge transport processes in electrochemical systems and devices. Standard approaches for the interpretation of EIS data involve starting with a hypothetical
circuit model for the physical processes in the device based on experience/intuition, and then fitting the EIS data to this
circuit model. This work explores a mathematical approach for extracting key characteristic features from EIS data by relying on fundamental principles of complex analysis. These characteristic features can ascertain the presence of inductors and constant phase elements (non-ideal capacitors) in
circuit models and enable us to answer questions about the identifiability and uniqueness of equivalent
circuit models. In certain scenarios such as models with only resistors and capacitors, we are able to enumerate all possible families of
circuit models. Finally, we apply the mathematical framework presented here to real-world electrochemical systems and highlight results using impedance measurements from a lithium-ion battery coin
cell.
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All authors Jithin D. George, Willa Brenneis, Vinod K. Sangwan, Dilara Meli, Heather Kurtz, Jeffrey Richards, Lincoln J. Lauhon, Jonathan Rivnay, Mark C. Hersam, Jeffrey Lopez, Maria K. Y. Chan, Valerie Taylor
Signal Processing
Synthetic EEG Generation using Diffusion Models for Motor Imagery Tasks
Henrique de Lima Alexandre and Clodoaldo Aparecido de Moraes Lima
Electroencephalography (
EEG) is a widely used, non-invasive method for capturing
brain activity, and is particularly relevant for applications in
Brain-Computer Interfaces (BCI). However, collecting high-quality
EEG data remains a major challenge due to sensor costs, acquisition time, and inter-subject variability. To address these limitations, this study proposes a methodology for generating synthetic
EEG signals associated with motor imagery
brain tasks using
Diffusion Probabilistic Models (DDPM). The approach involves preprocessing real
EEG data, training a
diffusion model to reconstruct
EEG channels from noise, and evaluating the quality of the generated signals through both signal-level and task-level metrics. For validation, we employed classifiers such as K-Nearest Neighbors (KNN), Convolutional Neural Networks (CNN), and U-Net to compare the performance of synthetic data against real data in classification tasks. The generated data achieved classification accuracies above 95%, with low mean squared error and high correlation with real signals. Our results demonstrate that synthetic
EEG signals produced by
diffusion models can effectively complement
datasets, improving classification performance in
EEG-based BCIs and addressing data scarcity.
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Audio and Speech Processing · Cross-listed from cs.SD
EGSTalker: Real-Time Audio-Driven Talking Head Generation with Efficient Gaussian Deformation
Tianheng Zhu, Yinfeng Yu, Liejun Wang et al. (+2 more)
This paper presents EGSTalker, a real-time audio-driven talking head generation framework based on 3D Gaussian Splatting (3DGS). Designed to enhance both speed and visual fidelity, EGSTalker requires only 3-5 minutes of training
video to synthesize high-quality facial animations. The framework comprises two key stages: static Gaussian initialization and audio-driven deformation. In the first stage, a multi-resolution hash triplane and a Kolmogorov-Arnold Network (KAN) are used to extract spatial features and construct a compact 3D Gaussian representation. In the second stage, we propose an Efficient Spatial-Audio
Attention (ESAA) module to fuse audio and spatial cues, while KAN predicts the corresponding Gaussian deformations. Extensive experiments demonstrate that EGSTalker achieves rendering quality and lip-sync accuracy comparable to state-of-the-art methods, while significantly outperforming them in inference speed. These results highlight EGSTalker's potential for real-time multimedia applications.
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All authors Tianheng Zhu, Yinfeng Yu, Liejun Wang, Fuchun Sun, Wendong Zheng
Systems and Control · Cross-listed from cs.LG
Generalization of Graph Neural Network Models for Distribution Grid Fault Detection
Burak Karabulut, Carlo Manna and Chris Develder
Fault detection in power distribution grids is critical for ensuring system reliability and preventing costly outages. Moreover, fault detection methodologies should remain robust to evolving grid
topologies caused by factors such as reconfigurations, equipment failures, and Distributed Energy Resource (DER) integration. Current data-driven state-of-the-art methods use Recurrent Neural Networks (RNNs) for temporal modeling and Graph Neural Networks (GNNs) for spatial learning, in an RNN+GNN pipeline setting (RGNN in short). Specifically, for power system fault diagnosis, Graph Convolutional Networks (GCNs) have been adopted. Yet, various more advanced GNN architectures have been proposed and adopted in domains outside of power systems. In this paper, we set out to systematically and consistently
benchmark various GNN architectures in an RNN+GNN pipeline model. Specifically, to the best of our knowledge, we are the first to (i) propose to use GraphSAGE and Graph
Attention (GAT, GATv2) in an RGNN for fault diagnosis, and (ii) provide a comprehensive
benchmark against earlier proposed RGNN solutions (RGCN) as well as pure RNN models (especially Gated Recurrent Unit (GRU)), particularly (iii) exploring their generalization potential for deployment in different settings than those used for training them. Our experimental results on the IEEE 123-node distribution network show that RGATv2 has superior generalization capabilities, maintaining high performance with an F1-score reduction of $\sim$12% across different
topology settings. In contrast, pure RNN models largely fail, experiencing an F1-score reduction of up to $\sim$60%, while other RGNN variants also exhibit significant performance degradation, i.e., up to $\sim$25% lower F1-scores.
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Image and Video Processing
How We Won BraTS-SSA 2025: Brain Tumor Segmentation in the Sub-Saharan African Population Using Segmentation-Aware Data Augmentation and Model Ensembling
Claudia Takyi Ankomah, Livingstone Eli Ayivor, Ireneaus Nyame et al. (+5 more)
Brain tumors, particularly gliomas, pose significant chall-enges due to their complex growth patterns, infiltrative nature, and the variability in
brain structure across individuals, which makes accurate diagnosis and monitoring difficult. Deep learning models have been developed to accurately delineate these tumors. However, most of these models were trained on relatively homogenous high-resource
datasets, limiting their robustness when deployed in underserved regions. In this study, we performed
segmentation-aware offline data augmentation on the BraTS-Africa
dataset to increase the data sample size and diversity to enhance generalization. We further constructed an ensemble of three distinct architectures, MedNeXt, SegMamba, and Residual-Encoder U-Net, to leverage their complementary strengths. Our best-performing model, MedNeXt, was trained on 1000 epochs and achieved the highest average lesion-wise dice and normalized surface distance scores of 0.86 and 0.81 respectively. However, the ensemble model trained for 500 epochs produced the most balanced
segmentation performance across the tumour subregions. This work demonstrates that a combination of advanced augmentation and model ensembling can improve
segmentation accuracy and robustness on diverse and underrepresented
datasets. Code available at: https://github.com/SPARK-Academy-2025/SPARK-2025/tree/main/SPARK2025_BraTs_MODELS/SPARK_NeuroAshanti
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All authors Claudia Takyi Ankomah, Livingstone Eli Ayivor, Ireneaus Nyame, Leslie Wambo, Patrick Yeboah Bonsu, Aondona Moses Iorumbur, Raymond Confidence, Toufiq Musah
Systems and Control · Cross-listed from cs.MA
Long-Term Mapping of the Douro River Plume with Multi-Agent Reinforcement Learning
Nicolò Dal Fabbro, Milad Mesbahi, Renato Mendes et al. (+2 more)
We study the problem of long-term (multiple days) mapping of a river plume using multiple autonomous
underwater vehicles (AUVs), focusing on the Douro river representative use-case. We propose an energy - and communication - efficient multi-
agent reinforcement learning approach in which a central coordinator intermittently communicates with the AUVs, collecting measurements and issuing commands. Our approach integrates spatiotemporal Gaussian process regression (GPR) with a multi-head Q-network controller that regulates direction and speed for each AUV. Simulations using the Delft3D ocean model demonstrate that our method consistently outperforms both single- and multi-
agent benchmarks, with scaling the number of
agents both improving mean squared error (MSE) and operational endurance. In some instances, our
algorithm demonstrates that doubling the number of AUVs can more than double endurance while maintaining or improving accuracy, underscoring the benefits of multi-
agent coordination. Our learned policies generalize across unseen seasonal regimes over different months and years, demonstrating promise for future developments of data-driven long-term monitoring of dynamic plume environments.
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All authors Nicolò Dal Fabbro, Milad Mesbahi, Renato Mendes, João Borges de Sousa, George J. Pappas
Systems and Control · Cross-listed from cs.LG
Certifiable Safe RLHF: Semantic Grounding and Fixed Penalty Constraint Optimization for Safer LLM Alignment
Kartik Pandit, Sourav Ganguly, Arnesh Banerjee et al. (+2 more)
Ensuring safety is a foundational requirement for large
language models (LLMs). Achieving an appropriate balance between enhancing the utility of model outputs and mitigating their potential for harm is a complex and persistent challenge. Contemporary approaches frequently formalize this problem within the framework of Constrained Markov Decision Processes (CMDPs) and employ established CMDP optimization techniques. However, these methods exhibit two notable limitations. First, their reliance on reward and cost functions renders performance highly sensitive to the underlying scoring mechanism, which must capture semantic meaning rather than being triggered by superficial keywords. Second, CMDP-based training entails tuning dual-variable, a process that is both computationally expensive and does not provide any provable safety guarantee for a fixed dual variable that can be exploitable through
adversarial jailbreaks. To overcome these limitations, we introduce Certifiable Safe-RLHF (CS-RLHF) that introduces a cost model trained on a large-scale corpus to assign semantically grounded safety scores. In contrast to the lagrangian-based approach, CS-RLHF adopts a rectified penalty-based formulation. This design draws on the theory of exact penalty functions in constrained optimization, wherein constraint satisfaction is enforced directly through a suitably chosen penalty term. With an appropriately scaled penalty, feasibility of the safety constraints can be guaranteed at the optimizer, eliminating the need for dual-variable updates. Empirical evaluation demonstrates that CS-RLHF outperforms state-of-the-art LLM model responses rendering at-least 5 times efficient against nominal and jail-breaking
prompts
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All authors Kartik Pandit, Sourav Ganguly, Arnesh Banerjee, Shaahin Angizi, Arnob Ghosh
Signal Processing
COMET: Co-Optimization of a CNN Model using Efficient-Hardware OBC Techniques
Boyang Chen, Mohd Tasleem Khan, George Goussetis et al. (+4 more)
Convolutional Neural Networks (CNNs) achieve remarkable accuracy in
vision tasks, yet their computational complexity challenges low-power edge deployment. In this work, we present COMET, a framework of CNN models that employ efficient hardware offset-binary coding (OBC) techniques to enable co-optimization of performance and resource utilization. The approach formulates CNN inference using OBC representations applied separately to inputs (Scheme A) and weights (Scheme B), enabling exploitation of bit-width asymmetry. The shift-accumulate operation is modified by incorporating offset-term with the pre-scaled bias. Leveraging
symmetries in Schemes A and B, we introduce four look-up table (LUT) techniques – parallel, shared, split, and hybrid – and evaluate their efficiency. Building on this foundation, we develop a general
matrix multiplication core using the im2col transformation for efficient CNN acceleration. We consider LeNet-5 and All-CNN-C to demonstrate that the OBC-GEMM core efficiently supports modern workloads. Evaluation shows that COMET enables efficient FPGA deployment compared to state-of-the-art designs, with negligible accuracy loss, demonstrating its efficiency and scalability across diverse network architectures.
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All authors Boyang Chen, Mohd Tasleem Khan, George Goussetis, Mathini Sellathurai, Yuan Ding, João F. C. Mota, Jongeun Lee
Image and Video Processing · Cross-listed from cs.CV
Platonic Transformers: A Solid Choice For Equivariance
Mohammad Mohaiminul Islam, Rishabh Anand, David R. Wessels et al. (+7 more)
While widespread,
Transformers lack inductive biases for
geometric symmetries common in science and computer
vision. Existing equivariant methods often sacrifice the efficiency and flexibility that make
Transformers so effective through complex, computationally intensive designs. We introduce the Platonic
Transformer to resolve this trade-off. By defining
attention relative to reference frames from the Platonic solid
symmetry groups, our method induces a principled weight-sharing scheme. This enables combined equivariance to continuous translations and Platonic
symmetries, while preserving the exact architecture and computational cost of a standard
Transformer. Furthermore, we show that this
attention is formally equivalent to a dynamic group convolution, which reveals that the model learns adaptive
geometric filters and enables a highly scalable, linear-time convolutional variant. Across diverse
benchmarks in computer
vision (CIFAR-10), 3D point clouds (ScanObjectNN), and molecular property prediction (QM9, OMol25), the Platonic
Transformer achieves competitive performance by leveraging these
geometric constraints at no additional cost.
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All authors Mohammad Mohaiminul Islam, Rishabh Anand, David R. Wessels, Friso de Kruiff, Thijs P. Kuipers, Rex Ying, Clara I. Sánchez, Sharvaree Vadgama, Georg Bökman, Erik J. Bekkers
Systems and Control
Machine Learning-Driven Prediction of Lithium-Ion Battery Power Capability for eVTOL Aircraft
Hao Tu, Yebin Wang, Shaoshuai Mou et al. (+1 more)
Electric vertical take-off and landing (eVTOL) aircraft have emerged as a promising solution to transform urban transportation. They present a few technical challenges for battery management, a prominent one of which is the prediction of the power capability of their lithium-ion battery systems. The challenge originates from the high C-rate discharging conditions required during eVTOL flights as well as the complexity of lithium-ion batteries' electro-thermal dynamics. This paper, for the first time, formulates a power limit prediction problem for eVTOL which explicitly considers long prediction horizons and the possible occurrence of emergency landings. We then harness machine learning to solve this problem in two intertwined ways. First, we adopt a dynamic model that integrates physics with machine learning to predict a lithium-ion battery's voltage and temperature behaviors with high accuracy. Second, while performing search for the maximum power, we leverage machine learning to predict the remaining discharge time and use the prediction to accelerate the search with fast computation. Our validation results show the effectiveness of the proposed study for eVTOL operations.
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All authors Hao Tu, Yebin Wang, Shaoshuai Mou, Huazhen Fang
Systems and Control · Cross-listed from math.OC
Contingency-Aware Nodal Optimal Power Investments with High Temporal Resolution
Thomas Lee and Andy Sun
We present CANOPI, a novel algorithmic framework, for solving the Contingency-Aware Nodal Power Investments problem, a large-scale nonlinear optimization problem that jointly optimizes investments in generation, storage, and transmission upgrades, including representations of unit commitment and long-duration storage. The problem's scale arises from the confluence of spatial and temporal resolutions, along with the large number of contingency constraints. Further, the underlying problem is nonlinear due to transmission upgrades' impact on impedances. We propose algorithmic approaches to address these computational challenges. We pose a linear approximation and develop a fixed-point
algorithm to adjust for nonlinear impedance feedback. We solve the large-scale linear expansion model with a specialized level-bundle method leveraging a novel interleaved approach to contingency constraint generation. We introduce a minimum cycle basis
algorithm that improves numerical sparsity and solve times of cycle-based DC power flow. CANOPI is demonstrated on a 1493-bus Western Interconnection test system built from realistic-geography network data, with hourly operations spanning 52 week-long scenarios and a total possible set of 20 billion individual transmission contingency constraints. Numerical experiments quantify reliability and economic benefits of incorporating transmission contingencies in integrated planning models and highlight the computational advantages of the proposed methods.
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Systems and Control · Cross-listed from cs.RO
Optimization-Based Robust Permissive Synthesis for Interval MDPs
Khang Vo Huynh, David Parker and Lu Feng
We present an optimization-based framework for robust permissive synthesis for Interval Markov Decision Processes (IMDPs). While robust IMDP controller synthesis typically yields a single policy and most permissive-synthesis methods assume exact transition models, we synthesize multi-strategies that retain multiple actions while guaranteeing satisfaction of probabilistic reachability or expected-reward specifications under all admissible transition probabilities. We formulate the problem as a mixed-integer linear program (MILP) that maximizes the number of enabled state–action pairs subject to robust Bellman constraints. We develop two encodings: a direct vertex-enumeration formulation and a dualization-based formulation that avoids explicit enumeration of uncertainty-polytope vertices and has size linear in the number of successor transitions. Experiments on four
benchmark domains show that both encodings achieve the same optimal permissiveness and scale to IMDPs with hundreds of thousands of states. Compared with standard robust single-policy synthesis, the resulting multi-strategies retain substantially more action choices.
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Systems and Control · Cross-listed from math.OC
A Sequential Quadratic Programming Perspective on Optimal Control
Abhijeet and Suman Chakravorty
This paper offers a unified perspective on different approaches to the solution of optimal control problems through the lens of constrained sequential quadratic programming. In particular, it allows us to find the relationships between Newton's method, the iterative LQR (iLQR), and Differential Dynamic Programming (DDP) approaches to solve the problem. It is shown that the iLQR is a principled SQP approach, rather than simply an approximation of DDP by neglecting the Hessian terms, to solve optimal control problems that can be guaranteed to always produce a cost-descent direction and converge to an optimum; while Newton's approach or DDP do not have similar guarantees, especially far from an optimum. Our empirical evaluations on the pendulum and cart-pole swing-up tasks serve to corroborate the SQP-based analysis proposed in this paper.
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Systems and Control · Cross-listed from math.OC
Cooling Under Convexity: An Inventory Control Perspective on Industrial Refrigeration
Vade Shah, Yohan John, Ethan Freifeld et al. (+2 more)
Industrial refrigeration systems have substantial energy needs, but optimizing their operation remains challenging due to the tension between minimizing energy costs and meeting strict cooling requirements. Load shifting–strategic overcooling in anticipation of future demands–offers substantial efficiency gains. This work seeks to rigorously quantify these potential savings through the derivation of optimal load shifting policies. Our first contribution establishes a novel connection between industrial refrigeration and inventory control problems with convex ordering costs, where the convexity arises from the relationship between energy consumption and cooling capacity. Leveraging this formulation, we derive three main theoretical results: (1) an optimal
algorithm for deterministic demand scenarios, along with proof that optimal trajectories are non-increasing (a valuable structural insight for practical control); (2) performance bounds that quantify the value of load shifting as a function of cost convexity, demand variability, and temporal patterns; (3) a computationally tractable load shifting heuristic with provable near-optimal performance under uncertainty. Numerical simulations validate our theoretical findings, and a case study using real industrial refrigeration data demonstrates an opportunity for improved load shifting.
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All authors Vade Shah, Yohan John, Ethan Freifeld, Lily Y. Chen, Jason R. Marden
Systems and Control · Cross-listed from cs.NI
Scalable Ground Station Selection for Large LEO Constellations
Grace Ra Kim, Duncan Eddy, Vedant Srinivas et al. (+1 more)
Effective ground station selection is critical for low Earth orbiting (LEO) satellite constellations to minimize operational costs, maximize data downlink volume, and reduce communication gaps between access windows. Traditional ground station selection typically begins by choosing from a fixed set of locations offered by Ground Station-as-a-Service (GSaaS) providers, which helps reduce the problem scope to optimizing locations over existing infrastructure. However, finding a globally optimal solution for stations using existing mixed-integer programming methods quickly becomes intractable at scale, especially when considering multiple providers and large satellite constellations. To address this issue, we introduce a scalable, hierarchical framework that decomposes the global selection problem into single-satellite, short time-window subproblems. Optimal station choices from each subproblem are clustered to identify consistently high-value locations across all decomposed cases. Cluster-level sets are then matched back to the closest GSaaS candidate sites to produce a globally feasible solution. This approach enables scalable coordination while maintaining near-optimal performance. We evaluate our method's performance on synthetic Walker-
Star test cases (1-10 satellites, 1-10 stations), achieving solutions within 95% of the global IP optimum for all test cases. Real-world evaluations on Capella Space (5 satellites), ICEYE (40), and
Planet's Flock (96) show that while exact IP solutions fail to scale, our framework continues to deliver high-quality site selections.
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All authors Grace Ra Kim, Duncan Eddy, Vedant Srinivas, Mykel J. Kochenderfer
Signal Processing · Cross-listed from physics.med-ph
Application of a Virtual Imaging Framework for Investigating a Deep Learning-Based Reconstruction Method for 3D Quantitative Photoacoustic Computed Tomography
Refik Mert Cam, Seonyeong Park, Umberto Villa et al. (+1 more)
Quantitative photoacoustic computed
tomography (qPACT) is a promising imaging modality for estimating physiological parameters such as blood oxygen saturation. However, developing robust qPACT reconstruction methods remains challenging due to computational demands, modeling difficulties, and experimental uncertainties. Learning-based methods have been proposed to address these issues but remain largely unvalidated. Virtual imaging (VI) studies are essential for validating such methods early in development, before proceeding to less-controlled phantom or in vivo studies. Effective VI studies must employ ensembles of stochastically generated numerical phantoms that accurately reflect relevant anatomy and physiology. Yet, most prior VI studies for qPACT relied on overly simplified phantoms. In this work, a realistic VI testbed is employed for the first time to assess a representative 3D learning-based qPACT reconstruction method for breast imaging. The method is evaluated across subject variability and physical factors such as measurement noise and acoustic aberrations, offering insights into its strengths and limitations.
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All authors Refik Mert Cam, Seonyeong Park, Umberto Villa, Mark A. Anastasio
Systems and Control · Cross-listed from math.OC
Efficient Input-Constrained Impulsive Optimal Control of Linear Systems with Application to Spacecraft Relative Motion
Ethan Foss and Simone D'Amico
This work presents a novel
algorithm for impulsive optimal control of linear time-varying systems with the inclusion of input magnitude constraints. Impulsive optimal control problems, where the optimal input solution is a sum of delta functions, are typically formulated as an optimization over a normed function space subject to integral equality constraints and can be efficiently solved for linear time-varying systems in their dual formulation. In this dual setting, the problem takes the form of a semi-infinite program which is readily solvable in online scenarios for constructing maneuver plans. This work augments the approach with the inclusion of magnitude constraints on the input over time windows of interest, which is shown to preserve the impulsive nature of the optimal solution and enable efficient solution procedures via semi-infinite programming. The resulting
algorithm is demonstrated on the highly relevant problem of relative motion control of spacecraft in Low Earth Orbit (LEO).
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