Systems and Control · Cross-listed from math.OC
A tensor phase theory with applications in multilinear control
Chengdong Liu, Yimin Wei and Guofeng Zhang
The purpose of this paper is to initiate a phase theory for tensors under the Einstein product, and explore its applications in multilinear control systems. Firstly, the sectorial tensor decomposition for sectorial tensors is derived, which allows us to define phases for sectorial tensors. A numerical procedure for computing phases of a sectorial tensor is also proposed. Secondly, the maximin and minimax expressions for tensor phases are given, which are used to quantify how close the phases of a sectorial tensor are to those of its compressions. Thirdly, the compound spectrum, compound numerical ranges and compound angular numerical ranges of two sectorial tensors $A,B$ are defined and characterized in terms of the compound numerical ranges and compound angular numerical ranges of the sectorial tensors $A,B$. Fourthly, it is shown that the angles of
eigenvalues of the product of two sectorial tensors are upper bounded by the sum of their individual phases. Finally, based on the tensor phase theory developed above, a tensor version of the small phase
theorem is presented, which can be regarded as a natural generalization of the
matrix case, recently proposed in Ref. [10]. The results offer powerful new tools for the stability and robustness analysis of multilinear feedback control systems.
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Signal Processing · Cross-listed from q-bio.NC
NeuroSketch: A Practical Design Recipe for Neural Decoding
Gaorui Zhang, Zhizhang Yuan, Jialan Yang et al. (+4 more)
Neural decoding is fundamental to
brain-computer interfaces, with growing applications in healthcare. Previous research has focused on leveraging signal processing and deep learning methods to enhance neural decoding performance. However, systematic guidance on architectural design for neural decoding remains limited. In this study, we develop NeuroSketch, a practical design recipe for neural decoding, through a basic architecture study followed by macro- and micro-level optimization. Comparing nine basic architectures, we find that CNN-2D outperforms other architectures in neural decoding tasks and explore its effectiveness from temporal and spatial perspectives. Building on this backbone, we combine gradual feature-map expansion and early downsampling at the macro level with grouped convolutions at the micro level. These choices form the recipe, which we instantiate as NeuroSketch-Base (1.4M parameters) and NeuroSketch-Large (4.2M parameters). The recipe is developed and evaluated through nearly 5,000 experiments across eight tasks spanning visual, auditory, and
speech modalities and
EEG, SEEG, and ECoG signals. Against ten baselines, the two variants collectively achieve the best accuracy on each task. Our code is available at https://github.com/
Galaxy-Dawn/NeuroSketch.
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All authors Gaorui Zhang, Zhizhang Yuan, Jialan Yang, Junru Chen, Fanqi Shen, Li Meng, Yang Yang
Signal Processing
Analytical and DNN-Aided Performance Evaluation of IRS-Assisted THz Communication Systems
Soumendu Das, Nagendra Kumar and Dharmendra Dixit
This paper investigates the performance of an intelligent reflecting surface (IRS)-assisted terahertz (THz) communication system, where the IRS facilitates connectivity between the source and destination nodes in the absence of a direct transmission path. The source-IRS and IRS-destination links are subject to various challenges, including atmospheric attenuation, asymmetric $\alpha$-$\mu$ distributed small-scale fading, and beam misalignment-induced pointing errors. The IRS link is characterized using the Laguerre series expansion (LSE) approximation, while both the source-IRS and IRS-destination channels are modeled as independent and identically distributed (i.i.d.) $\alpha$-$\mu$ fading channels. Furthermore, closed-form analytical expressions are derived for the outage probability (OP), average channel capacity (ACC), and average symbol error rate (ASER) for rectangular QAM (RQAM) and hexagonal QAM (HQAM) schemes over the end-to-end (e2e) link. The impact of random co-phasing and phase quantization errors are also examined. In addition to the theoretical analysis, deep neural network-based frameworks are developed to predict key performance metrics, facilitating fast and accurate system evaluation without computationally intensive analytical computations. Moreover, the asymptotic analysis in the high-signal-to-noise ratio (SNR) regime yields closed-form expressions for coding gain and diversity order, providing further insights into performance trends. Finally, Monte Carlo simulations validate the theoretical formulations and present a comprehensive assessment of system behavior under practical conditions.
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Systems and Control
Personalized Building Climate Control with Contextual Preferential Bayesian Optimization
Wenbin Wang, Jicheng Shi and Colin N. Jones
Efficient tuning of building
climate controllers to optimize occupant utility is essential for ensuring overall comfort and satisfaction. However, this is a challenging task since the latent utility are difficult to measure directly. Time-varying contextual factors, such as outdoor temperature, further complicate the problem. To address these challenges, we propose a contextual preferential
Bayesian optimization
algorithm that leverages binary preference feedback together with contextual information to enable efficient real-time controller tuning. We validate the approach by tuning an economic MPC controller on BOPTEST, a high-fidelity building simulation platform. Over a two-month simulation period, our method outperforms the baseline controller and achieves an improvement of up to 23% in utility. Moreover, for different occupant types, we demonstrate that the
algorithm automatically adapts to individual preferences, enabling personalized controller tuning.
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Systems and Control
Power Control of Multi-Layer Repeater Networks (POLARNet)
Johan Siwerson and Johan Thunberg
In this letter we introduce POLARNet – power control of multi-layer repeater networks – for local optimization of SNR given different repeater power constraints. We assume relays or repeaters in groups or layers spatially separated. Under ideal circumstances SISO narrow-band communication and TDD, the system may be viewed as a dual to a deep neural network, where activations, corresponding to repeater amplifications, are optimized and weight
matrices, corresponding to channel
matrices, are static. Repeater amplifications are locally optimized layer-by-layer in a forward-backward manner over compact sets. The method is applicable for a wide range of constraints on within-layer power/energy utilization, is furthermore gradient-free, step-size-free, and has proven monotonicity in the objective. Numerical simulations show significant improvement compared to upper bounds on the expected SNR. In addition, power distribution over multiple repeaters is shown to be superior to optimal selection of single repeaters in the layers.
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Signal Processing
Joint Channel Estimation and Localization in Pinching-Antenna OFDM Systems: The Blessing of Multipath
Min Liu, Yue Xiao, Shuaixin Yang et al. (+3 more)
Pinching-
antenna systems (PASS) have recently attracted considerable
attention owing to their capability of flexibly reconfiguring large-scale wireless channels. Motivated by this potential, we investigate the issue of joint localization and channel estimation for the uplink PASS in the presence of multipath dispersion. To this end, a comprehensive multi-user orthogonal frequency division multiplexing (OFDM) uplink PASS model is first established, where the use of a cyclic prefix (CP) enables the multipath-induced time-domain dispersion to be transformed into a set of superimposed sinusoids in the frequency domain. Building upon this model, we propose a hybrid inference framework capable of accurately estimating both channel parameters and user locations. Specifically, expectation propagation is first employed to mitigate multi-user interference, while the path delays are then extracted from noisy channel state information using an orthogonal matching pursuit (OMP) based approach, or a hybrid belief propagation-variational inference (BP-VI)
algorithm. Then the estimated delays are subsequently refined through the embedded
geometric information via an iterative localization procedure, wherein the estimated channel
matrices are recursively fed back to EP. Furthermore, the Cramer-Rao lower bound (CRLB) is derived to characterize the fundamental estimation limits. Finally, simulation results validate that our proposed framework closely approaches the CRLB, with performance comparable to cooperative multi-base station localization, with significantly fewer RF chains and reduced hardware complexity.
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All authors Min Liu, Yue Xiao, Shuaixin Yang, Gang Wu, Xianfu Lei, Wei Xiang
Image and Video Processing
QSMnet-INR: Single-Orientation Quantitative Susceptibility Mapping via Implicit Neural Representation in k-Space
Xuan Cai, Ruo-Mi Guo, Xiao-Wen Luo et al. (+6 more)
Quantitative Susceptibility Mapping (QSM) quantifies tissue
magnetic susceptibility from
magnetic-resonance phase data and plays a crucial role in
brain microstructure imaging, iron-deposition assessment, and neurological-disease research. However, single-orientation QSM inversion remains highly ill-posed because the dipole kernel exhibits a cone-null region in the
Fourier domain, leading to streaking artifacts and structural loss. To overcome this limitation, we propose QSMnet-INR, a deep, physics-informed framework that integrates an Implicit Neural Representation (INR) into the k-space domain. The INR module continuously models multi-directional dipole responses and explicitly completes the cone-null region, while a frequency-domain residual-weighted Dipole Loss enforces physical consistency. The overall network combines a 3D U-Net-based QSMnet backbone with the INR module through alternating optimization for end-to-end joint training. Experiments on the 2016 QSM Reconstruction Challenge, a multi-orientation GRE
dataset, and both in-house and public single-orientation clinical data demonstrate that QSMnet-INR consistently outperforms conventional and recent deep-learning approaches across multiple quantitative metrics. The proposed framework shows notable advantages in structural recovery within cone-null regions and in artifact suppression. Ablation studies further confirm the complementary contributions of the INR module and Dipole Loss to detail preservation and physical stability. Overall, QSMnet-INR effectively alleviates the ill-posedness of single-orientation QSM without requiring multi-orientation acquisition, achieving high accuracy, robustness, and strong cross-scenario generalization-highlighting its potential for clinical translation.
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All authors Xuan Cai, Ruo-Mi Guo, Xiao-Wen Luo, Jing Zhao, Silun Wang, Tao Tan, Yue Liu, Hongbin Han, Mengting Liu
Systems and Control
A Simulation Framework with Guaranteed Error Bounds for Safety and Fuel-Efficiency Analysis of Vehicle Platoons
Yuhao Chen and Ahmet Cetinkaya
Vehicle platooning is an important technology in modern transportation systems, offering significant improvements in highway traffic efficiency and fuel economy. Achieving coordinated behavior among vehicles in a platoon depends on wireless communication. However, packet losses in wireless communication can create critical safety issues when they occur together with sudden braking. In this paper, we propose a rigorous simulation-based method for studying such safety issues by analyzing the minimum inter-vehicle distance over time across control parameters that guarantee string stability. In particular, our method computes the exact distance at simulation time instants and guarantees that the change in distance between consecutive simulation time instants remains bounded. Therefore, the distances obtained at simulation times are representative of the continuous-time behavior, and the distances between those times can be accurately approximated. Our derivation relies on a lifted state representation and differential inequalities. For the proposed simulation method, we provide two approaches for selecting simulation times to ensure that the error in distance approximation remains within a given bound. We then extend our method to fuel-efficiency analysis, with guaranteed error bounds for calculating the average fuel savings of vehicles. Through an example involving a highway scenario with a merging lane, we demonstrate that among string-stable control parameter settings for a vehicle platoon, some perform better in terms of safety under simultaneous packet losses and sudden braking. We also identify control parameters that result in tradeoffs between safety and average fuel savings in a vehicle platoon.
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Image and Video Processing · Cross-listed from cs.LG
Rates and architectures for learning geometrically non-trivial operators
T. Mitchell Roddenberry, Leo Tzou, Ivan Dokmanić et al. (+2 more)
Deep learning methods have proven capable of recovering operators between high-dimensional spaces, such as solution maps of PDEs and similar objects in mathematical physics, from very few training samples. This phenomenon of data-efficiency has been proven for certain classes of
elliptic operators with simple geometry, i.e., operators that do not change the domain of the function or propagate singularities. However, scientific machine learning is commonly used for problems that do involve the propagation of singularities in a priori unknown ways, such as waves, advection, and fluid dynamics. In light of this, we expand the learning theory to include double fibration transforms–
geometric integral operators that include generalized Radon and geodesic ray transforms. We prove that this class of operators does not suffer from the curse of dimensionality: the error decays superalgebraically, that is, faster than any fixed power of the reciprocal of the number of training samples. Furthermore, we investigate architectures that explicitly encode the geometry of these transforms, demonstrating that an architecture reminiscent of cross-
attention based on levelset methods yields a parameterization that is universal, stable, and learns double fibration transforms from very few training examples. Our results contribute to a rapidly-growing line of theoretical work on learning operators for scientific machine learning.
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All authors T. Mitchell Roddenberry, Leo Tzou, Ivan Dokmanić, Maarten V. de Hoop, Richard G. Baraniuk
Systems and Control
Intelligent Resilience Testing for Decision-Making Agents with Dual-Mode Surrogate Adaptation
Jingxuan Yang, Weichao Xu, Yuchen Shi et al. (+3 more)
Testing and evaluating decision-making
agents remains challenging due to unknown system architectures, limited access to internal states, and the vastness of high-dimensional scenario spaces. Existing testing approaches often rely on surrogate models of decision-making
agents to generate large-scale scenario libraries; however, discrepancies between surrogate models and real decision-making
agents significantly limit their generalizability and practical applicability. To address this challenge, this paper proposes intelligent resilience testing (IRTest), a unified online adaptive testing framework designed to rapidly adjust to diverse decision-making
agents. IRTest initializes with an offline-trained surrogate prediction model and progressively reduces surrogate-to-real gap during testing through two complementary adaptation mechanisms: (i) online neural fine-tuning in data-rich regimes, and (ii) lightweight importance-sampling-based weighting correction in data-limited regimes. A
Bayesian optimization strategy, equipped with bias-corrected acquisition functions, guides scenario generation to balance exploration and exploitation in complex testing spaces. Extensive experiments across varying levels of task complexity and system heterogeneity demonstrate that IRTest consistently improves failure-discovery efficiency, testing robustness, and cross-system generalizability. These results highlight the potential of IRTest as a practical solution for scalable, adaptive, and resilient testing of decision-making
agents.
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All authors Jingxuan Yang, Weichao Xu, Yuchen Shi, Yi Zhang, Shuo Feng, Huaxin Pei
Image and Video Processing
NOC4SC: A Bandwidth-Efficient Multi-User Semantic Communication Framework for Interference-Resilient Transmission
Yunhao Wang, Shuai Ma, Pengfei He et al. (+3 more)
With the explosive growth of connected devices and emerging applications, current wireless networks are encountering unprecedented demands for massive user access, where the inter-user interference has become a critical challenge to maintaining high quality of service (QoS) in multi-user communication systems. To tackle this issue, we propose a bandwidth-efficient semantic communication paradigm termed Non-Orthogonal Codewords for Semantic Communication (NOC4SC), which enables simultaneous same-frequency transmission without spectrum spreading. By leveraging the Swin
Transformer, the proposed NOC4SC framework enables each user to independently extract semantic features through a unified encoder-decoder architecture with shared network parameters across all users, which ensures that the user's data remains protected from unauthorized decoding. Furthermore, we introduce an adaptive NOC and SNR Modulation (NSM) block, which employs deep learning to dynamically regulate SNR and generate approximately orthogonal semantic features within distinct feature subspaces, thereby effectively mitigating inter-user interference. Extensive experiments demonstrate the proposed NOC4SC achieves comparable performance to the DeepJSCC-PNOMA and outperforms other multi-user SemCom baseline methods.
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All authors Yunhao Wang, Shuai Ma, Pengfei He, Dahua Gao, Guangming Shi, Xiang Cheng
Signal Processing · Cross-listed from physics.optics
133-Tbps 1040-km (13$\times$80 km) Lumped-Amplified Transmission Over 22 THz in S-to-U-Band Using Hybrid Multiband Repeater with PPLN-Based Optical Parametric Amplifiers and EDFAs
Shimpei Shimizu, Takayuki Kobayashi, Masashi Abe et al. (+14 more)
We demonstrated 22.05-THz four-band long-haul transmission with a S-to-U-band lumped repeater consisting of PPLN-based optical parametric amplifiers and EDFAs over an 80-km-span SMF link. The achieved net bitrate was 133.06 Tbps at 1040 km with the 25.5-dBm fibre launch power designed by accounting for ISRS.
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All authors Shimpei Shimizu, Takayuki Kobayashi, Masashi Abe, Takushi Kazama, Akira Kawai, Kosuke Kimura, Masanori Nakamura, Fukutaro Hamaoka, Koji Enbutsu, Takahiro Kashiwazaki, Munehiko Nagatani, Hitoshi Wakita, Yuta Shiratori, Hiroshi Yamazaki, Hiroyuki Takahashi, Takeshi Umeki, Yutaka Miyamoto
Signal Processing
389.3-Tb/s 1017-km C-band Transmission over Field-Installed 12-Coupled-Core Fiber Cable with >12-Tb/s Spatial MIMO Channels
Akira Kawai, Kohki Shibahara, Megumi Hoshi et al. (+16 more)
We demonstrate 4.65-THz WDM/SDM transmission of 140-Gbaud PS-QAM signals over field-installed 12-coupled-core fiber cable with standard cladding diameter, achieving a record 0.455 Pb/s coupled-core capacity in a field environment. We also demonstrate 0.389 Pb/s over-1000-km transmission of spatial
MIMO channels with >12 Tb/s/wavelength net bitrate.
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All authors Akira Kawai, Kohki Shibahara, Megumi Hoshi, Masanori Nakamura, Takayuki Kobayashi, Ryota Imada, Takayoshi Mori, Taiji Sakamoto, Yusuke Yamada, Kazuhide Nakajima, Munehiko Nagatani, Hitoshi Wakita, Yuta Shiratori, Hiroshi Yamazaki, Hiroyuki Takahashi, Soichi Endo, Takemi Hasegawa, Ryo Nagase, Yutaka Miyamoto
Systems and Control
Analysis of Frequency and Voltage Strength in Power Electronics-Dominated Power Systems Based on Eigen-subsystems
Huisheng Gao, Linbin Huang, Huanhai Xin et al. (+2 more)
The large-scale integration of inverter-based resources (IBRs) has deteriorated the frequency/voltage (F/V) responses of power systems, leading to a higher risk of instability. Consequently, evaluating the F/V strength has become an important task in power electronics (PE)-dominated power systems. Existing methods typically examine F/V strength separately, employing fundamentally different metrics, such as inertia (focusing on device dynamics) and short-
circuit ratio (SCR, addressing network characteristics). These fragmented approaches have resulted in a lack of comprehensive understanding of the overall system strength, potentially overlooking critical aspects. To address this problem, this paper proposes a unified framework for analyzing F/V strength. First, a unified modeling of F/V regulations is introduced. Then, based on modal decoupling, the power systems are decomposed into several eigen-subsystems, where the F/V responses are both decomposed into common-mode (CM) and differential-mode (DM) components, namely, CM-F, DM-F, CM-V, and DM-V. The CM-F and CM-V represent the collective response of all devices to external active or reactive power disturbances, independent of the power network characteristics. In contrast, the DM-F and DM-V capture the redistribution of disturbance power within the system, which is strongly influenced by the network
topology and the locations of devices. Notably, traditional strength analysis generally ignores the CM-V (global voltage response), which, as discovered in this paper, may also become unstable in PE-dominated power systems. Based on the proposed framework, new metrics are proposed to evaluate the strength of each modal component. Finally, the effectiveness of the proposed approach is validated through simulations.
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All authors Huisheng Gao, Linbin Huang, Huanhai Xin, Zhiyi Li, Ping Ju
Systems and Control
Electric Arc Furnaces Scheduling under Electricity Price Volatility with Reinforcement Learning
Ruonan Pi, Zhiyuan Fan and Bolun Xu
This paper proposes a reinforcement learning-based framework for optimizing the operation of electric arc furnaces (EAFs) under volatile electricity prices. We formulate the deterministic version of the EAF scheduling problem into a mixed-integer linear programming (MILP) formulation, and then develop a Q-learning
algorithm to perform real-time control of multiple EAF units under real-time price
volatility and shared feeding capacity constraints. We design a custom reward function for the Q-learning
algorithm to smooth the start-up penalties of the EAFs. Using real data from EAF designs and electricity prices in New York State, we
benchmark our
algorithm against a baseline rule-based controller and a MILP
benchmark, assuming perfect price forecasts. The results show that our reinforcement learning
algorithm achieves around 90% of the profit compared to the perfect MILP
benchmark in various single-unit and multi-unit cases under a non-anticipatory control setting.
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Image and Video Processing
SITP: A High-Reliability Semantic Information Transport Protocol Without Retransmission for Semantic Communication
Yunhao Wang, Shuai Ma, Youlong Wu et al. (+4 more)
With the
evolution of 6G networks, modern communication systems are facing unprecedented demands for high reliability and low latency. However, conventional transport protocols are designed for bit-level reliability, failing to meet the semantic robustness requirements. To address this limitation, this paper proposes a novel Semantic Information Transport Protocol (SITP), which achieves TCP-level reliability and UDP level latency by verifying only packet headers while retaining potentially corrupted payloads for semantic decoding. Building upon SITP, a cross-layer analytical model is established to quantify packet-loss probability across the physical, data-link, network, transport, and application layers. The model provides a unified probabilistic formulation linking signal noise rate (SNR) and packet-loss rate, offering theoretical foundation into end-to-end semantic transmission. Furthermore, a cross-image feature interleaving mechanism is developed to mitigate consecutive burst losses by redistributing semantic features across multiple correlated images, thereby enhancing robustness in burst-fade channels. Extensive experiments show that SITP offers lower latency than TCP with comparable reliability at low SNRs, while matching UDP-level latency and delivering superior reconstruction quality. In addition, the proposed cross-image semantic interleaving mechanism further demonstrates its effectiveness in mitigating degradation caused by bursty packet losses.
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All authors Yunhao Wang, Shuai Ma, Youlong Wu, Guangming Shi, Xiang Cheng, Yuxuan Liu, Pengfei He
Systems and Control · Cross-listed from cs.LG
Goal inference with Rao-Blackwellized Particle Filters
Yixuan Wang, Dan P. Guralnik and Warren E. Dixon
Inferring the eventual goal of a mobile
agent from noisy observations of its trajectory is a fundamental estimation problem. We initiate the study of such intent inference using a variant of a Rao-Blackwellized Particle Filter (RBPF), subject to the assumption that the
agent's intent manifests through closed-loop behavior with a state-of-the-art provable practical stability property. Leveraging the assumed closed-form
agent dynamics, the RBPF analytically marginalizes the linear-Gaussian substructure and updates particle weights only, improving sample efficiency over a standard particle filter. Two difference
estimators are introduced: a Gaussian mixture model using the RBPF weights and a reduced version confining the mixture to the effective sample. We quantify how well the adversary can recover the
agent's intent using information-theoretic leakage metrics and provide computable lower bounds on the Kullback-Leibler (KL) divergence between the true intent distribution and RBPF estimates via Gaussian-mixture KL bounds. We also provide a bound on the difference in performance between the two
estimators, highlighting the fact that the reduced
estimator performs almost as well as the complete one. Experiments illustrate fast and accurate intent recovery for compliant
agents, motivating future work on designing intent-obfuscating controllers.
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Audio and Speech Processing
Human perception of audio deepfakes: the role of language and speaking style
Eugenia San Segundo, Aurora López-Jareño, Xin Wang et al. (+1 more)
Audio deepfakes have reached a level of realism that makes it increasingly difficult to distinguish between human and artificial voices, which poses risks such as identity theft or spread of disinformation. Despite these concerns, research on humans' ability to identify deepfakes is limited, with most studies focusing on English and very few exploring the reasons behind listeners' perceptual decisions. This study addresses this gap through a perceptual experiment in which 54 listeners (28 native Spanish speakers and 26 native Japanese speakers) classified voices as natural or synthetic, and justified their choices. The experiment included 80 stimuli (50% artificial), organized according to three variables:
language (Spanish/Japanese),
speech style (audiobooks/interviews), and familiarity with the voice (familiar/unfamiliar). The goal was to examine how these variables influence detection and to analyze qualitatively the
reasoning behind listeners' perceptual decisions. Results indicate an average accuracy of 59.11%, with higher performance on authentic samples. Judgments of vocal naturalness rely on a combination of linguistic and non-linguistic cues. Comparing Japanese and Spanish listeners, our qualitative analysis further reveals both shared cues and notable cross-linguistic differences in how listeners conceptualize the "humanness" of
speech. Overall, participants relied primarily on suprasegmental and higher-level or extralinguistic characteristics - such as intonation, rhythm, fluency, pauses, speed, breathing, and laughter - over segmental features. These findings underscore the complexity of human perceptual strategies in distinguishing natural from artificial
speech and align partly with prior research emphasizing the importance of prosody and phenomena typical of spontaneous
speech, such as disfluencies.
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All authors Eugenia San Segundo, Aurora López-Jareño, Xin Wang, Junichi Yamagishi
Systems and Control
MPC for momentum counter-balanced and zero-impulse contact with a free-spinning satellite
Theofania Karampela, Rishie Seshadri, Florian Dörfler et al. (+1 more)
In on-orbit robotics, a servicer satellite's ability to make contact with a free-spinning target satellite is essential to completing most on-orbit servicing (OOS) tasks. This manuscript develops a nonlinear model predictive control (MPC) framework that generates feasible controls for a servicer satellite to achieve zero-impulse contact with a free-spinning target satellite. The overall maneuver requires coordination between two separately actuated modules of the servicer satellite: (1) a moment generation module and (2) a manipulation module. We apply MPC to control both modules by explicitly modeling the cross-coupling dynamics between them. We demonstrate that the MPC controller can enforce actuation and state constraints that prior control approaches could not account for. We evaluate the performance of the MPC controller by simulating zero-impulse contact scenarios with a free-spinning target satellite via numerical Monte Carlo (MC) trials and comparing the simulation results with prior control approaches. Our simulation results validate the effectiveness of the MPC controller in maintaining
spin synchronization and zero-impulse contact under operation constraints, moving contact location, and observation and actuation noise.
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All authors Theofania Karampela, Rishie Seshadri, Florian Dörfler, Sarah H.Q. Li