IEEE open journal of control systems最新文献

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"title":"Balancing Agility and Communication: Denser Networks Require Faster Agents","authors":"Yu-Mei Huang;Arthur C. B. de Oliveira;Dinesh Murugan;Milad Siami","doi":"10.1109/OJCSYS.2023.3324274","DOIUrl":"10.1109/OJCSYS.2023.3324274","url":null,"abstract":"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 \u0000<inline-formula><tex-math>$mathcal {H}_{2}$</tex-math></inline-formula>\u0000-based robustness analysis to demonstrate the relationship between the \u0000<inline-formula><tex-math>$mathcal {H}_{2}$</tex-math></inline-formula>\u0000-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 \u0000<inline-formula><tex-math>$mathcal {H}_{2}$</tex-math></inline-formula>\u0000-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.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"2 ","pages":"464-476"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10283896","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136304144","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}

{"title":"Training Reflexes Using Adaptive Feedforward Control","authors":"Erick Mejia Uzeda;Mireille E. Broucke","doi":"10.1109/OJCSYS.2023.3322906","DOIUrl":"https://doi.org/10.1109/OJCSYS.2023.3322906","url":null,"abstract":"We consider the problem of mixed feedforward and feedback based disturbance rejection, where the feedforward measurement only provides a partial reconstruction of the disturbance. In doing so, we pose a new biologically relevant disturbance rejection problem which puts the role of feedforward measurements at the forefront. Based on the architecture of the human brain, we propose a design that utilizes an adaptive internal model operating on a fast timescale that, in turn, trains the correct feedforward gains on a slow timescale. As such, the training of reflexes in biological systems can be explained by leveraging the theory of adaptive feedforward control. It is proven that our design provides an arbitrary level of disturbance attenuation, and the benefits of using reflexes are illustrated via a multitude of simulations.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"2 ","pages":"396-409"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552933/9973428/10274847.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50376167","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}