Pub Date : 2025-07-10DOI: 10.1109/OJCSYS.2025.3587537
Aleena Thomas;Abhijith Ajayakumar;Raju K. George
In this paper, controllability and observability of a heterogeneous networked system with Linear Time Invariant (LTI) nodal systems having Multiple-Inputs and Multiple-Outputs (MIMO) aligned in a weighted and directed network topology are studied. Apart from the heterogenity in nodal dynamics, the inner-coupling matrices that quantify the interactions among nodes are also different. In contrast to the existing literature, the system under consideration has distinct node dimensions, which adds to the generality. Necessary and sufficient conditions for controllability and observability as well as certain necessary conditions for controllability of a class of networked systems are established. These conditions show the dependence of network controllability and observability on various node and network-specific factors. As a practical application, a three-sector economy is modelled as a heterogeneous networked system with distinct node dimensions and its controllability is analysed. Computational time in floating point operations (flops) of the proposed methods are estimated, which indicates their efficiency on comparison with the classical conditions. This is illustrated by computational comparison of the existing and proposed schemes, applied to a randomly generated networked system. Also, robustness of the proposed schemes are analysed with the example of randomly generated networked systems. All the results are supported with illustrative numerical examples.
{"title":"Controllability and Observability of Heterogeneous Networked Systems With Non-Uniform Node Dimensions and Distinct Inner-Coupling Matrices","authors":"Aleena Thomas;Abhijith Ajayakumar;Raju K. George","doi":"10.1109/OJCSYS.2025.3587537","DOIUrl":"https://doi.org/10.1109/OJCSYS.2025.3587537","url":null,"abstract":"In this paper, controllability and observability of a heterogeneous networked system with Linear Time Invariant (LTI) nodal systems having Multiple-Inputs and Multiple-Outputs (MIMO) aligned in a weighted and directed network topology are studied. Apart from the heterogenity in nodal dynamics, the inner-coupling matrices that quantify the interactions among nodes are also different. In contrast to the existing literature, the system under consideration has distinct node dimensions, which adds to the generality. Necessary and sufficient conditions for controllability and observability as well as certain necessary conditions for controllability of a class of networked systems are established. These conditions show the dependence of network controllability and observability on various node and network-specific factors. As a practical application, a three-sector economy is modelled as a heterogeneous networked system with distinct node dimensions and its controllability is analysed. Computational time in floating point operations (flops) of the proposed methods are estimated, which indicates their efficiency on comparison with the classical conditions. This is illustrated by computational comparison of the existing and proposed schemes, applied to a randomly generated networked system. Also, robustness of the proposed schemes are analysed with the example of randomly generated networked systems. All the results are supported with illustrative numerical examples.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"4 ","pages":"219-235"},"PeriodicalIF":0.0,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11075535","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144831949","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 : 2025-07-02DOI: 10.1109/OJCSYS.2025.3585427
L. van de Kamp;B. Hunnekens;T. Oomen;N. van de Wouw
Safe deployment of neural networks to classify time series in safety-critical applications relies on the ability of the classifier to detect data that does not originate from the same distribution as the training data. The aim of this paper is to propose a framework for detecting whether time-series data is sampled from a different distribution than the training data, known as the problem of out-of-distribution (OOD) detection. We propose a novel distance-based OOD method for time-series data using a hierarchical clustering method together with dynamic time-warping to measure the difference between a new data instance and the training set. The method is evaluated in the context of mechanical ventilation, a safety critical application, using both simulated and clinical datasets. Results of the mechanical ventilation use case demonstrate that the proposed approach effectively detects out-of-distribution data and improves classification performance in diverse settings.
在安全关键应用中，安全部署神经网络对时间序列进行分类依赖于分类器检测与训练数据不同分布的数据的能力。本文的目的是提出一个框架，用于检测时间序列数据是否从不同于训练数据的分布中采样，称为out- distribution （OOD）检测问题。我们提出了一种新的基于距离的时间序列数据OOD方法，使用层次聚类方法和动态时间规整来度量新数据实例与训练集之间的差异。该方法在机械通气这一安全关键应用的背景下进行评估，使用模拟和临床数据集。机械通气用例的结果表明，该方法可以有效地检测出分布外数据，并提高了不同设置下的分类性能。
{"title":"Time-Series Out-of-Distribution Data Detection in Mechanical Ventilation","authors":"L. van de Kamp;B. Hunnekens;T. Oomen;N. van de Wouw","doi":"10.1109/OJCSYS.2025.3585427","DOIUrl":"https://doi.org/10.1109/OJCSYS.2025.3585427","url":null,"abstract":"Safe deployment of neural networks to classify time series in safety-critical applications relies on the ability of the classifier to detect data that does not originate from the same distribution as the training data. The aim of this paper is to propose a framework for detecting whether time-series data is sampled from a different distribution than the training data, known as the problem of <italic>out-of-distribution</i> (OOD) detection. We propose a novel distance-based OOD method for time-series data using a hierarchical clustering method together with dynamic time-warping to measure the difference between a new data instance and the training set. The method is evaluated in the context of mechanical ventilation, a safety critical application, using both simulated and clinical datasets. Results of the mechanical ventilation use case demonstrate that the proposed approach effectively detects out-of-distribution data and improves classification performance in diverse settings.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"4 ","pages":"236-249"},"PeriodicalIF":0.0,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11066264","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144914214","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}
The stochastic linear bandit problem has emerged as a fundamental building-block in machine learning and control, and a realistic model for many applications. By equipping this classical problem with safety constraints, the safe linear bandit problem further broadens its relevance to safety-critical applications. However, most existing algorithms for safe linear bandits only consider linear constraints, making them inadequate for many real-world applications, which often have non-linear constraints. To alleviate this limitation, we study the problem of safe linear bandits under general (non-linear) constraints. Under a novel constraint regularity condition that is weaker than convexity, we give two algorithms with $tilde{mathcal {O}}(d sqrt{T})$ regret. We then give efficient implementations of these algorithms for several specific settings. Lastly, we give simulation results demonstrating the effectiveness of our algorithms in choosing dynamic pricing signals for a demand response problem under distribution power flow constraints.
{"title":"Optimistic Algorithms for Safe Linear Bandits Under General Constraints","authors":"Spencer Hutchinson;Arghavan Zibaie;Ramtin Pedarsani;Mahnoosh Alizadeh","doi":"10.1109/OJCSYS.2025.3558118","DOIUrl":"https://doi.org/10.1109/OJCSYS.2025.3558118","url":null,"abstract":"The stochastic linear bandit problem has emerged as a fundamental building-block in machine learning and control, and a realistic model for many applications. By equipping this classical problem with safety constraints, the <italic>safe linear bandit problem</i> further broadens its relevance to safety-critical applications. However, most existing algorithms for safe linear bandits only consider <italic>linear constraints</i>, making them inadequate for many real-world applications, which often have non-linear constraints. To alleviate this limitation, we study the problem of safe linear bandits under general (non-linear) constraints. Under a novel constraint regularity condition that is weaker than convexity, we give two algorithms with <inline-formula><tex-math>$tilde{mathcal {O}}(d sqrt{T})$</tex-math></inline-formula> regret. We then give efficient implementations of these algorithms for several specific settings. Lastly, we give simulation results demonstrating the effectiveness of our algorithms in choosing dynamic pricing signals for a demand response problem under distribution power flow constraints.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"4 ","pages":"103-116"},"PeriodicalIF":0.0,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10950393","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896165","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 : 2025-03-30DOI: 10.1109/OJCSYS.2025.3575305
Bo Chen;Baike She;Calvin Hawkins;Philip E. Paré;Matthew T. Hale
Reproduction numbers are widely used to analyze epidemic spreading processes over networks. However,conventional reproduction numbers of an overall network, which require spreading information from the entire network, do not indicate where an epidemic is spreading. To address this limitation, we first propose a novel class of local distributed reproduction numbers that capture spreading behaviors at the level of individual nodes. We demonstrate how to compute these values in a distributed way and use them to derive new threshold conditions for network spreading analysis. Due to the fact that epidemic data are often collected at multiple geographic or administrative scales, we then define a class of cluster distributed reproduction numbers to describe the spread between groups of nodes such as communities, cities, or states. We further show that the local distributed reproduction numbers can be aggregated to form the cluster distributed reproduction numbers. Unlike conventional network-level reproduction numbers, these distributed measures reveal fine-grained interaction patterns that may raise privacy concerns by exposing the frequency or intensity of interactions across regions. To address this issue, we propose a privacy-enhanced distributed reproduction number framework that implements differential privacy. This framework enables scalable and privacy-preserving analysis of epidemic spreading processes in networked populations through the calculation of privacy-preserving distributed reproduction numbers. Numerical experiments show that while maintaining differential privacy, the private distributed reproduction numbers yield accurate estimates of epidemic spread while also offering more insights than conventional reproduction numbers.
{"title":"Scalable Distributed Reproduction Numbers of Network Epidemics With Differential Privacy","authors":"Bo Chen;Baike She;Calvin Hawkins;Philip E. Paré;Matthew T. Hale","doi":"10.1109/OJCSYS.2025.3575305","DOIUrl":"https://doi.org/10.1109/OJCSYS.2025.3575305","url":null,"abstract":"Reproduction numbers are widely used to analyze epidemic spreading processes over networks. However,conventional reproduction numbers of an overall network, which require spreading information from the entire network, do not indicate where an epidemic is spreading. To address this limitation, we first propose a novel class of local distributed reproduction numbers that capture spreading behaviors at the level of individual nodes. We demonstrate how to compute these values in a distributed way and use them to derive new threshold conditions for network spreading analysis. Due to the fact that epidemic data are often collected at multiple geographic or administrative scales, we then define a class of cluster distributed reproduction numbers to describe the spread between groups of nodes such as communities, cities, or states. We further show that the local distributed reproduction numbers can be aggregated to form the cluster distributed reproduction numbers. Unlike conventional network-level reproduction numbers, these distributed measures reveal fine-grained interaction patterns that may raise privacy concerns by exposing the frequency or intensity of interactions across regions. To address this issue, we propose a privacy-enhanced distributed reproduction number framework that implements differential privacy. This framework enables scalable and privacy-preserving analysis of epidemic spreading processes in networked populations through the calculation of privacy-preserving distributed reproduction numbers. Numerical experiments show that while maintaining differential privacy, the private distributed reproduction numbers yield accurate estimates of epidemic spread while also offering more insights than conventional reproduction numbers.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"4 ","pages":"199-218"},"PeriodicalIF":0.0,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11018355","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524373","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 : 2025-03-22DOI: 10.1109/OJCSYS.2025.3572375
Josh Netter;Kyriakos G. Vamvoudakis;Timothy F. Walsh;Jaideep Ray
In this paper, we propose a learning-based method utilizing the Soft Actor-Critic (SAC) algorithm to train a binary Support Vector Machine (SVM) classifier. This classifier is designed to identify valid input spaces in high-dimensional, highly constrained systems while minimizing the total runtime of offline simulations. The simulations adapt their runtime based on the likelihood that a given training input will be informative to the classifier. Furthermore, we introduce a method for using the trained SAC model to predict whether a desired system input is likely to violate constraints, along with a technique to adjust the input as necessary. Additionally, we explore the potential of this model to detect faults or adversarial attacks within the system. The effectiveness of our approach is demonstrated through various simulations of challenging classification problems and a constrained quadrotor model.
{"title":"Safe and Robust Binary Classification and Fault Detection Using Reinforcement Learning","authors":"Josh Netter;Kyriakos G. Vamvoudakis;Timothy F. Walsh;Jaideep Ray","doi":"10.1109/OJCSYS.2025.3572375","DOIUrl":"https://doi.org/10.1109/OJCSYS.2025.3572375","url":null,"abstract":"In this paper, we propose a learning-based method utilizing the Soft Actor-Critic (SAC) algorithm to train a binary Support Vector Machine (SVM) classifier. This classifier is designed to identify valid input spaces in high-dimensional, highly constrained systems while minimizing the total runtime of offline simulations. The simulations adapt their runtime based on the likelihood that a given training input will be informative to the classifier. Furthermore, we introduce a method for using the trained SAC model to predict whether a desired system input is likely to violate constraints, along with a technique to adjust the input as necessary. Additionally, we explore the potential of this model to detect faults or adversarial attacks within the system. The effectiveness of our approach is demonstrated through various simulations of challenging classification problems and a constrained quadrotor model.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"4 ","pages":"172-186"},"PeriodicalIF":0.0,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11010134","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144481878","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 : 2025-03-18DOI: 10.1109/OJCSYS.2025.3552999
Ashwin P. Dani;Shubhendu Bhasin
In this paper, a continuous-time adaptive actor-critic reinforcement learning (RL) controller is developed for drift-free uncertain nonlinear systems. Practical examples of such systems are image-based visual servoing (IBVS) and wheeled mobile robots (WMR), where the system dynamics include a parametric uncertainty in the control effectiveness matrix with no drift term. The uncertainty in the input term poses a challenge when developing a continuous-time RL controller using existing methods. This paper presents an actor-critic/synchronous policy iteration (PI)-based RL controller with a newly derived constrained concurrent learning (CCL)-based parameter update law for estimating the unknown parameters of the linearly parametrized control effectiveness matrix. The parameter update law ensures that the parameters do not converge to $zero$, avoiding possible loss of stabilization. An infinite-horizon value function minimization objective is achieved by regulating the current states to the desired with near-optimal control efforts. The proposed controller guarantees closed-loop stability, and simulation results in the presence of noise validate the proposed theory using IBVS and WMR examples.
{"title":"Adaptive Actor-Critic Based Optimal Regulation for Drift-Free Nonlinear Systems","authors":"Ashwin P. Dani;Shubhendu Bhasin","doi":"10.1109/OJCSYS.2025.3552999","DOIUrl":"https://doi.org/10.1109/OJCSYS.2025.3552999","url":null,"abstract":"In this paper, a continuous-time adaptive actor-critic reinforcement learning (RL) controller is developed for drift-free uncertain nonlinear systems. Practical examples of such systems are image-based visual servoing (IBVS) and wheeled mobile robots (WMR), where the system dynamics include a parametric uncertainty in the control effectiveness matrix with no drift term. The uncertainty in the input term poses a challenge when developing a continuous-time RL controller using existing methods. This paper presents an actor-critic/synchronous policy iteration (PI)-based RL controller with a newly derived constrained concurrent learning (CCL)-based parameter update law for estimating the unknown parameters of the linearly parametrized control effectiveness matrix. The parameter update law ensures that the parameters do not converge to <inline-formula><tex-math>$zero$</tex-math></inline-formula>, avoiding possible loss of stabilization. An infinite-horizon value function minimization objective is achieved by regulating the current states to the desired with near-optimal control efforts. The proposed controller guarantees closed-loop stability, and simulation results in the presence of noise validate the proposed theory using IBVS and WMR examples.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"4 ","pages":"117-129"},"PeriodicalIF":0.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10932715","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072925","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 : 2025-03-15DOI: 10.1109/OJCSYS.2025.3570578
Nariman Niknejad;Farnaz Adib Yaghmaie;Hamidreza Modares
This paper presents online prior-knowledge-based data-driven approaches for verifying stability and learning a stabilizing dynamic controller for linear stochastic input-output systems. The system is modeled in an autoregressive exogenous (ARX) framework to accommodate cases where states are not fully observable. A key challenge addressed in this article is online stabilizing open-loop unstable systems, where collecting sufficient data for controller learning is impractical due to the risk of failure. To mitigate this, the proposed method integrates uncertain prior knowledge, derived from system physics, with limited available data. Inspired by set-membership system identification, the prior knowledge set is dynamically updated as new data becomes available, reducing conservatism over time. Unlike traditional approaches, this method bypasses explicit system identification, directly designing controllers based on current knowledge and data. A connection between ARX models and behavior theory is established, providing necessary and sufficient stability conditions using strict lossy S-Lemma. Quadratic difference forms serve as a framework for Lyapunov functions, and robust dynamic controllers are synthesized via linear matrix inequalities. The methodology is validated through simulations, including an unstable scalar system visualizing the integration of prior knowledge and data, and a rotary inverted pendulum demonstrating controller effectiveness in a nonlinear, unstable setting.
{"title":"Online Learning of Stabilizing Controllers Using Noisy Input-Output Data and Prior Knowledge","authors":"Nariman Niknejad;Farnaz Adib Yaghmaie;Hamidreza Modares","doi":"10.1109/OJCSYS.2025.3570578","DOIUrl":"https://doi.org/10.1109/OJCSYS.2025.3570578","url":null,"abstract":"This paper presents online prior-knowledge-based data-driven approaches for verifying stability and learning a stabilizing dynamic controller for linear stochastic input-output systems. The system is modeled in an autoregressive exogenous (ARX) framework to accommodate cases where states are not fully observable. A key challenge addressed in this article is online stabilizing open-loop unstable systems, where collecting sufficient data for controller learning is impractical due to the risk of failure. To mitigate this, the proposed method integrates uncertain prior knowledge, derived from system physics, with limited available data. Inspired by set-membership system identification, the prior knowledge set is dynamically updated as new data becomes available, reducing conservatism over time. Unlike traditional approaches, this method bypasses explicit system identification, directly designing controllers based on current knowledge and data. A connection between ARX models and behavior theory is established, providing necessary and sufficient stability conditions using strict lossy <italic>S</i>-Lemma. Quadratic difference forms serve as a framework for Lyapunov functions, and robust dynamic controllers are synthesized via linear matrix inequalities. The methodology is validated through simulations, including an unstable scalar system visualizing the integration of prior knowledge and data, and a rotary inverted pendulum demonstrating controller effectiveness in a nonlinear, unstable setting.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"4 ","pages":"156-171"},"PeriodicalIF":0.0,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11005422","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144323194","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 : 2025-03-07DOI: 10.1109/OJCSYS.2025.3567867
T. Shinohara;T. Namerikawa
The state estimation problem in the presence of malicious sensor attacks is commonly referred to as a secure state estimation problem. Central to addressing this problem is the concept of the sparse observability index, defined as the largest integer $ delta$ for which the system remains observable after the removal of any $delta$ sensors. This index plays a critical role in quantifying the resilience of the system, as a higher $delta$ enables unique state reconstruction despite the presence of more compromised sensors. In this study, for undirected multi-agent systems consisting of $ n$ agents, we analyze the relationship between the number of agents $ n$ and the sparse observability index $ delta$ for effective secure state estimation. In particular, we consider four typical graph structures: path, cycle, complete, and complete bipartite graphs. Our analysis reveals that $delta$ does not increase monotonically with $n$, and that resilience is intricately tied to the underlying network structure. Notably, we demonstrate that the system exhibits enhanced resilience when the number of agents $n$ is a prime number, although the specifics of this relationship vary depending on the graph topology.
{"title":"Relationship Between the Number of Agents and Sparse Observability Index","authors":"T. Shinohara;T. Namerikawa","doi":"10.1109/OJCSYS.2025.3567867","DOIUrl":"https://doi.org/10.1109/OJCSYS.2025.3567867","url":null,"abstract":"The state estimation problem in the presence of malicious sensor attacks is commonly referred to as a secure state estimation problem. Central to addressing this problem is the concept of the sparse observability index, defined as the largest integer <inline-formula><tex-math>$ delta$</tex-math></inline-formula> for which the system remains observable after the removal of any <inline-formula><tex-math>$delta$</tex-math></inline-formula> sensors. This index plays a critical role in quantifying the resilience of the system, as a higher <inline-formula><tex-math>$delta$</tex-math></inline-formula> enables unique state reconstruction despite the presence of more compromised sensors. In this study, for undirected multi-agent systems consisting of <inline-formula><tex-math>$ n$</tex-math></inline-formula> agents, we analyze the relationship between the number of agents <inline-formula><tex-math>$ n$</tex-math></inline-formula> and the sparse observability index <inline-formula><tex-math>$ delta$</tex-math></inline-formula> for effective secure state estimation. In particular, we consider four typical graph structures: path, cycle, complete, and complete bipartite graphs. Our analysis reveals that <inline-formula><tex-math>$delta$</tex-math></inline-formula> does not increase monotonically with <inline-formula><tex-math>$n$</tex-math></inline-formula>, and that resilience is intricately tied to the underlying network structure. Notably, we demonstrate that the system exhibits enhanced resilience when the number of agents <inline-formula><tex-math>$n$</tex-math></inline-formula> is a prime number, although the specifics of this relationship vary depending on the graph topology.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"4 ","pages":"144-155"},"PeriodicalIF":0.0,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10989748","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144170955","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 : 2025-03-01DOI: 10.1109/OJCSYS.2025.3566323
Liangshun Wu;Junsuo Qu
This paper presents an uncertainty-aware optimization method for high-precision servo control in automotive dosing cylinder gluing. A comprehensive system model captures the interdependent dynamics of mechanical, hydraulic, and servo motor subsystems, formulating the control problem as a Markov Decision Process (MDP). Using Deep Deterministic Policy Gradient (DDPG) reinforcement learning with Model Predictive Control (MPC), the approach combines MPC's optimization capabilities with DDPG's adaptive learning, improving resilience to uncertainties. The DDPG Actor refines the MPC baseline, while uncertainty analysis in the MPC objective anticipates future variations. The Critic evaluates Q-values with uncertainty feedback. Simulations and real-world tests confirm the method's stability, precision, and reliability for high-precision industrial gluing.
{"title":"Precision Cylinder Gluing With Uncertainty-Aware MPC-Enhanced DDPG","authors":"Liangshun Wu;Junsuo Qu","doi":"10.1109/OJCSYS.2025.3566323","DOIUrl":"https://doi.org/10.1109/OJCSYS.2025.3566323","url":null,"abstract":"This paper presents an uncertainty-aware optimization method for high-precision servo control in automotive dosing cylinder gluing. A comprehensive system model captures the interdependent dynamics of mechanical, hydraulic, and servo motor subsystems, formulating the control problem as a Markov Decision Process (MDP). Using Deep Deterministic Policy Gradient (DDPG) reinforcement learning with Model Predictive Control (MPC), the approach combines MPC's optimization capabilities with DDPG's adaptive learning, improving resilience to uncertainties. The DDPG Actor refines the MPC baseline, while uncertainty analysis in the MPC objective anticipates future variations. The Critic evaluates Q-values with uncertainty feedback. Simulations and real-world tests confirm the method's stability, precision, and reliability for high-precision industrial gluing.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"4 ","pages":"130-143"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10981597","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144148132","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 : 2025-02-04DOI: 10.1109/OJCSYS.2025.3538267
Shiping Shao;Abhishek Gupta
We consider risk-sensitive Markov decision processes (MDPs), where the MDP model is influenced by a parameter which takes values in a compact metric space. These situations arise when the underlying dynamics of the system depend on parameters that drifts over time. For example, mass of a vehicle depends on the number of passengers in the vehicle, which may change from one trip to another. Similarly, the energy demand of a building depends on the local weather, which changes every hour of the day. We identify sufficient conditions under which small perturbations in the model parameters lead to small changes in the optimal value function and optimal policy. This is achieved by establishing the continuity of the value function with respect to the parameters. A direct consequence of this result is that an optimal policy under a specific parameter remains near-optimal if the parameter is perturbed slightly. Implications of the results for data-driven decision-making, decision-making with preference uncertainty, and systems with changing noise distributions are discussed.
{"title":"Robustness to Modeling Errors in Risk-Sensitive Markov Decision Problems With Markov Risk Measures","authors":"Shiping Shao;Abhishek Gupta","doi":"10.1109/OJCSYS.2025.3538267","DOIUrl":"https://doi.org/10.1109/OJCSYS.2025.3538267","url":null,"abstract":"We consider risk-sensitive Markov decision processes (MDPs), where the MDP model is influenced by a parameter which takes values in a compact metric space. These situations arise when the underlying dynamics of the system depend on parameters that drifts over time. For example, mass of a vehicle depends on the number of passengers in the vehicle, which may change from one trip to another. Similarly, the energy demand of a building depends on the local weather, which changes every hour of the day. We identify sufficient conditions under which small perturbations in the model parameters lead to small changes in the optimal value function and optimal policy. This is achieved by establishing the continuity of the value function with respect to the parameters. A direct consequence of this result is that an optimal policy under a specific parameter remains near-optimal if the parameter is perturbed slightly. Implications of the results for data-driven decision-making, decision-making with preference uncertainty, and systems with changing noise distributions are discussed.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"4 ","pages":"70-82"},"PeriodicalIF":0.0,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10872799","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570694","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}
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