This paper investigates payload grasping from a moving platform using a hook-equipped aerial manipulator. First, a computationally efficient trajectory optimization based on complementarity constraints is proposed to determine the optimal grasping time. To enable application in complex, dynamically changing environments, the future motion of the payload is predicted using physics simulator-based models. The success of payload grasping under model uncertainties and external disturbances is formally verified through a robustness analysis method based on integral quadratic constraints. The proposed algorithms are evaluated in a high-fidelity physical simulator, and in real flight experiments using a custom-designed aerial manipulator platform.
{"title":"Hook-Based Aerial Payload Grasping from a Moving Platform","authors":"Péter Antal, Tamás Péni, Roland Tóth","doi":"arxiv-2409.11788","DOIUrl":"https://doi.org/arxiv-2409.11788","url":null,"abstract":"This paper investigates payload grasping from a moving platform using a\u0000hook-equipped aerial manipulator. First, a computationally efficient trajectory\u0000optimization based on complementarity constraints is proposed to determine the\u0000optimal grasping time. To enable application in complex, dynamically changing\u0000environments, the future motion of the payload is predicted using physics\u0000simulator-based models. The success of payload grasping under model\u0000uncertainties and external disturbances is formally verified through a\u0000robustness analysis method based on integral quadratic constraints. The\u0000proposed algorithms are evaluated in a high-fidelity physical simulator, and in\u0000real flight experiments using a custom-designed aerial manipulator platform.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264197","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}
To advance theoretical solutions and address limitations in modeling complex servo-driven actuation systems experiencing high non-linearity and load disturbances, this paper aims to design a practical model-free generic robust control (GRC) framework for these mechanisms. This framework is intended to be applicable across all actuator systems encompassing electrical, hydraulic, or pneumatic servomechanisms, while also functioning within complex interactions among dynamic components and adhering to control input constraints. In this respect, the state-space model of actuator systems is decomposed into smaller subsystems that incorporate the first principle equation of actuator motion dynamics and interactive energy conversion equations. This decomposition operates under the assumption that the comprehensive model of the servo-driven actuator system and energy conversion, uncertainties, load disturbances, and their bounds are unknown. Then, the GRC employs subsystem-based adaptive control strategies for each state-variant subsystem separately. Despite control input constraints and the unknown interactive system model, the GRC-applied actuator mechanism ensures uniform exponential stability and robustness in tracking desired motions. It features straightforward implementation, experimentally evaluated by applying it to two industrial applications.
{"title":"Model-Free Generic Robust Control for Servo-Driven Actuation Mechanisms with Experimental Verification","authors":"Mehdi Heydari Shahna, Jouni Mattila","doi":"arxiv-2409.11828","DOIUrl":"https://doi.org/arxiv-2409.11828","url":null,"abstract":"To advance theoretical solutions and address limitations in modeling complex\u0000servo-driven actuation systems experiencing high non-linearity and load\u0000disturbances, this paper aims to design a practical model-free generic robust\u0000control (GRC) framework for these mechanisms. This framework is intended to be\u0000applicable across all actuator systems encompassing electrical, hydraulic, or\u0000pneumatic servomechanisms, while also functioning within complex interactions\u0000among dynamic components and adhering to control input constraints. In this\u0000respect, the state-space model of actuator systems is decomposed into smaller\u0000subsystems that incorporate the first principle equation of actuator motion\u0000dynamics and interactive energy conversion equations. This decomposition\u0000operates under the assumption that the comprehensive model of the servo-driven\u0000actuator system and energy conversion, uncertainties, load disturbances, and\u0000their bounds are unknown. Then, the GRC employs subsystem-based adaptive\u0000control strategies for each state-variant subsystem separately. Despite control\u0000input constraints and the unknown interactive system model, the GRC-applied\u0000actuator mechanism ensures uniform exponential stability and robustness in\u0000tracking desired motions. It features straightforward implementation,\u0000experimentally evaluated by applying it to two industrial applications.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264109","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}
The advent of 3D printing has transformed the pharmaceutical industry, enabling precision drug manufacturing with controlled release profiles, dosing, and structural complexity. Additive manufacturing (AM) addresses the growing demand for personalized medicine, overcoming limitations of traditional methods. This technology facilitates tailored dosage forms, complex geometries, and real-time quality control. Recent advancements in drop-on-demand printing, UV curable inks, material science, and regulatory frameworks are discussed. Despite opportunities for cost reduction, flexibility, and decentralized manufacturing, challenges persist in scalability, reproducibility, and regulatory adaptation. This review provides an in-depth analysis of the current state of AM in pharmaceutical manufacturing, exploring recent developments, challenges, and future directions for mainstream integration.
三维打印技术的出现改变了制药行业，实现了具有可控释放曲线、剂量和结构复杂性的精密药物制造。增材制造（AM）克服了传统方法的局限性，满足了日益增长的个性化医疗需求。这项技术有利于定制剂型、复杂几何形状和实时质量控制。尽管在降低成本、灵活性和分散制造方面存在机遇，但在可扩展性、可重复性和监管适应性方面仍然存在挑战。这篇综述深入分析了制药制造业中的 AM 现状，探讨了主流集成的最新发展、挑战和未来方向。
{"title":"Revolutionizing Pharmaceutical Manufacturing: Advances and Challenges of 3D Printing System and Control","authors":"Rahul Kumar, Vikram Singh, Priya Gupta","doi":"arxiv-2409.11712","DOIUrl":"https://doi.org/arxiv-2409.11712","url":null,"abstract":"The advent of 3D printing has transformed the pharmaceutical industry,\u0000enabling precision drug manufacturing with controlled release profiles, dosing,\u0000and structural complexity. Additive manufacturing (AM) addresses the growing\u0000demand for personalized medicine, overcoming limitations of traditional\u0000methods. This technology facilitates tailored dosage forms, complex geometries,\u0000and real-time quality control. Recent advancements in drop-on-demand printing,\u0000UV curable inks, material science, and regulatory frameworks are discussed.\u0000Despite opportunities for cost reduction, flexibility, and decentralized\u0000manufacturing, challenges persist in scalability, reproducibility, and\u0000regulatory adaptation. This review provides an in-depth analysis of the current\u0000state of AM in pharmaceutical manufacturing, exploring recent developments,\u0000challenges, and future directions for mainstream integration.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"77 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264249","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}
J. S. van Hulst, W. P. M. H. Heemels, D. J. Antunes
Reinforcement learning (RL) has seen significant research and application results but often requires large amounts of training data. This paper proposes two data-efficient off-policy RL methods that use parametrized Q-learning. In these methods, the Q-function is chosen to be linear in the parameters and quadratic in selected basis functions in the state and control deviations from a base policy. A cost penalizing the $ell_1$-norm of Bellman errors is minimized. We propose two methods: Linear Matrix Inequality Q-Learning (LMI-QL) and its iterative variant (LMI-QLi), which solve the resulting episodic optimization problem through convex optimization. LMI-QL relies on a convex relaxation that yields a semidefinite programming (SDP) problem with linear matrix inequalities (LMIs). LMI-QLi entails solving sequential iterations of an SDP problem. Both methods combine convex optimization with direct Q-function learning, significantly improving learning speed. A numerical case study demonstrates their advantages over existing parametrized Q-learning methods.
{"title":"Data-Efficient Quadratic Q-Learning Using LMIs","authors":"J. S. van Hulst, W. P. M. H. Heemels, D. J. Antunes","doi":"arxiv-2409.11986","DOIUrl":"https://doi.org/arxiv-2409.11986","url":null,"abstract":"Reinforcement learning (RL) has seen significant research and application\u0000results but often requires large amounts of training data. This paper proposes\u0000two data-efficient off-policy RL methods that use parametrized Q-learning. In\u0000these methods, the Q-function is chosen to be linear in the parameters and\u0000quadratic in selected basis functions in the state and control deviations from\u0000a base policy. A cost penalizing the $ell_1$-norm of Bellman errors is\u0000minimized. We propose two methods: Linear Matrix Inequality Q-Learning (LMI-QL)\u0000and its iterative variant (LMI-QLi), which solve the resulting episodic\u0000optimization problem through convex optimization. LMI-QL relies on a convex\u0000relaxation that yields a semidefinite programming (SDP) problem with linear\u0000matrix inequalities (LMIs). LMI-QLi entails solving sequential iterations of an\u0000SDP problem. Both methods combine convex optimization with direct Q-function\u0000learning, significantly improving learning speed. A numerical case study\u0000demonstrates their advantages over existing parametrized Q-learning methods.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264104","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}
We present an approach for systematically anticipating the actions and policies employed by emph{oblivious} environments in concurrent stochastic games, while maximizing a reward function. Our main contribution lies in the synthesis of a finite emph{information state machine} whose alphabet ranges over the actions of the environment. Each state of the automaton is mapped to a belief state about the policy used by the environment. We introduce a notion of consistency that guarantees that the belief states tracked by our automaton stays within a fixed distance of the precise belief state obtained by knowledge of the full history. We provide methods for checking consistency of an automaton and a synthesis approach which upon successful termination yields such a machine. We show how the information state machine yields an MDP that serves as the starting point for computing optimal policies for maximizing a reward function defined over plays. We present an experimental evaluation over benchmark examples including human activity data for tasks such as cataract surgery and furniture assembly, wherein our approach successfully anticipates the policies and actions of the environment in order to maximize the reward.
{"title":"Anticipating Oblivious Opponents in Stochastic Games","authors":"Shadi Tasdighi Kalat, Sriram Sankaranarayanan, Ashutosh Trivedi","doi":"arxiv-2409.11671","DOIUrl":"https://doi.org/arxiv-2409.11671","url":null,"abstract":"We present an approach for systematically anticipating the actions and\u0000policies employed by emph{oblivious} environments in concurrent stochastic\u0000games, while maximizing a reward function. Our main contribution lies in the\u0000synthesis of a finite emph{information state machine} whose alphabet ranges\u0000over the actions of the environment. Each state of the automaton is mapped to a\u0000belief state about the policy used by the environment. We introduce a notion of\u0000consistency that guarantees that the belief states tracked by our automaton\u0000stays within a fixed distance of the precise belief state obtained by knowledge\u0000of the full history. We provide methods for checking consistency of an\u0000automaton and a synthesis approach which upon successful termination yields\u0000such a machine. We show how the information state machine yields an MDP that\u0000serves as the starting point for computing optimal policies for maximizing a\u0000reward function defined over plays. We present an experimental evaluation over\u0000benchmark examples including human activity data for tasks such as cataract\u0000surgery and furniture assembly, wherein our approach successfully anticipates\u0000the policies and actions of the environment in order to maximize the reward.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"52 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264199","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}
With continuous advancements in science and technology, there is increasing focus on environmental sustainability, leading to heightened interest in autonomous electric vehicles (AEVs). AEVs hold significant potential for enhancing electric mobility, energy efficiency, environmental preservation, and driving capabilities. They offer numerous advantages that could revolutionize transportation and urban lifestyles, notably by improving road safety through the reduction of human error, a leading cause of accidents. This paper presents a comprehensive simulation of an AEV by developing and integrating models for the driving system, battery, motor, transmission, and vehicle body within the MATLAB/Simulink environment. Each component is configured and interconnected to create a pure vehicle model. The simulation is conducted under UDDS cycle conditions to evaluate the vehicle's performance in speed maintenance, driving distance, acceleration capabilities, and battery state-of-charge (SoC) dynamics. The results demonstrate that the vehicle exhibits strong output characteristics. Furthermore, the driver model incorporates an energy brake recovery function, which, when set to a 50% recovery efficiency, increases the driving distance by 25% compared to a vehicle without this function.
{"title":"Modeling and Simulation of a Fully Autonomous Electric Vehicle (AEV)","authors":"Qasim Ajao, Lanre Sadeeq","doi":"arxiv-2409.11641","DOIUrl":"https://doi.org/arxiv-2409.11641","url":null,"abstract":"With continuous advancements in science and technology, there is increasing\u0000focus on environmental sustainability, leading to heightened interest in\u0000autonomous electric vehicles (AEVs). AEVs hold significant potential for\u0000enhancing electric mobility, energy efficiency, environmental preservation, and\u0000driving capabilities. They offer numerous advantages that could revolutionize\u0000transportation and urban lifestyles, notably by improving road safety through\u0000the reduction of human error, a leading cause of accidents. This paper presents\u0000a comprehensive simulation of an AEV by developing and integrating models for\u0000the driving system, battery, motor, transmission, and vehicle body within the\u0000MATLAB/Simulink environment. Each component is configured and interconnected to\u0000create a pure vehicle model. The simulation is conducted under UDDS cycle\u0000conditions to evaluate the vehicle's performance in speed maintenance, driving\u0000distance, acceleration capabilities, and battery state-of-charge (SoC)\u0000dynamics. The results demonstrate that the vehicle exhibits strong output\u0000characteristics. Furthermore, the driver model incorporates an energy brake\u0000recovery function, which, when set to a 50% recovery efficiency, increases the\u0000driving distance by 25% compared to a vehicle without this function.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264247","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}
Dayuan Tan, Mohamed Younis, Wassila Lalouani, Shuyao Fan, Guozhi Song
The major advances in intelligent transportation systems are pushing societal services toward autonomy where road management is to be more agile in order to cope with changes and continue to yield optimal performance. However, the pedestrian experience is not sufficiently considered. Particularly, signalized intersections are expected to be popular if not dominant in urban settings where pedestrian density is high. This paper presents the design of a novel environment for simulating human motion on signalized crosswalks at a fine-grained level. Such a simulation not only captures typical behavior, but also handles cases where large pedestrian groups cross from both directions. The proposed simulator is instrumental for optimized road configuration management where the pedestrians' quality of experience, for example, waiting time, is factored in. The validation results using field data show that an accuracy of 98.37 percent can be obtained for the estimated crossing time. Other results using synthetic data show that our simulator enables optimized traffic light scheduling that diminishes pedestrians' waiting time without sacrificing vehicular throughput.
{"title":"A novel pedestrian road crossing simulator for dynamic traffic light scheduling systems","authors":"Dayuan Tan, Mohamed Younis, Wassila Lalouani, Shuyao Fan, Guozhi Song","doi":"arxiv-2409.11623","DOIUrl":"https://doi.org/arxiv-2409.11623","url":null,"abstract":"The major advances in intelligent transportation systems are pushing societal\u0000services toward autonomy where road management is to be more agile in order to\u0000cope with changes and continue to yield optimal performance. However, the\u0000pedestrian experience is not sufficiently considered. Particularly, signalized\u0000intersections are expected to be popular if not dominant in urban settings\u0000where pedestrian density is high. This paper presents the design of a novel\u0000environment for simulating human motion on signalized crosswalks at a\u0000fine-grained level. Such a simulation not only captures typical behavior, but\u0000also handles cases where large pedestrian groups cross from both directions.\u0000The proposed simulator is instrumental for optimized road configuration\u0000management where the pedestrians' quality of experience, for example, waiting\u0000time, is factored in. The validation results using field data show that an\u0000accuracy of 98.37 percent can be obtained for the estimated crossing time.\u0000Other results using synthetic data show that our simulator enables optimized\u0000traffic light scheduling that diminishes pedestrians' waiting time without\u0000sacrificing vehicular throughput.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"119 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264248","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}
The development of finite/fixed-time stable optimization algorithms typically involves study of specific problem instances. The lack of a unified framework hinders understanding of more sophisticated algorithms, e.g., primal-dual gradient flow dynamics. The purpose of this paper is to address the following question: Given an exponentially stable optimization algorithm, can it be modified to obtain a finite/fixed-time stable algorithm? We provide an affirmative answer, demonstrate how the solution can be computed on a finite-time interval via a simple scaling of the right-hand-side of the original dynamics, and certify the desired properties of the modified algorithm using the Lyapunov function that proves exponential stability of the original system. Finally, we examine nonsmooth composite optimization problems and smooth problems with linear constraints to demonstrate the merits of our approach.
{"title":"From exponential to finite/fixed-time stability: Applications to optimization","authors":"Ibrahim K. Ozaslan, Mihailo R. Jovanović","doi":"arxiv-2409.11713","DOIUrl":"https://doi.org/arxiv-2409.11713","url":null,"abstract":"The development of finite/fixed-time stable optimization algorithms typically\u0000involves study of specific problem instances. The lack of a unified framework\u0000hinders understanding of more sophisticated algorithms, e.g., primal-dual\u0000gradient flow dynamics. The purpose of this paper is to address the following\u0000question: Given an exponentially stable optimization algorithm, can it be\u0000modified to obtain a finite/fixed-time stable algorithm? We provide an\u0000affirmative answer, demonstrate how the solution can be computed on a\u0000finite-time interval via a simple scaling of the right-hand-side of the\u0000original dynamics, and certify the desired properties of the modified algorithm\u0000using the Lyapunov function that proves exponential stability of the original\u0000system. Finally, we examine nonsmooth composite optimization problems and\u0000smooth problems with linear constraints to demonstrate the merits of our\u0000approach.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264198","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}
Amir-Mohammad Esmaieeli-Sikaroudi, Boris Goikhman, Dmitri Chubarov, Hung Dinh Nguyen, Michael Chertkov, Petr Vorobev
Energy efficiency of buildings is considered to be one of the major means of achieving the net-zero carbon goal around the world. The big part of the energy savings are supposed to be coming from optimizing the operation of the building heating, ventilation, and air conditioning (HVAC) systems. There is a natural trade-off between the energy efficiency and the indoor comfort level, and finding an optimal operating schedule/regime requires knowing the occupancy of different spaces inside of the building. Moreover, the COVID-19 pandemic has also revealed the need to sustain the high quality of the indoor air in order to reduce the risk of spread of infection. Occupancy detection from indoor sensors is thus an important practical problem. In the present paper, we propose detection of occupancy based on the carbon dioxide measurements inside the building. In particular, a new approach based on the, so-called, switching auto-regressive process with Markov regime is presented and justified by the physical model of the carbon dioxide concentration dynamics. We demonstrate the efficiency of the method compared to simple Hidden Markov approaches on simulated and real-life data. We also show that the model is flexible and can be generalized to account for different ventilation regimes, simultaneously detecting the occupancy and the ventilation rate.
{"title":"Physics-Informed Building Occupancy Detection: a Switching Process with Markov Regime","authors":"Amir-Mohammad Esmaieeli-Sikaroudi, Boris Goikhman, Dmitri Chubarov, Hung Dinh Nguyen, Michael Chertkov, Petr Vorobev","doi":"arxiv-2409.11743","DOIUrl":"https://doi.org/arxiv-2409.11743","url":null,"abstract":"Energy efficiency of buildings is considered to be one of the major means of\u0000achieving the net-zero carbon goal around the world. The big part of the energy\u0000savings are supposed to be coming from optimizing the operation of the building\u0000heating, ventilation, and air conditioning (HVAC) systems. There is a natural\u0000trade-off between the energy efficiency and the indoor comfort level, and\u0000finding an optimal operating schedule/regime requires knowing the occupancy of\u0000different spaces inside of the building. Moreover, the COVID-19 pandemic has\u0000also revealed the need to sustain the high quality of the indoor air in order\u0000to reduce the risk of spread of infection. Occupancy detection from indoor\u0000sensors is thus an important practical problem. In the present paper, we\u0000propose detection of occupancy based on the carbon dioxide measurements inside\u0000the building. In particular, a new approach based on the, so-called, switching\u0000auto-regressive process with Markov regime is presented and justified by the\u0000physical model of the carbon dioxide concentration dynamics. We demonstrate the\u0000efficiency of the method compared to simple Hidden Markov approaches on\u0000simulated and real-life data. We also show that the model is flexible and can\u0000be generalized to account for different ventilation regimes, simultaneously\u0000detecting the occupancy and the ventilation rate.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"92 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264112","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}
Consensus control of multiagent systems arises in various robotic applications such as rendezvous and formation control. For example, to compute the control inputs of individual agents, the difference in the positions in aligned coordinate frames i.e., the pairwise displacements are typically measured. However, the local coordinate frames might be subject to rotational ambiguities, such as a rotation or a reflection, particularly if the positions of the agent are not directly observed but reconstructed from e.g. pairwise Euclidean distances. This rotational ambiguity causes stability issues in practice, as agents have rotated perceptions of the environment. In this work, we conduct a thorough analysis of the stability in the presence of rotational ambiguities in several scenarios including e.g., proper and improper rotation, and the homogeneity of rotations. We give stability criteria and stability margin on the rotations, which are numerically verified with two traditional examples of consensus control.
{"title":"On the Stability of Consensus Control under Rotational Ambiguities","authors":"Zhonggang Li, Changheng Li, Raj Thilak Rajan","doi":"arxiv-2409.11979","DOIUrl":"https://doi.org/arxiv-2409.11979","url":null,"abstract":"Consensus control of multiagent systems arises in various robotic\u0000applications such as rendezvous and formation control. For example, to compute\u0000the control inputs of individual agents, the difference in the positions in\u0000aligned coordinate frames i.e., the pairwise displacements are typically\u0000measured. However, the local coordinate frames might be subject to rotational\u0000ambiguities, such as a rotation or a reflection, particularly if the positions\u0000of the agent are not directly observed but reconstructed from e.g. pairwise\u0000Euclidean distances. This rotational ambiguity causes stability issues in\u0000practice, as agents have rotated perceptions of the environment. In this work,\u0000we conduct a thorough analysis of the stability in the presence of rotational\u0000ambiguities in several scenarios including e.g., proper and improper rotation,\u0000and the homogeneity of rotations. We give stability criteria and stability\u0000margin on the rotations, which are numerically verified with two traditional\u0000examples of consensus control.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264106","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}
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