This paper develops a direct data-driven inverse optimal control (3DIOC) algorithm for the linear time-invariant (LTI) system who conducts a linear quadratic (LQ) control, where the underlying objective function is learned directly from measured input-output trajectories without system identification. By introducing the Fundamental Lemma, we establish the input-output representation of the LTI system. We accordingly propose a model-free optimality necessary condition for the forward LQ problem to build a connection between the objective function and collected data, with which the inverse optimal control problem is solved. We further improve the algorithm so that it requires a less computation and data. Identifiability condition and perturbation analysis are provided. Simulations demonstrate the efficiency and performance of our algorithms.
{"title":"3DIOC: Direct Data-Driven Inverse Optimal Control for LTI Systems","authors":"Chendi Qu, Jianping He, Xiaoming Duan","doi":"arxiv-2409.10884","DOIUrl":"https://doi.org/arxiv-2409.10884","url":null,"abstract":"This paper develops a direct data-driven inverse optimal control (3DIOC)\u0000algorithm for the linear time-invariant (LTI) system who conducts a linear\u0000quadratic (LQ) control, where the underlying objective function is learned\u0000directly from measured input-output trajectories without system identification.\u0000By introducing the Fundamental Lemma, we establish the input-output\u0000representation of the LTI system. We accordingly propose a model-free\u0000optimality necessary condition for the forward LQ problem to build a connection\u0000between the objective function and collected data, with which the inverse\u0000optimal control problem is solved. We further improve the algorithm so that it\u0000requires a less computation and data. Identifiability condition and\u0000perturbation analysis are provided. Simulations demonstrate the efficiency and\u0000performance of our algorithms.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"64 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tracking controllers enable robotic systems to accurately follow planned reference trajectories. In particular, reinforcement learning (RL) has shown promise in the synthesis of controllers for systems with complex dynamics and modest online compute budgets. However, the poor sample efficiency of RL and the challenges of reward design make training slow and sometimes unstable, especially for high-dimensional systems. In this work, we leverage the inherent Lie group symmetries of robotic systems with a floating base to mitigate these challenges when learning tracking controllers. We model a general tracking problem as a Markov decision process (MDP) that captures the evolution of both the physical and reference states. Next, we prove that symmetry in the underlying dynamics and running costs leads to an MDP homomorphism, a mapping that allows a policy trained on a lower-dimensional "quotient" MDP to be lifted to an optimal tracking controller for the original system. We compare this symmetry-informed approach to an unstructured baseline, using Proximal Policy Optimization (PPO) to learn tracking controllers for three systems: the Particle (a forced point mass), the Astrobee (a fullyactuated space robot), and the Quadrotor (an underactuated system). Results show that a symmetry-aware approach both accelerates training and reduces tracking error after the same number of training steps.
{"title":"Leveraging Symmetry to Accelerate Learning of Trajectory Tracking Controllers for Free-Flying Robotic Systems","authors":"Jake Welde, Nishanth Rao, Pratik Kunapuli, Dinesh Jayaraman, Vijay Kumar","doi":"arxiv-2409.11238","DOIUrl":"https://doi.org/arxiv-2409.11238","url":null,"abstract":"Tracking controllers enable robotic systems to accurately follow planned\u0000reference trajectories. In particular, reinforcement learning (RL) has shown\u0000promise in the synthesis of controllers for systems with complex dynamics and\u0000modest online compute budgets. However, the poor sample efficiency of RL and\u0000the challenges of reward design make training slow and sometimes unstable,\u0000especially for high-dimensional systems. In this work, we leverage the inherent\u0000Lie group symmetries of robotic systems with a floating base to mitigate these\u0000challenges when learning tracking controllers. We model a general tracking\u0000problem as a Markov decision process (MDP) that captures the evolution of both\u0000the physical and reference states. Next, we prove that symmetry in the\u0000underlying dynamics and running costs leads to an MDP homomorphism, a mapping\u0000that allows a policy trained on a lower-dimensional \"quotient\" MDP to be lifted\u0000to an optimal tracking controller for the original system. We compare this\u0000symmetry-informed approach to an unstructured baseline, using Proximal Policy\u0000Optimization (PPO) to learn tracking controllers for three systems: the\u0000Particle (a forced point mass), the Astrobee (a fullyactuated space robot), and\u0000the Quadrotor (an underactuated system). Results show that a symmetry-aware\u0000approach both accelerates training and reduces tracking error after the same\u0000number of training steps.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Targeted interventions in games present a challenging problem due to the asymmetric information available to the regulator and the agents. This note addresses the problem of steering the actions of self-interested agents in quadratic network games towards a target action profile. A common starting point in the literature assumes prior knowledge of utility functions and/or network parameters. The goal of the results presented here is to remove this assumption and address scenarios where such a priori knowledge is unavailable. To this end, we design a data-driven dynamic intervention mechanism that relies solely on historical observations of agent actions and interventions. Additionally, we modify this mechanism to limit the amount of interventions, thereby considering budget constraints. Analytical convergence guarantees are provided for both mechanisms, and a numerical case study further demonstrates their effectiveness.
{"title":"Data-driven Dynamic Intervention Design in Network Games","authors":"Xiupeng Chen, Nima Monshizadeh","doi":"arxiv-2409.11069","DOIUrl":"https://doi.org/arxiv-2409.11069","url":null,"abstract":"Targeted interventions in games present a challenging problem due to the\u0000asymmetric information available to the regulator and the agents. This note\u0000addresses the problem of steering the actions of self-interested agents in\u0000quadratic network games towards a target action profile. A common starting\u0000point in the literature assumes prior knowledge of utility functions and/or\u0000network parameters. The goal of the results presented here is to remove this\u0000assumption and address scenarios where such a priori knowledge is unavailable.\u0000To this end, we design a data-driven dynamic intervention mechanism that relies\u0000solely on historical observations of agent actions and interventions.\u0000Additionally, we modify this mechanism to limit the amount of interventions,\u0000thereby considering budget constraints. Analytical convergence guarantees are\u0000provided for both mechanisms, and a numerical case study further demonstrates\u0000their effectiveness.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Decision-changing imitation is a prevalent phenomenon in financial markets, where investors imitate others' decision-changing rates when making their own investment decisions. In this work, we study the optimal investment problem under the influence of decision-changing imitation involving one leading expert and one retail investor whose decisions are unilaterally influenced by the leading expert. In the objective functional of the optimal investment problem, we propose the integral disparity to quantify the distance between the two investors' decision-changing rates. Due to the underdetermination of the optimal investment problem, we first derive its general solution using the variational method and find the retail investor's optimal decisions under two special cases of the boundary conditions. We theoretically analyze the asymptotic properties of the optimal decision as the influence of decision-changing imitation approaches infinity, and investigate the impact of decision-changing imitation on the optimal decision. Our analysis is validated using numerical experiments on real stock data. This study is essential to comprehend decision-changing imitation and devise effective mechanisms to guide investors' decisions.
{"title":"Optimal Investment under the Influence of Decision-changing Imitation","authors":"Huisheng Wang, H. Vicky Zhao","doi":"arxiv-2409.10933","DOIUrl":"https://doi.org/arxiv-2409.10933","url":null,"abstract":"Decision-changing imitation is a prevalent phenomenon in financial markets,\u0000where investors imitate others' decision-changing rates when making their own\u0000investment decisions. In this work, we study the optimal investment problem\u0000under the influence of decision-changing imitation involving one leading expert\u0000and one retail investor whose decisions are unilaterally influenced by the\u0000leading expert. In the objective functional of the optimal investment problem,\u0000we propose the integral disparity to quantify the distance between the two\u0000investors' decision-changing rates. Due to the underdetermination of the\u0000optimal investment problem, we first derive its general solution using the\u0000variational method and find the retail investor's optimal decisions under two\u0000special cases of the boundary conditions. We theoretically analyze the\u0000asymptotic properties of the optimal decision as the influence of\u0000decision-changing imitation approaches infinity, and investigate the impact of\u0000decision-changing imitation on the optimal decision. Our analysis is validated\u0000using numerical experiments on real stock data. This study is essential to\u0000comprehend decision-changing imitation and devise effective mechanisms to guide\u0000investors' decisions.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chih-Yuan Chiu, Jingqi Li, Maulik Bhatt, Negar Mehr
Dynamic games offer a versatile framework for modeling the evolving interactions of strategic agents, whose steady-state behavior can be captured by the Nash equilibria of the games. Nash equilibria are often computed in feedback, with policies depending on the state at each time, or in open-loop, with policies depending only on the initial state. Empirically, open-loop Nash equilibria (OLNE) are often more efficient to compute, while feedback Nash equilibria (FBNE) encode more complex interactions. However, it remains unclear exactly which dynamic games yield FBNE and OLNE that differ significantly and which do not. To address this problem, we present a principled comparison study of OLNE and FBNE in linear quadratic (LQ) dynamic games. Specifically, we prove that the OLNE strategies of an LQ dynamic game can be synthesized by solving the coupled Riccati equations of an auxiliary LQ game with perturbed costs. The construction of the auxiliary game allows us to establish conditions under which OLNE and FBNE coincide and derive an upper bound on the deviation between FBNE and OLNE of an LQ game.
{"title":"To What Extent do Open-loop and Feedback Nash Equilibria Diverge in General-Sum Linear Quadratic Dynamic Games?","authors":"Chih-Yuan Chiu, Jingqi Li, Maulik Bhatt, Negar Mehr","doi":"arxiv-2409.11257","DOIUrl":"https://doi.org/arxiv-2409.11257","url":null,"abstract":"Dynamic games offer a versatile framework for modeling the evolving\u0000interactions of strategic agents, whose steady-state behavior can be captured\u0000by the Nash equilibria of the games. Nash equilibria are often computed in\u0000feedback, with policies depending on the state at each time, or in open-loop,\u0000with policies depending only on the initial state. Empirically, open-loop Nash\u0000equilibria (OLNE) are often more efficient to compute, while feedback Nash\u0000equilibria (FBNE) encode more complex interactions. However, it remains unclear\u0000exactly which dynamic games yield FBNE and OLNE that differ significantly and\u0000which do not. To address this problem, we present a principled comparison study\u0000of OLNE and FBNE in linear quadratic (LQ) dynamic games. Specifically, we prove\u0000that the OLNE strategies of an LQ dynamic game can be synthesized by solving\u0000the coupled Riccati equations of an auxiliary LQ game with perturbed costs. The\u0000construction of the auxiliary game allows us to establish conditions under\u0000which OLNE and FBNE coincide and derive an upper bound on the deviation between\u0000FBNE and OLNE of an LQ game.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sensory substitution is an effective approach for displaying stable haptic feedback to a teleoperator under time delay. The finger is highly articulated, and can sense movement and force in many directions, making it a promising location for sensory substitution based on kinesthetic feedback. However, existing finger kinesthetic devices either provide only one-degree-of-freedom feedback, are bulky, or have low force output. Soft pneumatic actuators have high power density, making them suitable for realizing high force kinesthetic feedback in a compact form factor. We present a soft pneumatic handheld kinesthetic feedback device for the index finger that is controlled using a constant curvature kinematic model. changed{It has respective position and force ranges of +-3.18mm and +-1.00N laterally, and +-4.89mm and +-6.01N vertically, indicating its high power density and compactness. The average open-loop radial position and force accuracy of the kinematic model are 0.72mm and 0.34N.} Its 3Hz bandwidth makes it suitable for moderate speed haptic interactions in soft environments. We demonstrate the three-dimensional kinesthetic force feedback capability of our device for sensory substitution at the index figure in a virtual telemanipulation scenario.
{"title":"Three Degree-of-Freedom Soft Continuum Kinesthetic Haptic Display for Telemanipulation Via Sensory Substitution at the Finger","authors":"Jiaji Su, Kaiwen Zuo, Zonghe Chua","doi":"arxiv-2409.11606","DOIUrl":"https://doi.org/arxiv-2409.11606","url":null,"abstract":"Sensory substitution is an effective approach for displaying stable haptic\u0000feedback to a teleoperator under time delay. The finger is highly articulated,\u0000and can sense movement and force in many directions, making it a promising\u0000location for sensory substitution based on kinesthetic feedback. However,\u0000existing finger kinesthetic devices either provide only one-degree-of-freedom\u0000feedback, are bulky, or have low force output. Soft pneumatic actuators have\u0000high power density, making them suitable for realizing high force kinesthetic\u0000feedback in a compact form factor. We present a soft pneumatic handheld\u0000kinesthetic feedback device for the index finger that is controlled using a\u0000constant curvature kinematic model. changed{It has respective position and\u0000force ranges of +-3.18mm and +-1.00N laterally, and +-4.89mm and +-6.01N\u0000vertically, indicating its high power density and compactness. The average\u0000open-loop radial position and force accuracy of the kinematic model are 0.72mm\u0000and 0.34N.} Its 3Hz bandwidth makes it suitable for moderate speed haptic\u0000interactions in soft environments. We demonstrate the three-dimensional\u0000kinesthetic force feedback capability of our device for sensory substitution at\u0000the index figure in a virtual telemanipulation scenario.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A pose estimation technique based on error-state extended Kalman that fuses angular rates, accelerations, and relative range measurements is presented in this paper. An unconstrained dynamic model with kinematic coupling for a thrust-capable satellite is considered for the state propagation, and a pragmatic measurement model of the rate gyroscope, accelerometer, and an ultra-wideband radio are leveraged for the measurement update. The error-state extended Kalman filter framework is formulated for pose estimation, and its performance has been analyzed via several simulation scenarios. An application of the pose estimator for proximity operations and scaffolding formation of CubeSat deputies relative to their mother-ship is outlined. Finally, the performance of the error-state extended Kalman filter is demonstrated using experimental analysis consisting of a 3-DOF thrust cable satellite mock-up, rate gyroscope, accelerometer, and ultra-wideband radar modules.
{"title":"Pose estimation of CubeSats via sensor fusion and Error-State Extended Kalman Filter","authors":"Deep Parikh, Manoranjan Majji","doi":"arxiv-2409.10815","DOIUrl":"https://doi.org/arxiv-2409.10815","url":null,"abstract":"A pose estimation technique based on error-state extended Kalman that fuses\u0000angular rates, accelerations, and relative range measurements is presented in\u0000this paper. An unconstrained dynamic model with kinematic coupling for a\u0000thrust-capable satellite is considered for the state propagation, and a\u0000pragmatic measurement model of the rate gyroscope, accelerometer, and an\u0000ultra-wideband radio are leveraged for the measurement update. The error-state\u0000extended Kalman filter framework is formulated for pose estimation, and its\u0000performance has been analyzed via several simulation scenarios. An application\u0000of the pose estimator for proximity operations and scaffolding formation of\u0000CubeSat deputies relative to their mother-ship is outlined. Finally, the\u0000performance of the error-state extended Kalman filter is demonstrated using\u0000experimental analysis consisting of a 3-DOF thrust cable satellite mock-up,\u0000rate gyroscope, accelerometer, and ultra-wideband radar modules.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Richie R. Suganda, Tony Tran, Miao Pan, Lei Fan, Qin Lin, Bin Hu
This paper addresses a distributed leader-follower formation control problem for a group of agents, each using a body-fixed camera with a limited field of view (FOV) for state estimation. The main challenge arises from the need to coordinate the agents' movements with their cameras' FOV to maintain visibility of the leader for accurate and reliable state estimation. To address this challenge, we propose a novel perception-aware distributed leader-follower safe control scheme that incorporates FOV limits as state constraints. A Control Barrier Function (CBF) based quadratic program is employed to ensure the forward invariance of a safety set defined by these constraints. Furthermore, new neural network based and double bounding boxes based estimators, combined with temporal filters, are developed to estimate system states directly from real-time image data, providing consistent performance across various environments. Comparison results in the Gazebo simulator demonstrate the effectiveness and robustness of the proposed framework in two distinct environments.
{"title":"Distributed Perception Aware Safe Leader Follower System via Control Barrier Methods","authors":"Richie R. Suganda, Tony Tran, Miao Pan, Lei Fan, Qin Lin, Bin Hu","doi":"arxiv-2409.11394","DOIUrl":"https://doi.org/arxiv-2409.11394","url":null,"abstract":"This paper addresses a distributed leader-follower formation control problem\u0000for a group of agents, each using a body-fixed camera with a limited field of\u0000view (FOV) for state estimation. The main challenge arises from the need to\u0000coordinate the agents' movements with their cameras' FOV to maintain visibility\u0000of the leader for accurate and reliable state estimation. To address this\u0000challenge, we propose a novel perception-aware distributed leader-follower safe\u0000control scheme that incorporates FOV limits as state constraints. A Control\u0000Barrier Function (CBF) based quadratic program is employed to ensure the\u0000forward invariance of a safety set defined by these constraints. Furthermore,\u0000new neural network based and double bounding boxes based estimators, combined\u0000with temporal filters, are developed to estimate system states directly from\u0000real-time image data, providing consistent performance across various\u0000environments. Comparison results in the Gazebo simulator demonstrate the\u0000effectiveness and robustness of the proposed framework in two distinct\u0000environments.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Super-Resolution Ultrasound (SRUS) imaging through localising and tracking microbubbles, also known as Ultrasound Localisation Microscopy (ULM), has demonstrated significant potential for reconstructing microvasculature and flows with sub-diffraction resolution in clinical diagnostics. However, imaging organs with large tissue movements, such as those caused by respiration, presents substantial challenges. Existing methods often require breath holding to maintain accumulation accuracy, which limits data acquisition time and ULM image saturation. To improve image quality in the presence of large tissue movements, this study introduces an approach integrating high-frame-rate ultrasound with online precise robotic probe control. Tested on a microvasculature phantom with translation motions up to 20 mm, twice the aperture size of the matrix array used, our method achieved real-time tracking of the moving phantom and imaging volume rate at 85 Hz, keeping majority of the target volume in the imaging field of view. ULM images of the moving cross channels in the phantom were successfully reconstructed in post-processing, demonstrating the feasibility of super-resolution imaging under large tissue motions. This represents a significant step towards ULM imaging of organs with large motion.
{"title":"Online 4D Ultrasound-Guided Robotic Tracking Enables 3D Ultrasound Localisation Microscopy with Large Tissue Displacements","authors":"Jipeng Yan, Shusei Kawara, Qingyuan Tan, Jingwen Zhu, Bingxue Wang, Matthieu Toulemonde, Honghai Liu, Ying Tan, Meng-Xing Tang","doi":"arxiv-2409.11391","DOIUrl":"https://doi.org/arxiv-2409.11391","url":null,"abstract":"Super-Resolution Ultrasound (SRUS) imaging through localising and tracking\u0000microbubbles, also known as Ultrasound Localisation Microscopy (ULM), has\u0000demonstrated significant potential for reconstructing microvasculature and\u0000flows with sub-diffraction resolution in clinical diagnostics. However, imaging\u0000organs with large tissue movements, such as those caused by respiration,\u0000presents substantial challenges. Existing methods often require breath holding\u0000to maintain accumulation accuracy, which limits data acquisition time and ULM\u0000image saturation. To improve image quality in the presence of large tissue\u0000movements, this study introduces an approach integrating high-frame-rate\u0000ultrasound with online precise robotic probe control. Tested on a\u0000microvasculature phantom with translation motions up to 20 mm, twice the\u0000aperture size of the matrix array used, our method achieved real-time tracking\u0000of the moving phantom and imaging volume rate at 85 Hz, keeping majority of the\u0000target volume in the imaging field of view. ULM images of the moving cross\u0000channels in the phantom were successfully reconstructed in post-processing,\u0000demonstrating the feasibility of super-resolution imaging under large tissue\u0000motions. This represents a significant step towards ULM imaging of organs with\u0000large motion.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper considers a finite sample perspective on the problem of identifying an LTI system from a finite set of possible systems using trajectory data. To this end, we use the maximum likelihood estimator to identify the true system and provide an upper bound for its sample complexity. Crucially, the derived bound does not rely on a potentially restrictive stability assumption. Additionally, we leverage tools from information theory to provide a lower bound to the sample complexity that holds independently of the used estimator. The derived sample complexity bounds are analyzed analytically and numerically.
{"title":"Sample Complexity Bounds for Linear System Identification from a Finite Set","authors":"Nicolas Chatzikiriakos, Andrea Iannelli","doi":"arxiv-2409.11141","DOIUrl":"https://doi.org/arxiv-2409.11141","url":null,"abstract":"This paper considers a finite sample perspective on the problem of\u0000identifying an LTI system from a finite set of possible systems using\u0000trajectory data. To this end, we use the maximum likelihood estimator to\u0000identify the true system and provide an upper bound for its sample complexity.\u0000Crucially, the derived bound does not rely on a potentially restrictive\u0000stability assumption. Additionally, we leverage tools from information theory\u0000to provide a lower bound to the sample complexity that holds independently of\u0000the used estimator. The derived sample complexity bounds are analyzed\u0000analytically and numerically.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"54 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}