Trust model is a topic that first gained interest in organizational studies and then human factors in automation. Thanks to recent advances in human-robot interaction (HRI) and human-autonomy teaming, human trust in robots has gained growing interest among researchers and practitioners. This article focuses on a survey of computational models of human-robot trust and their applications in robotics and robot controls. The motivation is to provide an overview of the state-of-the-art computational methods to quantify trust so as to provide feedback and situational awareness in HRI. Different from other existing survey papers on human-robot trust models, we seek to provide in-depth coverage of the trust model categorization, formulation, and analysis, with a focus on their utilization in robotics and robot controls. The paper starts with a discussion of the difference between human-robot trust with general agent-agent trust, interpersonal trust, and human trust in automation and machines. A list of impacting factors for human-robot trust and different trust measurement approaches, and their corresponding scales are summarized. We then review existing computational human-robot trust models and discuss the pros and cons of each category of models. These include performance-centric algebraic, time-series, Markov decision process (MDP)/Partially Observable MDP (POMDP)-based, Gaussian-based, and dynamic Bayesian network (DBN)-based trust models. Following the summary of each computational human-robot trust model, we examine its utilization in robot control applications, if any. We also enumerate the main limitations and open questions in this field and discuss potential future research directions.
{"title":"Human Trust in Robots: A Survey on Trust Models and Their Controls/Robotics Applications","authors":"Yue Wang;Fangjian Li;Huanfei Zheng;Longsheng Jiang;Maziar Fooladi Mahani;Zhanrui Liao","doi":"10.1109/OJCSYS.2023.3345090","DOIUrl":"https://doi.org/10.1109/OJCSYS.2023.3345090","url":null,"abstract":"Trust model is a topic that first gained interest in organizational studies and then human factors in automation. Thanks to recent advances in human-robot interaction (HRI) and human-autonomy teaming, human trust in robots has gained growing interest among researchers and practitioners. This article focuses on a survey of computational models of human-robot trust and their applications in robotics and robot controls. The motivation is to provide an overview of the state-of-the-art computational methods to quantify trust so as to provide feedback and situational awareness in HRI. Different from other existing survey papers on human-robot trust models, we seek to provide in-depth coverage of the trust model categorization, formulation, and analysis, with a focus on their utilization in robotics and robot controls. The paper starts with a discussion of the difference between human-robot trust with general agent-agent trust, interpersonal trust, and human trust in automation and machines. A list of impacting factors for human-robot trust and different trust measurement approaches, and their corresponding scales are summarized. We then review existing computational human-robot trust models and discuss the pros and cons of each category of models. These include performance-centric algebraic, time-series, Markov decision process (MDP)/Partially Observable MDP (POMDP)-based, Gaussian-based, and dynamic Bayesian network (DBN)-based trust models. Following the summary of each computational human-robot trust model, we examine its utilization in robot control applications, if any. We also enumerate the main limitations and open questions in this field and discuss potential future research directions.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"3 ","pages":"58-86"},"PeriodicalIF":0.0,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10366819","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139431049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-15DOI: 10.1109/OJCSYS.2023.3315635
{"title":"IEEE Control Systems Society Information","authors":"","doi":"10.1109/OJCSYS.2023.3315635","DOIUrl":"https://doi.org/10.1109/OJCSYS.2023.3315635","url":null,"abstract":"","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"2 ","pages":"C3-C3"},"PeriodicalIF":0.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10361563","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138678663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-15DOI: 10.1109/OJCSYS.2023.3315631
{"title":"IEEE Open Journal of Control Systems Publication Information","authors":"","doi":"10.1109/OJCSYS.2023.3315631","DOIUrl":"https://doi.org/10.1109/OJCSYS.2023.3315631","url":null,"abstract":"","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"2 ","pages":"C2-C2"},"PeriodicalIF":0.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10361595","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138713667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-15DOI: 10.1109/OJCSYS.2023.3343598
Takeshi Hatanaka;Takahiro Mochizuki;Takumi Sumino;José M. Maestre;Nikhil Chopra
In this article, we study a one-human-multiple-robot interaction for human-enabled multi-robot navigation in three dimensions. We employ two fully distributed control architectures designed based on human passivity and human passivity shortage. The first half of this article focuses on human modeling and analysis for the passivity-based control architecture through human operation data on a 3-D human-in-the-loop simulator. Specifically, we compare virtual reality (VR) interfaces with a traditional interface, and examine the impacts that VR technology has on human properties in terms of model accuracy, performance, passivity and workload, demonstrating that VR interfaces have a positive effect on all aspects. In contrast to 1-D operation, we confirm that operators hardly attain passivity regardless of the network structure, even with the VR interfaces. We thus take the passivity-shortage-based control architecture and analyze the degree of passivity shortage. We then observe through user studies that operators tend to meet the degree of shortage needed to prove closed-loop stability.
{"title":"Human Modeling and Passivity Analysis for Semi-Autonomous Multi-Robot Navigation in Three Dimensions","authors":"Takeshi Hatanaka;Takahiro Mochizuki;Takumi Sumino;José M. Maestre;Nikhil Chopra","doi":"10.1109/OJCSYS.2023.3343598","DOIUrl":"https://doi.org/10.1109/OJCSYS.2023.3343598","url":null,"abstract":"In this article, we study a one-human-multiple-robot interaction for human-enabled multi-robot navigation in three dimensions. We employ two fully distributed control architectures designed based on human passivity and human passivity shortage. The first half of this article focuses on human modeling and analysis for the passivity-based control architecture through human operation data on a 3-D human-in-the-loop simulator. Specifically, we compare virtual reality (VR) interfaces with a traditional interface, and examine the impacts that VR technology has on human properties in terms of model accuracy, performance, passivity and workload, demonstrating that VR interfaces have a positive effect on all aspects. In contrast to 1-D operation, we confirm that operators hardly attain passivity regardless of the network structure, even with the VR interfaces. We thus take the passivity-shortage-based control architecture and analyze the degree of passivity shortage. We then observe through user studies that operators tend to meet the degree of shortage needed to prove closed-loop stability.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"3 ","pages":"45-57"},"PeriodicalIF":0.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10361530","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139431037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-06DOI: 10.1109/OJCSYS.2023.3340034
Sooyung Byeon;Joonwon Choi;Inseok Hwang
This article proposes a quantitative framework for optimally allocating task functions in human-autonomy teaming (HAT). HAT involves cooperation between humans and autonomous agents to achieve common goals. As humans and autonomous agents possess different capabilities, function allocation plays a crucial role in ensuring effective HAT. However, designing the best adaptive function allocation remains a challenge, as existing methods often rely on qualitative rules and intensive human-subject studies. To address this limitation, we propose a computational function allocation approach that leverages cognitive engineering, computational work models, and optimization techniques. The proposed optimal adaptive function allocation method is composed of three main elements: 1) analyze the teamwork to identify a set of all possible function allocations within a team construction, 2) numerically simulate the teamwork in temporal semantics to explore the interaction of the team with complex environments using the identified function allocations in a trial-and-error manner, and 3) optimize the adaptive function allocation with respect to a given situation such as physical conditions, available information resources, and human mental workload. For the optimization, we utilize performance metrics such as task performance, human mental workload, and coherency in function allocations. To illustrate the effectiveness of the proposed framework, we present a simulated HAT scenario involving a human work model and drone fleet for last-mile delivery in disaster relief operations.
本文提出了一个定量框架，用于优化人类-自主团队合作（HAT）中的任务功能分配。HAT 涉及人类与自主代理之间为实现共同目标而开展的合作。由于人类和自主代理拥有不同的能力，功能分配在确保有效的 HAT 中起着至关重要的作用。然而，设计最佳的自适应功能分配仍然是一项挑战，因为现有的方法通常依赖于定性规则和密集的人体研究。为了解决这一局限性，我们提出了一种计算功能分配方法，该方法利用了认知工程学、计算工作模型和优化技术。所提出的最优自适应功能分配方法由三个主要元素组成：1）分析团队工作，确定团队建设中所有可能的功能分配集合；2）在时间语义上对团队工作进行数值模拟，以试错方式探索团队与复杂环境的互动，并使用确定的功能分配；3）针对给定情况（如物理条件、可用信息资源和人的心理工作量）优化自适应功能分配。在优化过程中，我们利用任务性能、人的精神工作量和功能分配一致性等性能指标。为了说明所提框架的有效性，我们提出了一个模拟的 HAT 场景，其中涉及人类工作模型和无人机队，用于救灾行动中的最后一英里配送。
{"title":"A Computational Framework for Optimal Adaptive Function Allocation in a Human-Autonomy Teaming Scenario","authors":"Sooyung Byeon;Joonwon Choi;Inseok Hwang","doi":"10.1109/OJCSYS.2023.3340034","DOIUrl":"https://doi.org/10.1109/OJCSYS.2023.3340034","url":null,"abstract":"This article proposes a quantitative framework for optimally allocating task functions in human-autonomy teaming (HAT). HAT involves cooperation between humans and autonomous agents to achieve common goals. As humans and autonomous agents possess different capabilities, function allocation plays a crucial role in ensuring effective HAT. However, designing the best adaptive function allocation remains a challenge, as existing methods often rely on qualitative rules and intensive human-subject studies. To address this limitation, we propose a computational function allocation approach that leverages cognitive engineering, computational work models, and optimization techniques. The proposed optimal adaptive function allocation method is composed of three main elements: 1) analyze the teamwork to identify a set of all possible function allocations within a team construction, 2) numerically simulate the teamwork in temporal semantics to explore the interaction of the team with complex environments using the identified function allocations in a trial-and-error manner, and 3) optimize the adaptive function allocation with respect to a given situation such as physical conditions, available information resources, and human mental workload. For the optimization, we utilize performance metrics such as task performance, human mental workload, and coherency in function allocations. To illustrate the effectiveness of the proposed framework, we present a simulated HAT scenario involving a human work model and drone fleet for last-mile delivery in disaster relief operations.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"3 ","pages":"32-44"},"PeriodicalIF":0.0,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10345767","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139041247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-08DOI: 10.1109/OJCSYS.2023.3331193
Hafiz Ahsan Said;Demián García-Violini;Nicolás Faedo;John V. Ringwood
Optimal control of wave energy converters (WECs), while converting wave energy into a usable form, such as electricity, may inject (reactive) power into the system at various points in the wave cycle. Though somewhat counter-intuitive, this action usually results in improved overall energy conversion. However, recent experimental results show that, on occasion, reactive power peaks can be significantly in excess of active power levels, leaving device developers with difficult decision in how to rate the power take-off of the system i.e. whether to cater for these high reactive power peaks, or limit power flow to rated (active) levels. The origins of these excessive power peaks are currently poorly understood, creating significant uncertainty in how to deal with them. In this paper, we show that, using both theoretical results and an illustrative simulation case study, under matched controller conditions (impedance-matching optimal condition), for both monochromatic and panchromatic sea-states, that the maximum peak reactive/active power ratio never exceeds unity . However, under mismatched WEC/controller conditions, this peak power ratio can exceed unity, bringing unrealistic demands on the power take-off (PTO) rating. The paper examines the various origins of system/controller mismatch, including modelling error, controller synthesis inaccuracies, and non-ideal PTO behaviour, highlighting the consequences of such errors on reactive power flow levels. This important result points to the need for accurate WEC modeling, while also showing the folly of catering for excessive reactive power peaks.
{"title":"On the Ratio of Reactive to Active Power in Wave Energy Converter Control","authors":"Hafiz Ahsan Said;Demián García-Violini;Nicolás Faedo;John V. Ringwood","doi":"10.1109/OJCSYS.2023.3331193","DOIUrl":"10.1109/OJCSYS.2023.3331193","url":null,"abstract":"Optimal control of wave energy converters (WECs), while converting wave energy into a usable form, such as electricity, may \u0000<italic>inject</i>\u0000 (reactive) power into the system at various points in the wave cycle. Though somewhat counter-intuitive, this action usually results in improved overall energy conversion. However, recent experimental results show that, on occasion, reactive power peaks can be significantly in excess of active power levels, leaving device developers with difficult decision in how to rate the power take-off of the system i.e. whether to cater for these high reactive power peaks, or limit power flow to rated (active) levels. The origins of these excessive power peaks are currently poorly understood, creating significant uncertainty in how to deal with them. In this paper, we show that, using both theoretical results and an illustrative simulation case study, \u0000<italic>under matched controller conditions</i>\u0000 (impedance-matching optimal condition), for both monochromatic and panchromatic sea-states, that the maximum peak reactive/active power ratio \u0000<italic>never exceeds unity</i>\u0000. However, under mismatched WEC/controller conditions, this peak power ratio can exceed unity, bringing unrealistic demands on the power take-off (PTO) rating. The paper examines the various origins of system/controller mismatch, including modelling error, controller synthesis inaccuracies, and non-ideal PTO behaviour, highlighting the consequences of such errors on reactive power flow levels. This important result points to the need for accurate WEC modeling, while also showing the folly of catering for excessive reactive power peaks.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"3 ","pages":"14-31"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10313027","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135560786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-08DOI: 10.1109/OJCSYS.2023.3331195
Yuzhen Qin;Alberto Maria Nobili;Danielle S. Bassett;Fabio Pasqualetti
Cluster synchronization is of great importance for the normal functioning of numerous technological and natural systems. Deviations from normal cluster synchronization patterns are closely associated with various malfunctions, such as neurological disorders in the brain. Therefore, it is crucial to restore normal system functions by stabilizing the appropriate cluster synchronization patterns. Most existing studies focus on designing controllers based on state measurements to achieve system stabilization. However, in many real-world scenarios, measuring system states in real time, such as neuronal activity in the brain, poses significant challenges, rendering the stabilization of such systems difficult. To overcome this challenge, in this article, we employ an open-loop control strategy, vibrational control , which does not require any state measurements. We establish some sufficient conditions under which vibrational inputs stabilize cluster synchronization. Further, we provide a tractable approach to design vibrational control. Finally, numerical experiments are conducted to demonstrate our theoretical findings.
{"title":"Vibrational Stabilization of Cluster Synchronization in Oscillator Networks","authors":"Yuzhen Qin;Alberto Maria Nobili;Danielle S. Bassett;Fabio Pasqualetti","doi":"10.1109/OJCSYS.2023.3331195","DOIUrl":"10.1109/OJCSYS.2023.3331195","url":null,"abstract":"Cluster synchronization is of great importance for the normal functioning of numerous technological and natural systems. Deviations from normal cluster synchronization patterns are closely associated with various malfunctions, such as neurological disorders in the brain. Therefore, it is crucial to restore normal system functions by stabilizing the appropriate cluster synchronization patterns. Most existing studies focus on designing controllers based on state measurements to achieve system stabilization. However, in many real-world scenarios, measuring system states in real time, such as neuronal activity in the brain, poses significant challenges, rendering the stabilization of such systems difficult. To overcome this challenge, in this article, we employ an open-loop control strategy, \u0000<italic>vibrational control</i>\u0000, which does not require any state measurements. We establish some sufficient conditions under which vibrational inputs stabilize cluster synchronization. Further, we provide a tractable approach to design vibrational control. Finally, numerical experiments are conducted to demonstrate our theoretical findings.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"2 ","pages":"439-453"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10313029","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135559909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.1109/OJCSYS.2023.3329394
Abolfazl Lavaei;Mateo Perez;Milad Kazemi;Fabio Somenzi;Sadegh Soudjani;Ashutosh Trivedi;Majid Zamani
We propose a compositional approach to synthesize policies for networks of continuous-space stochastic control systems with unknown dynamics using model-free reinforcement learning (RL). The approach is based on implicitly abstracting each subsystem in the network with a finite Markov decision process with unknown transition probabilities, synthesizing a strategy for each abstract model in an assume-guarantee fashion using RL, and then mapping the results back over the original network with approximate optimality guarantees. We provide lower bounds on the satisfaction probability of the overall network based on those over individual subsystems. A key contribution is to leverage the convergence results for adversarial RL (minimax Q-learning) on finite stochastic arenas to provide control strategies maximizing the probability of satisfaction over the network of continuous-space systems. We consider finite-horizon properties expressed in the syntactically co-safe fragment of linear temporal logic. These properties can readily be converted into automata-based reward functions, providing scalar reward signals suitable for RL. Since such reward functions are often sparse, we supply a potential-based reward shaping technique to accelerate learning by producing dense rewards. The effectiveness of the proposed approaches is demonstrated via two physical benchmarks including regulation of a room temperature network and control of a road traffic network.
{"title":"Compositional Reinforcement Learning for Discrete-Time Stochastic Control Systems","authors":"Abolfazl Lavaei;Mateo Perez;Milad Kazemi;Fabio Somenzi;Sadegh Soudjani;Ashutosh Trivedi;Majid Zamani","doi":"10.1109/OJCSYS.2023.3329394","DOIUrl":"10.1109/OJCSYS.2023.3329394","url":null,"abstract":"We propose a compositional approach to synthesize policies for networks of continuous-space stochastic control systems with unknown dynamics using model-free reinforcement learning (RL). The approach is based on \u0000<italic>implicitly</i>\u0000 abstracting each subsystem in the network with a finite Markov decision process with \u0000<italic>unknown</i>\u0000 transition probabilities, synthesizing a strategy for each abstract model in an assume-guarantee fashion using RL, and then mapping the results back over the original network with \u0000<italic>approximate optimality</i>\u0000 guarantees. We provide lower bounds on the satisfaction probability of the overall network based on those over individual subsystems. A key contribution is to leverage the convergence results for adversarial RL (minimax Q-learning) on finite stochastic arenas to provide control strategies maximizing the probability of satisfaction over the network of continuous-space systems. We consider \u0000<italic>finite-horizon</i>\u0000 properties expressed in the syntactically co-safe fragment of linear temporal logic. These properties can readily be converted into automata-based reward functions, providing scalar reward signals suitable for RL. Since such reward functions are often sparse, we supply a potential-based \u0000<italic>reward shaping</i>\u0000 technique to accelerate learning by producing dense rewards. The effectiveness of the proposed approaches is demonstrated via two physical benchmarks including regulation of a room temperature network and control of a road traffic network.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"2 ","pages":"425-438"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10304199","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135362318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-30DOI: 10.1109/OJCSYS.2023.3328201
Matin Jafarian;David Chávez Huerta;Gianluca Villani;Anders Lansner;Karl H. Johansson
This article studies free recall, i.e., the reactivation of stored memory items, namely patterns , in any order, of a model of working memory. Our free recall model is based on a biologically plausible modular neural network composed of $H$ modules, namely hypercolumns , each of which is a bundle of $M$minicolumns . The coupling weights and constant bias values of the network are determined by a Hebbian plasticity rule. Using techniques from nonlinear stability theory, we show that cluster synchronization is the central mechanism governing free recall of orthogonally encoded patterns. Particularly, we show that free recall's cluster synchronization is the combination of two main mechanisms: simultaneous activities of minicolumns representing an encoded pattern, i.e., within-pattern synchronization, together with time-divided activities of minicolumns representing different patterns. We characterize the coupling and bias value conditions under which cluster synchronization emerges. We also discuss the role of heterogeneous coupling weights and bias values of minicolumns' dynamics in free recall. Specifically, we compare the behaviour of two $H times 2$ networks with identical and non-identical coupling weights and bias values. For these two networks, we obtain bounds on couplings and bias values under which both encoded patterns are recalled. Our analysis shows that having non-identical couplings and bias values for different patterns increases the possibility of their free recall. Numerical simulations are given to validate the theoretical analysis.
本文研究的是自由回忆，即重新激活工作记忆模型中存储的记忆项（即任意顺序的模式）。我们的自由回忆模型基于一个生物学上可信的模块化神经网络，该网络由 $H$ 模块（即超柱）组成，每个模块都是一束 $M$ 小柱。网络的耦合权重和恒定偏置值由海比可塑性规则决定。利用非线性稳定性理论的技术，我们证明了集群同步是支配正交编码模式自由回忆的核心机制。特别是，我们证明了自由回忆的集群同步是两种主要机制的结合：代表一种编码模式的小柱的同时活动（即模式内同步），以及代表不同模式的小柱的分时活动。我们描述了集群同步出现的耦合和偏置值条件。我们还讨论了小柱动态的异质耦合权重和偏置值在自由回忆中的作用。具体来说，我们比较了两个具有相同和非相同耦合权重和偏置值的 $H times 2$ 网络的行为。对于这两个网络，我们得到了耦合和偏置值的边界，在此边界下，两种编码模式都能被召回。我们的分析表明，不同模式的非相同耦合和偏置值增加了它们被自由调用的可能性。我们给出了数值模拟来验证理论分析。
{"title":"Cluster Synchronization as a Mechanism of Free Recall in Working Memory Networks","authors":"Matin Jafarian;David Chávez Huerta;Gianluca Villani;Anders Lansner;Karl H. Johansson","doi":"10.1109/OJCSYS.2023.3328201","DOIUrl":"10.1109/OJCSYS.2023.3328201","url":null,"abstract":"This article studies free recall, i.e., the reactivation of stored memory items, namely \u0000<italic>patterns</i>\u0000, in any order, of a model of working memory. Our free recall model is based on a biologically plausible modular neural network composed of \u0000<inline-formula><tex-math>$H$</tex-math></inline-formula>\u0000 modules, namely \u0000<italic>hypercolumns</i>\u0000, each of which is a bundle of \u0000<inline-formula><tex-math>$M$</tex-math></inline-formula>\u0000 \u0000<italic>minicolumns</i>\u0000. The coupling weights and constant bias values of the network are determined by a Hebbian plasticity rule. Using techniques from nonlinear stability theory, we show that cluster synchronization is the central mechanism governing free recall of orthogonally encoded patterns. Particularly, we show that free recall's cluster synchronization is the combination of two main mechanisms: simultaneous activities of minicolumns representing an encoded pattern, i.e., within-pattern synchronization, together with time-divided activities of minicolumns representing different patterns. We characterize the coupling and bias value conditions under which cluster synchronization emerges. We also discuss the role of heterogeneous coupling weights and bias values of minicolumns' dynamics in free recall. Specifically, we compare the behaviour of two \u0000<inline-formula><tex-math>$H times 2$</tex-math></inline-formula>\u0000 networks with identical and non-identical coupling weights and bias values. For these two networks, we obtain bounds on couplings and bias values under which both encoded patterns are recalled. Our analysis shows that having non-identical couplings and bias values for different patterns increases the possibility of their free recall. Numerical simulations are given to validate the theoretical analysis.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"2 ","pages":"454-463"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10301556","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135313278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-12DOI: 10.1109/OJCSYS.2023.3324274
Yu-Mei Huang;Arthur C. B. de Oliveira;Dinesh Murugan;Milad Siami
This article delves into the challenges of ensuring stability (in some sense) and robustness in large-scale second-order consensus networks (SOCNs) and autonomous vehicle platoons in the discrete-time domain. We propose a graph-theoretic methodology for designing a state feedback law for these systems in a discrete-time framework. By analyzing the behavior of the solutions of the networks based on the algebraic properties of the Laplacian matrices of the underlying graphs and on the value of the update cycle (also known as the time step) of each vehicle, we provide a necessary and sufficient condition for the stability of a linear second-order consensus network in the discrete-time domain. We then perform an $mathcal {H}_{2}$ -based robustness analysis to demonstrate the relationship between the $mathcal {H}_{2}$ -norm of the system, network size, connectivity, and update cycles, providing insights into how these factors impact the convergence and robustness of the system. A key contribution of this work is the development of a formal framework for understanding the link between an $mathcal {H}_{2}$ -based performance measure and the restrictions on the update cycle of the vehicles. Specifically, we show that denser networks (i.e., networks with more communication links) might require faster agents (i.e., smaller update cycles) to outperform or achieve the same level of robustness as sparse networks (i.e., networks with fewer communication links) - see Fig. 1. These findings have important implications for the design and implementation of large-scale consensus networks and autonomous vehicle platoons, highlighting the need for a balance between network density and update cycle speed for optimal performance. We finish the article with results from simulations and experiments that illustrate the effectiveness of the proposed framework in predicting the behavior of vehicle platoons, even for more complex agents with nonlinear dynamics, using Quanser's Qlabs and Qcars.
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