IEEE open journal of control systems最新文献_第2页

Tempering the Bayes Filter Towards Improved Model-Based Estimation 基于改进模型估计的贝叶斯滤波

IEEE open journal of control systems Pub Date : 2026-01-01 Epub Date: 2026-03-18 DOI: 10.1109/OJCSYS.2026.3675495

Menno J. T. C. van Zutphen;Domagoj Herceg;Giannis Delimpaltadakis;Duarte J. Antunes

{"title":"Tempering the Bayes Filter Towards Improved Model-Based Estimation","authors":"Menno J. T. C. van Zutphen;Domagoj Herceg;Giannis Delimpaltadakis;Duarte J. Antunes","doi":"10.1109/OJCSYS.2026.3675495","DOIUrl":"https://doi.org/10.1109/OJCSYS.2026.3675495","url":null,"abstract":"Model-based filtering is often carried out while subject to an imperfect model, as learning partially-observable stochastic systems remains a challenge. Recent work on Bayesian inference found that tempering the likelihood or full posterior of an imperfect model can improve predictive accuracy, as measured by expected negative log likelihood. In this paper, we develop the discrete-time tempered Bayes filter, improving performance of the belief-distribution estimate through both of the aforementioned, and one newly introduced, tempering modalities. The result admits a recursive implementation with a computational complexity no higher than that of the original Bayes filter. Our analysis reveals that — besides the well-known fact that likelihood tempering affects the balance between prior and likelihood — full-posterior tempering tunes the belief-distribution entropy level. We further find that a region of the tempering space can be understood as interpolating between the Bayes- and MAP filters, recovering these as special cases. Analytical results further establish conditions under which a tempered Bayes filter improves predictive performance. Specializing the results to the linear Gaussian case, we obtain the tempered Kalman filter. In this context, we interpret how the parameters affect the Kalman state estimate and covariance. Empirical results confirm that our method consistently improves predictive accuracy over the Bayes filter baseline.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"5 ","pages":"149-163"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11442284","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147606328","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}

引用次数: 0

Integral Input Shaper Designed With Impulse Vectors for Removing Residual Vibration 基于脉冲矢量的积分输入整形器去除残余振动

IF 2.1

IEEE open journal of control systems Pub Date : 2026-01-01 Epub Date: 2026-08-26 DOI: 10.1109/OJCSYS.2026.3727927

Sang-Gyu Lee

{"title":"Integral Input Shaper Designed With Impulse Vectors for Removing Residual Vibration","authors":"Sang-Gyu Lee","doi":"10.1109/OJCSYS.2026.3727927","DOIUrl":"https://doi.org/10.1109/OJCSYS.2026.3727927","url":null,"abstract":"This paper proposes an integral input shaper (IIS) for suppressing residual vibration in second-order systems. The proposed shaper is derived by extending the impulse vector representation from a finite discrete impulse sequence to a continuous integral form. In this formulation, equally spaced impulse vectors with identical magnitude are extended over one full rotation, resulting in a continuous form input shaper that can be interpreted as an extension of the zero vibration (ZV) shaper. The general form of the IIS, including the effect of damping, is derived, and an approximate form for the near-zero damping case is also presented. To evaluate robustness against natural frequency mismatch, the sensitivity curve of the IIS is obtained and compared with those of the ZV and zero vibration derivative (ZVD) shapers. The results show that the IIS provides an alternative robustness profile with a sensitivity curve structure different from those of conventional discrete input shapers. In addition, the time domain characteristics of the IIS are analyzed for commonly used command inputs. The IIS transforms step, ramp, and periodic commands into shaped inputs that can be expressed in intuitive functional forms, enabling interpretation of the corresponding system responses. Experimental results under selected ramp and periodic input conditions showed qualitative trends consistent with the simulation results, providing representative validation of the proposed IIS under the tested conditions.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"5 ","pages":"453-464"},"PeriodicalIF":2.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11667278","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148985837","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}

引用次数: 0

Lyapunov-Based Iterative Learning of Regions of Attraction for Autonomous Systems 基于lyapunov的自治系统吸引域迭代学习

IF 2.1

IEEE open journal of control systems Pub Date : 2026-01-01 Epub Date: 2026-03-08 DOI: 10.1109/OJCSYS.2026.3691579

Guido Cavraro;Yue Chen;Andrey Bernstein

引用次数: 0

Mamba Sequence Modeling Meets Model Predictive Control 曼巴序列建模满足模型预测控制

IF 2.1

IEEE open journal of control systems Pub Date : 2026-01-01 Epub Date: 2026-08-18 DOI: 10.1109/OJCSYS.2026.3724785

M. Cevaal;T.O. de Jong;M. Lazar

{"title":"Mamba Sequence Modeling Meets Model Predictive Control","authors":"M. Cevaal;T.O. de Jong;M. Lazar","doi":"10.1109/OJCSYS.2026.3724785","DOIUrl":"https://doi.org/10.1109/OJCSYS.2026.3724785","url":null,"abstract":"In this paper, we consider the design of Model Predictive Control (MPC) algorithms based on Mamba neural networks. Mamba is a neural network architecture capable of sub-quadratic computational scaling in sequence length with state-of-the-art modeling capabilities. We provide a consistent and complete mathematical description of the Mamba neural network. Then, adjustments and optimizations are made to construct a decoder-only Mamba multi-step predictor for MPC and an input-output formulation is given for sequence-to-sequence modeling of dynamical systems. The performance of Mamba-MPC is evaluated on several numerical examples and compared to a Long-Short-Term-Memory-Based MPC (LSTM-MPC) and a Transformer based MPC (Transformer-MPC) for computational scaling comparison. First, a Single-Input-Single-Output (SISO) Van der Pol oscillator is considered, where stability, reference tracking, and noise robustness are evaluated. Then, a MIMO Four Tank setup is introduced where Multiple-Input-Multiple-Output (MIMO) reference tracking is evaluated. Lastly, Mamba-MPC is implemented on a physical Quanser Aero2 setup for closed-loop reference tracking. The results demonstrate that Mamba-MPC is able to stabilize and track a reference for SISO and MIMO systems, both in simulation and on a physical setup. Moreover, Mamba-MPC consistently outperforms LSTM-MPC in predictive control and is significantly computationally faster.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"5 ","pages":"511-525"},"PeriodicalIF":2.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11657796","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149131727","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}

引用次数: 0

Health-Aware Economic Model Predictive Control of Wave Energy Converters Based on a Reliability Metric 基于可靠性度量的波浪能变流器健康经济模型预测控制

IEEE open journal of control systems Pub Date : 2026-01-01 Epub Date: 2026-04-24 DOI: 10.1109/OJCSYS.2026.3685925

Amin Ziaei;Hafiz Ahsan Said;John V. Ringwood

{"title":"Health-Aware Economic Model Predictive Control of Wave Energy Converters Based on a Reliability Metric","authors":"Amin Ziaei;Hafiz Ahsan Said;John V. Ringwood","doi":"10.1109/OJCSYS.2026.3685925","DOIUrl":"https://doi.org/10.1109/OJCSYS.2026.3685925","url":null,"abstract":"Conventionalenergy-maximising controllers for wave energy converters (WECs) often exaggerate device motion, which can shorten device lifetime and increase operational costs (OpEx), by reducing maintenance intervals and system reliability. This paper proposes a novel health-aware WEC controller to achieve a suitable trade-off between captured energy and device lifetime, ultimately leading to a lower levelised cost of energy (LCoE). Using a reliability metric, the proposed controller incorporates power take-off degradation into the performance function of an economic model predictive controller (EMPC). The proposed performance function does not require additional (regularisation) terms to convexify the optimisation problem, as is often necessary for typical energy-maximising WEC EMPCs. Simulation results demonstrate the effectiveness of the proposed controller in achieving an optimal trade-off between captured energy and device longevity. Furthermore, this study examines potential challenges in the implementation of health-aware control for WECs and outlines key directions for future research in the health-aware WEC control domain.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"5 ","pages":"227-242"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11494242","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147828934","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}

引用次数: 0

Differentially Private Reward Functions in Markov Decision Processes: Policy Synthesis and Tradeoffs 马尔可夫决策过程中的差分私有奖励函数：策略综合与权衡

IEEE open journal of control systems Pub Date : 2026-01-01 Epub Date: 2026-04-23 DOI: 10.1109/OJCSYS.2026.3687085

Alexander Benvenuti;Calvin Hawkins;Brandon Fallin;Bo Chen;Brendan Bialy;Miriam Dennis;Matthew T. Hale

{"title":"Differentially Private Reward Functions in Markov Decision Processes: Policy Synthesis and Tradeoffs","authors":"Alexander Benvenuti;Calvin Hawkins;Brandon Fallin;Bo Chen;Brendan Bialy;Miriam Dennis;Matthew T. Hale","doi":"10.1109/OJCSYS.2026.3687085","DOIUrl":"https://doi.org/10.1109/OJCSYS.2026.3687085","url":null,"abstract":"Policy synthesis in Markov decision processes uses a known reward function to compute a policy that maximizes it. However, onlookers may infer reward functions by observing agents, which can reveal sensitive information. Therefore, in this paper we introduce and compare two methods for privatizing reward functions in policy synthesis for multi-agent Markov decision processes, which generalize Markov decision processes. Reward functions are privatized using differential privacy, a statistical framework for protecting sensitive data. The methods we develop perturb (i) each agent's individual reward function or (ii) the joint reward function shared by all agents. We prove that both of these methods are differentially private and show approach (i) provides better performance. We then develop guidelines for selecting reward functions to achieve high performance while remaining private, and we quantify the increase in computation required to compute policies with privatized rewards. Numerical simulations are performed on several examples, and these examples show that even relatively strong differential privacy (<inline-formula><tex-math>$epsilon =1.3$</tex-math></inline-formula>) induces as little as a 5% decrease in performance and 0.016% increase in computation time.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"5 ","pages":"243-257"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11493734","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147828952","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}

引用次数: 0

Optimal Control of Entity-Based Systems via Koopman Representations With Product Density Observables 基于产品密度可观测值的Koopman表示的实体系统最优控制

IEEE open journal of control systems Pub Date : 2026-01-01 Epub Date: 2026-03-18 DOI: 10.1109/OJCSYS.2026.3675406

Madeline Blischke;João P. Hespanha

{"title":"Optimal Control of Entity-Based Systems via Koopman Representations With Product Density Observables","authors":"Madeline Blischke;João P. Hespanha","doi":"10.1109/OJCSYS.2026.3675406","DOIUrl":"https://doi.org/10.1109/OJCSYS.2026.3675406","url":null,"abstract":"The Koopman operator, which describes a dynamical system via a linear representation that can be approximately learned from data, allows application of linear control techniques to nonlinear systems. This work considers a nonlinear optimal control problem with final state constraints, which we alternatively represent by a linear problem on a lifted state space described by the Koopman operator. We show that the optimal cost-to-go to this linear problem has a piecewise linear form with respect to the lifted state. A robust formulation, in which we minimize the worst-case cost with respect to bounded errors in the initial state and Koopman dynamics, retains a piecewise affine structure. Due to the combinatorial nature of the problem, the number of possible input sequences grows exponentially in the horizon, and so we provide a heuristic pruning algorithm that reduces the search space to a much smaller subset. Our control approach is generally applicable, but its effectiveness depends on a domain-specific choice of lifting functions, termed <italic>observables</i>. For a class of systems with dynamics described by some number of distinct “entities”, we define observables that describe “densities” of these entities, as well as products of densities that capture a richer class of interactions between entities.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"5 ","pages":"164-178"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11442653","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147665516","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}

引用次数: 0

Learning to Pursue AC Optimal Power Flow Solutions With Feasibility Guarantees 学习追求具有可行性保证的交流最优潮流解决方案

IEEE open journal of control systems Pub Date : 2026-01-01 Epub Date: 2026-04-01 DOI: 10.1109/OJCSYS.2026.3679925

Damola Ajeyemi;Yiting Chen;Antonin Colot;Jorge Cortés;Emiliano Dall’Anese

{"title":"Learning to Pursue AC Optimal Power Flow Solutions With Feasibility Guarantees","authors":"Damola Ajeyemi;Yiting Chen;Antonin Colot;Jorge Cortés;Emiliano Dall’Anese","doi":"10.1109/OJCSYS.2026.3679925","DOIUrl":"https://doi.org/10.1109/OJCSYS.2026.3679925","url":null,"abstract":"This paper focuses on an AC optimal power flow (OPF) problem for distribution feeders equipped with controllable distributed energy resources (DERs). We consider a solution method that is based on a continuous approximation of the projected gradient flow – referred to as the safe gradient flow – that incorporates voltage and current information obtained either through real-time measurements or power flow computations. These two setups enable both feedback-based online and offline implementations. The safe gradient flow involves the solution of convex quadratic programs (QPs). To enhance computational efficiency, we propose a novel framework that employs a neural network approximation of the optimal solution map of the QP. The resulting method has two key features: (a) it ensures that the DERs’ setpoints are practically feasible, even for an online implementation or when an offline algorithm has an early termination; (b) it ensures convergence to a neighborhood of a strict local optimizer of the AC OPF. The proposed method is tested on a 93-node distribution system with realistic loads and renewable generation. The test shows that our method successfully regulates voltages within limits during periods with high renewable generation.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"5 ","pages":"211-226"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11460216","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147737198","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}

引用次数: 0

Reducing Annotation Effort in Patient-Ventilator Asynchrony Detection With Distance-Based Clustering 基于距离聚类减少患者-呼吸机异步检测的标注工作量

IEEE open journal of control systems Pub Date : 2026-01-01 Epub Date: 2026-04-17 DOI: 10.1109/OJCSYS.2026.3685094

Lars van de Kamp;Dolf Weller;Rick Thijssen;Bram Hunnekens;Tom Bakkes;Simona Turco;Corstiaan den Uil;Tom Oomen;Nathan van de Wouw

{"title":"Reducing Annotation Effort in Patient-Ventilator Asynchrony Detection With Distance-Based Clustering","authors":"Lars van de Kamp;Dolf Weller;Rick Thijssen;Bram Hunnekens;Tom Bakkes;Simona Turco;Corstiaan den Uil;Tom Oomen;Nathan van de Wouw","doi":"10.1109/OJCSYS.2026.3685094","DOIUrl":"https://doi.org/10.1109/OJCSYS.2026.3685094","url":null,"abstract":"Objective: This study aims to reduce expert annotation effort in detecting patient–ventilator asynchrony (PVA) by introducing a semi-supervised learning framework for time series classification. Methods and procedures: We propose a model-independent framework that integrates hierarchical clustering and dynamic time-warping (DTW) for efficient data selection and label projection. The framework includes five steps: data collection, selection, annotation, projection, and model training. It is validated using a fully labeled dataset from Fondazione I.R.C.C.S. Policlinico San Matteo and applied to an unlabeled dataset from Maasstad Hospital, where annotation consistency and label quality are analyzed. Results: The framework reduces annotation effort by over 75% while closely resembling classification performance. On the San Matteo dataset, the model trained with projected labels achieved performance close to that of a fully supervised model. The method effectively captured rare PVA types and improved macro-averaged F1 scores compared to random sampling. On the Maasstad dataset, despite annotation inconsistencies, the framework demonstrated moderate detection performance (75% micro-averaged <inline-formula><tex-math>$F_{1}$</tex-math></inline-formula> score) using labels from a single clinical expert. Conclusion: Our semi-supervised framework enables scalable and efficient annotation of clinical time series data, maintaining model accuracy with minimal expert input. It is robust across datasets and adaptable to varying signal quality and annotation consistency.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"5 ","pages":"289-302"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11482846","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148003677","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}

引用次数: 0

Robustness Certificates for Neural Networks Against Data Poisoning and Evasion Attacks 神经网络抗数据中毒和逃避攻击的鲁棒性认证

IEEE open journal of control systems Pub Date : 2026-01-01 Epub Date: 2026-03-07 DOI: 10.1109/OJCSYS.2026.3689712

Sara Taheri;Mahalakshmi Sabanayagam;Debarghya Ghoshdastidar;Majid Zamani

{"title":"Robustness Certificates for Neural Networks Against Data Poisoning and Evasion Attacks","authors":"Sara Taheri;Mahalakshmi Sabanayagam;Debarghya Ghoshdastidar;Majid Zamani","doi":"10.1109/OJCSYS.2026.3689712","DOIUrl":"https://doi.org/10.1109/OJCSYS.2026.3689712","url":null,"abstract":"The increasing use of machine learning in safety-critical domains amplifies the risk of adversarial threats, especially data poisoning attacks that corrupt training data to degrade performance or induce unsafe behavior. Most existing defenses lack formal guarantees or rely on restrictive assumptions about the model class, attack type, extent of poisoning, or point-wise certification, limiting their practical reliability. This paper introduces a principled formal robustness certification framework that models gradient-based training as a discrete-time dynamical system (dt-DS) and formulates poisoning robustness as a formal safety verification problem.By adapting the concept of barrier certificates (BCs) from control theory, we introduce sufficient conditions to certify a robust radius ensuring that the terminal model remains safe under worst-case <inline-formula><tex-math>${ell }_{p}$</tex-math></inline-formula>-norm based poisoning. To make this practical, we parameterize BCs as neural networks trained on finite sets of poisoned trajectories. We further derive probably approximately correct (PAC) bounds by solving a scenario convex program (SCP), which yields a confidence lower bound on the certified robustness radius generalizing beyond the training set. Importantly, our framework also extends to certification against test-time attacks, making it the first unified framework to provide formal guarantees in both training and test-time attack settings. Experiments on MNIST, SVHN, CIFAR-10, and CIFAR-100 show that our approach certifies non-trivial perturbation budgets while being model-agnostic and requiring no prior knowledge of the attack or contamination level.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"5 ","pages":"303-318"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11511769","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148171972","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}

引用次数: 0