Pub Date : 2022-06-08DOI: 10.23919/ACC53348.2022.9867373
Keith Paarporn, R. Chandan, M. Alizadeh, Jason R. Marden
In this paper, we consider problems involving a central commander that must assign a pool of available resources to two separate competitions. In each competition, a sub-colonel allocates its endowed resources from the assignment against an opponent. We consider General Lotto games as the underlying model of competition. Here, we also take into account that the commander’s randomized resource assignments cause the opponents to have uncertainty about the sub-commanders’ actual assigned endowments. We find that randomized assignments, which induce General Lotto games of incomplete and asymmetric information in the component competitions, do not offer strategic advantages over deterministic ones when the opponents have fixed resource endowments. However, this is not the case when the opponents have per-unit costs to utilize resources. We find the optimal randomized assignment strategy can actually improve the commander’s payoff two-fold when compared to optimal deterministic assignments.
{"title":"The importance of randomization in resource assignment problems","authors":"Keith Paarporn, R. Chandan, M. Alizadeh, Jason R. Marden","doi":"10.23919/ACC53348.2022.9867373","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867373","url":null,"abstract":"In this paper, we consider problems involving a central commander that must assign a pool of available resources to two separate competitions. In each competition, a sub-colonel allocates its endowed resources from the assignment against an opponent. We consider General Lotto games as the underlying model of competition. Here, we also take into account that the commander’s randomized resource assignments cause the opponents to have uncertainty about the sub-commanders’ actual assigned endowments. We find that randomized assignments, which induce General Lotto games of incomplete and asymmetric information in the component competitions, do not offer strategic advantages over deterministic ones when the opponents have fixed resource endowments. However, this is not the case when the opponents have per-unit costs to utilize resources. We find the optimal randomized assignment strategy can actually improve the commander’s payoff two-fold when compared to optimal deterministic assignments.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125758274","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}
Pub Date : 2022-06-08DOI: 10.23919/ACC53348.2022.9867313
A. A. Thompson, Leela Cañuelas, D. Paley
Inspired by the periodic swimming of many fish species, this paper presents a dynamic model of self-propelled particles with a periodic controller. The dynamics are split into a burst phase during which each particle applies a control input and a coast phase during which each particle performs state estimation. Using a closed-loop heading controller and a linear observer, we evaluate conditions that stabilize the equilibrium points for a single particle and for multiple particles using noise-free state feedback or output feedback. Practical stability bounds are evaluated for a single particle with bounded actuator noise with state feedback and bounded sensor noise with output feedback.
{"title":"Estimation and Control for Collective Motion with Intermittent Locomotion","authors":"A. A. Thompson, Leela Cañuelas, D. Paley","doi":"10.23919/ACC53348.2022.9867313","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867313","url":null,"abstract":"Inspired by the periodic swimming of many fish species, this paper presents a dynamic model of self-propelled particles with a periodic controller. The dynamics are split into a burst phase during which each particle applies a control input and a coast phase during which each particle performs state estimation. Using a closed-loop heading controller and a linear observer, we evaluate conditions that stabilize the equilibrium points for a single particle and for multiple particles using noise-free state feedback or output feedback. Practical stability bounds are evaluated for a single particle with bounded actuator noise with state feedback and bounded sensor noise with output feedback.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125931417","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}
Pub Date : 2022-06-08DOI: 10.23919/ACC53348.2022.9867216
T. Guffanti, S. D’Amico
Miniaturized space-borne multi-agent systems, a.k.a spacecraft swarms, promise to overcome the limitations of monolithic satellites, across the board, from capabilities to mission costs. Nevertheless, the use of low-size-weight-and-power as well as commercial-off-the-shelf hardware make them more prone to contingencies which may cause loss of control capabilities. Therefore, spacecraft swarms shall be collision-free in the presence of contingencies that prevent trajectory re-planning and control on the short term. Passive safety consists in guaranteeing safe separation robust to control losses, while in presence of system uncertainties. This paper explores both closed-form and direct optimization-based approaches to swarm passive safety, highlighting advantages, limitations and comparing them on a representative test case drawn from the upcoming VIrtual Super Optics Reconfigurable Swarm (VISORS) mission. The underlying shared mathematical foundation is the modeling of the swarm dynamics through a state parameterization in integration constants. Such state choice provides unique geometrical intuition at closed-form level, and makes passive safety tractable at direct optimization level. In particular, it permits to exploit variation of parameters to reduce the number of constraints required to enforce passive safety, and to compensate for nonintegrable dynamics and uncertainty effects. Besides the key trades, this paper shows how the presented approaches can enable tight and reconfigurable satellite swarming in the presence of contingencies.
{"title":"Robust Passively Safe Spacecraft Swarming via Closed-form and Optimization-based Control Approaches","authors":"T. Guffanti, S. D’Amico","doi":"10.23919/ACC53348.2022.9867216","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867216","url":null,"abstract":"Miniaturized space-borne multi-agent systems, a.k.a spacecraft swarms, promise to overcome the limitations of monolithic satellites, across the board, from capabilities to mission costs. Nevertheless, the use of low-size-weight-and-power as well as commercial-off-the-shelf hardware make them more prone to contingencies which may cause loss of control capabilities. Therefore, spacecraft swarms shall be collision-free in the presence of contingencies that prevent trajectory re-planning and control on the short term. Passive safety consists in guaranteeing safe separation robust to control losses, while in presence of system uncertainties. This paper explores both closed-form and direct optimization-based approaches to swarm passive safety, highlighting advantages, limitations and comparing them on a representative test case drawn from the upcoming VIrtual Super Optics Reconfigurable Swarm (VISORS) mission. The underlying shared mathematical foundation is the modeling of the swarm dynamics through a state parameterization in integration constants. Such state choice provides unique geometrical intuition at closed-form level, and makes passive safety tractable at direct optimization level. In particular, it permits to exploit variation of parameters to reduce the number of constraints required to enforce passive safety, and to compensate for nonintegrable dynamics and uncertainty effects. Besides the key trades, this paper shows how the presented approaches can enable tight and reconfigurable satellite swarming in the presence of contingencies.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129500965","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}
Pub Date : 2022-06-08DOI: 10.23919/ACC53348.2022.9867162
Akshay Ajagekar, F. You
This work proposes a novel deep reinforcement learning (DRL) based control framework for greenhouse climate control. This framework utilizes a neural network to approximate state-action value estimation. The neural network is trained by adopting a Q-learning based approach for experience collection and parameter updates. Continuous action spaces are effectively handled by the proposed approach by extracting optimal actions for a given greenhouse state from the neural network approximator through stochastic gradient ascent. Analytical gradients of the state-action value estimate are not required but can be computed effectively through backpropagation. We evaluate the performance of our DRL algorithm on a semi-closed greenhouse simulation located in New York City. The obtained computational results indicate that the proposed Q-learning based DRL framework yields higher cumulative returns. They also demonstrate that the proposed control technique consumes 61% lesser energy than deep deterministic policy gradient (DDPG) method.
{"title":"Deep Reinforcement Learning Based Automatic Control in Semi-Closed Greenhouse Systems","authors":"Akshay Ajagekar, F. You","doi":"10.23919/ACC53348.2022.9867162","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867162","url":null,"abstract":"This work proposes a novel deep reinforcement learning (DRL) based control framework for greenhouse climate control. This framework utilizes a neural network to approximate state-action value estimation. The neural network is trained by adopting a Q-learning based approach for experience collection and parameter updates. Continuous action spaces are effectively handled by the proposed approach by extracting optimal actions for a given greenhouse state from the neural network approximator through stochastic gradient ascent. Analytical gradients of the state-action value estimate are not required but can be computed effectively through backpropagation. We evaluate the performance of our DRL algorithm on a semi-closed greenhouse simulation located in New York City. The obtained computational results indicate that the proposed Q-learning based DRL framework yields higher cumulative returns. They also demonstrate that the proposed control technique consumes 61% lesser energy than deep deterministic policy gradient (DDPG) method.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129722838","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}
Pub Date : 2022-06-08DOI: 10.23919/ACC53348.2022.9867224
Rongjian Liu, Jianquan Lu, C. Hadjicostis
The analysis of infinite-step opacity in the context of stochastic discrete-event systems has been investigated as almost-infinite-step opacity for quantitative purposes. In this paper, we revisit the verification problem for almost-infinite-step opacity by concentrating on reducing its computational complexity. One of the key steps in the verification of almost-infinite-step opacity is the recognition of the unsafe language for infinite-step opacity. Inspired by recently developed techniques in the literature, we present an improved methodology for the calculation of the unsafe language for infinite-step opacity, which further improves the complexity of the verification of almost-infinite-step opacity.
{"title":"Reduced Complexity Verification of Almost-Infinite-Step Opacity in Stochastic Discrete-Event Systems*","authors":"Rongjian Liu, Jianquan Lu, C. Hadjicostis","doi":"10.23919/ACC53348.2022.9867224","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867224","url":null,"abstract":"The analysis of infinite-step opacity in the context of stochastic discrete-event systems has been investigated as almost-infinite-step opacity for quantitative purposes. In this paper, we revisit the verification problem for almost-infinite-step opacity by concentrating on reducing its computational complexity. One of the key steps in the verification of almost-infinite-step opacity is the recognition of the unsafe language for infinite-step opacity. Inspired by recently developed techniques in the literature, we present an improved methodology for the calculation of the unsafe language for infinite-step opacity, which further improves the complexity of the verification of almost-infinite-step opacity.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128389447","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}
Pub Date : 2022-06-08DOI: 10.23919/ACC53348.2022.9867225
G. Perrino, G. Stan
Gene expression depends on the cellular con-text. One major contributor to gene expression variability is competition for limited transcriptional and translational re-sources, which may induce indirect couplings among otherwise independently-regulated genes. Here, we apply control theoretical concepts and tools to design an incoherent feedforward loop (iFFL) biomolecular controller operating in mammalian cells using translational-resource competition couplings. Harnessing a resource-aware mathematical model, we demonstrate analytically and computationally that our resource-aware design can achieve near-constant set-point regulation of gene expression whilst ensuring robustness to plasmid uptake variation. We also provide an analytical condition on the model parameters to guide the design of the resource-aware iFFL controller ensuring robustness and performance in set-point regulation. Our theoretical design based on translational-resource competition couplings represents a promising approach to build more sophisticated resource-aware control circuits operating at the host-cell level.
{"title":"Robust set-point regulation of gene expression using resource competition couplings in mammalian cells","authors":"G. Perrino, G. Stan","doi":"10.23919/ACC53348.2022.9867225","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867225","url":null,"abstract":"Gene expression depends on the cellular con-text. One major contributor to gene expression variability is competition for limited transcriptional and translational re-sources, which may induce indirect couplings among otherwise independently-regulated genes. Here, we apply control theoretical concepts and tools to design an incoherent feedforward loop (iFFL) biomolecular controller operating in mammalian cells using translational-resource competition couplings. Harnessing a resource-aware mathematical model, we demonstrate analytically and computationally that our resource-aware design can achieve near-constant set-point regulation of gene expression whilst ensuring robustness to plasmid uptake variation. We also provide an analytical condition on the model parameters to guide the design of the resource-aware iFFL controller ensuring robustness and performance in set-point regulation. Our theoretical design based on translational-resource competition couplings represents a promising approach to build more sophisticated resource-aware control circuits operating at the host-cell level.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128654230","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}
Pub Date : 2022-06-08DOI: 10.23919/ACC53348.2022.9867883
Richard Hugh Moulton, S. Scott, K. Rudie
Controlling a discrete-event system commonly entails synthesizing a supervisor to ensure that the plant’s closed-loop behaviour respects a certain specification. In the traditional approach to this problem, if the desired behaviour is not controllable then the specification’s supremal controllable sublanguage is enforced instead. Here we invert the problem and formulate the Minimum Control Base Problem, with the goal of finding the minimum set of controllable events that guarantees controllability for the desired behaviour. We show that the sets of controllable events that maintain controllability for the desired behaviour form a complete lattice with respect to subset inclusion and that there is therefore a minimum capability supervisor for any desired sublanguage of the plant’s behaviour. We apply our techniques to the Small Factory problem and discuss further applications including systems design, dynamic discrete-event systems, and biological systems.
{"title":"Synthesizing Supervisors with a Minimum Control Base for Discrete-Event Systems","authors":"Richard Hugh Moulton, S. Scott, K. Rudie","doi":"10.23919/ACC53348.2022.9867883","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867883","url":null,"abstract":"Controlling a discrete-event system commonly entails synthesizing a supervisor to ensure that the plant’s closed-loop behaviour respects a certain specification. In the traditional approach to this problem, if the desired behaviour is not controllable then the specification’s supremal controllable sublanguage is enforced instead. Here we invert the problem and formulate the Minimum Control Base Problem, with the goal of finding the minimum set of controllable events that guarantees controllability for the desired behaviour. We show that the sets of controllable events that maintain controllability for the desired behaviour form a complete lattice with respect to subset inclusion and that there is therefore a minimum capability supervisor for any desired sublanguage of the plant’s behaviour. We apply our techniques to the Small Factory problem and discuss further applications including systems design, dynamic discrete-event systems, and biological systems.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127034285","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}
Pub Date : 2022-06-08DOI: 10.23919/ACC53348.2022.9867803
Christoffer Sloth, Aljaz Kramberger, Emil Lykke Diget, Iñigo Iturrate
In this paper, we present contact point and surface normal estimators for robotic applications with flexible tools. The estimators rely on state information of a flexible tool model; this information is obtained from an unknown input observer. The observer uses force and torque measurements at the root of the flexible tool to estimate the deflection of the tool although the force applied to the tip of the tool is unknown. The flexible tool is modeled with a finite element approximation of an Euler-Bernoulli beam model including contact forces between the flexible tool tip and the environment.The unknown input observer provides estimates of the contact point between the flexible tool and the rigid environment in addition to the contact force. This information is subsequently used to estimate a surface normal of the environment. The estimators can be deployed together with an adaptive parallel position/force controller to ensure tracking of position and force references for the tip of a flexible tool.The proposed estimation algorithm is verified in simulation and validated in real robot experiments. The method enables accurate force and position tracking in addition to adaptation to the surface geometry.
{"title":"Towards Contact Point and Surface Normal Estimation for Control of Flexible Tool","authors":"Christoffer Sloth, Aljaz Kramberger, Emil Lykke Diget, Iñigo Iturrate","doi":"10.23919/ACC53348.2022.9867803","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867803","url":null,"abstract":"In this paper, we present contact point and surface normal estimators for robotic applications with flexible tools. The estimators rely on state information of a flexible tool model; this information is obtained from an unknown input observer. The observer uses force and torque measurements at the root of the flexible tool to estimate the deflection of the tool although the force applied to the tip of the tool is unknown. The flexible tool is modeled with a finite element approximation of an Euler-Bernoulli beam model including contact forces between the flexible tool tip and the environment.The unknown input observer provides estimates of the contact point between the flexible tool and the rigid environment in addition to the contact force. This information is subsequently used to estimate a surface normal of the environment. The estimators can be deployed together with an adaptive parallel position/force controller to ensure tracking of position and force references for the tip of a flexible tool.The proposed estimation algorithm is verified in simulation and validated in real robot experiments. The method enables accurate force and position tracking in addition to adaptation to the surface geometry.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127122842","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}
Pub Date : 2022-06-08DOI: 10.23919/ACC53348.2022.9867676
Ziqiao Zhang, Wencen Wu, Fumin Zhang
In this paper, a constrained cooperative Kalman filter is developed to estimate field values and gradients along trajectories of mobile robots collecting measurements. We assume the underlying field is generated by a polynomial partial differential equation with unknown time-varying parameters. A long short-term memory (LSTM) based Kalman filter, is applied for the parameter estimation leveraging the updated state estimates from the constrained cooperative Kalman filter. Convergence for the constrained cooperative Kalman filter has been justified. Simulation results in a 2-dimensional field are provided to validate the proposed method.
{"title":"Cooperative Filtering and Parameter Estimation for Polynomial PDEs using a Mobile Sensor Network","authors":"Ziqiao Zhang, Wencen Wu, Fumin Zhang","doi":"10.23919/ACC53348.2022.9867676","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867676","url":null,"abstract":"In this paper, a constrained cooperative Kalman filter is developed to estimate field values and gradients along trajectories of mobile robots collecting measurements. We assume the underlying field is generated by a polynomial partial differential equation with unknown time-varying parameters. A long short-term memory (LSTM) based Kalman filter, is applied for the parameter estimation leveraging the updated state estimates from the constrained cooperative Kalman filter. Convergence for the constrained cooperative Kalman filter has been justified. Simulation results in a 2-dimensional field are provided to validate the proposed method.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127123231","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}
Pub Date : 2022-06-08DOI: 10.23919/ACC53348.2022.9867558
Mattia Mattioni, S. Monaco, D. Normand-Cyrot
In this paper, a new connection protocol for consensus of multi-agent discrete-time systems under a general communication graph is proposed. In particular, the coupling is realized based on the outputs making each agent passive in the u-average sense so guaranteeing convergence to the agreement steady-state, with no need of mitigating the coupling gain, as typically done in concerned literature. The proposed connection rule is shown to apply for network dynamics under aperiodic sampling when the sampling sequence is known to all agents.
{"title":"A new connection protocol for multi-consensus of discrete-time systems","authors":"Mattia Mattioni, S. Monaco, D. Normand-Cyrot","doi":"10.23919/ACC53348.2022.9867558","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867558","url":null,"abstract":"In this paper, a new connection protocol for consensus of multi-agent discrete-time systems under a general communication graph is proposed. In particular, the coupling is realized based on the outputs making each agent passive in the u-average sense so guaranteeing convergence to the agreement steady-state, with no need of mitigating the coupling gain, as typically done in concerned literature. The proposed connection rule is shown to apply for network dynamics under aperiodic sampling when the sampling sequence is known to all agents.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127269083","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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