IEEE open journal of control systems最新文献

{"title":"SAFE–MA–RRT: Data-Driven Safe Motion Planning for Multi-Agent Systems","authors":"Babak Esmaeili;Hamidreza Modares","doi":"10.1109/OJCSYS.2026.3731543","DOIUrl":"https://doi.org/10.1109/OJCSYS.2026.3731543","url":null,"abstract":"This paper proposes a fully data-driven motion-planning framework for homogeneous linear multi-agent systems that operate in shared, obstacle-filled workspaces without access to explicit system models. Each agent independently learns its closed-loop behavior from experimental data by solving convex semidefinite programs that generate locally invariant ellipsoids and corresponding state-feedback gains. These ellipsoids, centered along grid-based waypoints, certify the dynamic feasibility of short-range transitions and define safe regions of operation. A sampling-based planner constructs a tree of such waypoints, where transitions are allowed only when adjacent ellipsoids overlap, ensuring invariant-to-invariant transitions and continuous safety. All agents expand their trees simultaneously and are coordinated through a space–time reservation table that guarantees inter-agent safety by preventing simultaneous occupancy and head-on collisions. Each successful edge in the tree is equipped with its own local controller, enabling execution without re-solving optimization problems at runtime. The resulting trajectories are not only dynamically feasible but also provably safe with respect to both environmental constraints and inter-agent collisions. Simulation results demonstrate the effectiveness of the approach in synthesizing synchronized, safe trajectories for multiple agents under shared dynamics and constraints, using only data and convex optimization tools.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"5 ","pages":"481-495"},"PeriodicalIF":2.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11683630","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149130903","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":"How Much Do Your Models Disagree? Adaptive MPC Safety From Ensemble Uncertainty","authors":"Ali Baheri","doi":"10.1109/OJCSYS.2026.3718343","DOIUrl":"https://doi.org/10.1109/OJCSYS.2026.3718343","url":null,"abstract":"Prediction errors of learned dynamics models vary across the state space, so a fixed safety margin is either too conservative where the model is accurate or too aggressive where it is not. We propose Ensemble-Aware Model Predictive Control (EA-MPC), which turns ensemble disagreement into state-dependent safety margins: it propagates a covariance capturing process noise and model disagreement and inflates obstacle constraints with one of three concentration inequalities: Chebyshev, Cantelli (tighter through one-sided geometry, at the same second-moment assumption), and Gaussian (tightest at these risk levels, under a distributional assumption). The covariance-bound schedule stays bounded, and we lift the per-step margins to the trajectory level via a Bonferroni correction (a union bound given per-step margins) and a steady-state correction (a practical approximation, conservative in our runs). On benchmarks with nonlinear dynamics and systematic mismatch, the bounds trace a cost–safety frontier: the distribution-free bound cuts constraint violations by 76% versus certainty-equivalent control, while the tighter margins reduce cost at the expense of more violations, under mild aggregate undercoverage. We compare EA-MPC against a data-driven quantile-tube baseline and validate it on an underactuated quadrotor, matching well-tuned fixed margins without manually tuning a fixed obstacle margin.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"5 ","pages":"465-480"},"PeriodicalIF":2.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11627318","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148985492","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":"Set-Point Control and Local Stability for Flat Nonlinear Systems Using Model-Following Control","authors":"Julian Willkomm;Kai Wulff;Johann Reger","doi":"10.1109/OJCSYS.2026.3704911","DOIUrl":"https://doi.org/10.1109/OJCSYS.2026.3704911","url":null,"abstract":"We consider the set-point control problem for nonlinear systems with flat output that are subject to perturbations. The nonlinear dynamics as well as the perturbations are locally Lipschitz. We apply the model-following control (MFC) approach which consists of a model control loop (MCL) for a feedforward generation and a process control loop (PCL) that compensates the perturbations using high-gain feedback. We analyse the resulting closed-loop system and discuss its relation to a standard flatness-based high-gain approach. In particular we analyse the estimated region of attraction provided by a quadratic Lyapunov function. A case study illustrates the approach and quantifies the region of attraction obtained for each control approach. Using the initial condition of the model control loop as tuning parameter for the MFC design, provides that a significantly larger set of initial process states can be stabilised compared to a conventional single-loop high-gain design.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"5 ","pages":"363-377"},"PeriodicalIF":2.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11569061","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148443319","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":"Neural Scaling Laws for Learning-Based Identification of Nonlinear Systems","authors":"Marco Roschkowski;Karim Cherifi;Hannes Gernandt","doi":"10.1109/OJCSYS.2026.3693603","DOIUrl":"https://doi.org/10.1109/OJCSYS.2026.3693603","url":null,"abstract":"The use of machine learning models in system identification has increased due to their ability to approximate complex nonlinear dynamics with high accuracy. However, often it is not clear how the performance of trained models scales with given resources such as data, compute, and model size. A recent development in the machine learning community is the use of so-called neural scaling laws (NSLs) between the amount of training resources and performance of a model. To allow for a better understanding of the scalability of the performance of machine learning models, we empirically verify NSLs in the context of system identification from input-state-output and input-output data using different evaluation metrics for accuracy, different systems and different neural network-based system identification architectures. Our results demonstrate predictable scaling behavior, providing a framework to forecast performance improvements and guide model design and data acquisition strategies.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"5 ","pages":"349-362"},"PeriodicalIF":2.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11521052","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148443320","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":"CANVAS: Competency-Aware Navigation Assessment Suite for Pedestrian Trajectory Forecasting","authors":"Jaeuk Shin;Joon Ho Han;Gihwan Kim;Jungjin Lee;Insoon Yang","doi":"10.1109/OJCSYS.2026.3711795","DOIUrl":"https://doi.org/10.1109/OJCSYS.2026.3711795","url":null,"abstract":"We introduce CANVAS, a unified, Python-native framework for control-aware evaluation of pedestrian trajectory forecasting models in dataset-driven, open-loop simulations. Assessing such forecasting models requires metrics that not only capture the downstream effect of forecasting errors on control performance, but also systematically incorporate online feedback, since the models may encounter unfamiliar pedestrian behaviors. To address this need, we devise <italic>competency indices</i>, calibrated measures that quantify how forecasting errors propagate to control, and respond to real-time distributional drift via adaptive conformal prediction (ACP). This integration combines control-aware competency evaluation with ACP, offering a principled and statistically guaranteed mechanism for estimating a model’s online competency. Owing to our dedicated out-of-distribution (OOD) dataset, CANVAS enables rigorous testing of whether forecasting models remain informative even under OOD regimes. As CANVAS standardizes the composition of datasets, forecasting models, and controllers, users can flexibly evaluate their custom prediction-control pipelines. Accordingly, CANVAS supports a reliable and reproducible online assessment of trajectory predictors, unlike existing toolkits that are restricted to offline predictive metrics, overlook control consequences, or disregard OOD conditions. By running evaluations across diverse models and controllers using CANVAS, we gain the key insight that low forecasting error does not necessarily translate into safer or more efficient control.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"5 ","pages":"378-393"},"PeriodicalIF":2.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11601080","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148749198","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":"Networked Competitive Bivirus SIS Model - Analysis of the Discrete-Time Case","authors":"Sebin Gracy;Yifan Xu;Ji Liu;Tamer Başar;César A. Uribe","doi":"10.1109/OJCSYS.2026.3733517","DOIUrl":"https://doi.org/10.1109/OJCSYS.2026.3733517","url":null,"abstract":"The paper studies the problem of simultaneous spread of two viruses, termed virus 1 and virus 2, in a meta-population, and assumes that said viruses are in competition with each other. Against this backdrop, we provide an in-depth analysis of a discrete-time networked competitive bivirus susceptible-infected-susceptible (SIS) model. This system has three kinds of equilibria, viz. the disease-free equilibrium (DFE), the single-virus endemic equilibrium (where one virus is present and the other one is not), and coexistence equilibria (where both the viruses are present in each population node). We show that the model is strongly monotone, and, under certain assumptions, does not admit any periodic orbit. With the limiting behavior thus secured, we turn our attention to identifying conditions guaranteeing the existence of and, in some cases, convergence to different equilibria of our model. We show that, when the reproduction number for virus 1 (resp. virus 2) is greater than one, but the reproduction number of virus 2 (resp. virus 1) is less than one, the spread dynamics converge to the single-virus endemic equilibrium (boundary equilibrium) corresponding to virus 1 (resp. virus 2). When the reproduction number of virus 1 and virus 2 is larger than one, we establish a condition which ensures local exponential convergence to the single-virus equilibrium of virus 1 (resp. virus 2). Subsequently, we provide a sufficient (resp. necessary) condition for the existence of a coexistence equilibrium. It turns out that the said necessary condition for the existence of a coexistence equilibrium also ensures that the boundary equilibrium of virus 2 (resp. virus 1) is locally exponentially stable, while that of virus 1 (resp. virus 2) is unstable. Thereafter, we identify a choice of parameters that gives rise to a locally exponentially attractive line of coexistence equilibria. Finally, we provide an extensive set of simulations to illustrate our theoretical findings.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"5 ","pages":"496-510"},"PeriodicalIF":2.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11689862","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149133845","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":"Predicted-Flow Control Barrier Functions for Real-Time Safe Optimal Control","authors":"Amirsaeid Safari;Jesse B. Hoagg","doi":"10.1109/OJCSYS.2026.3737359","DOIUrl":"https://doi.org/10.1109/OJCSYS.2026.3737359","url":null,"abstract":"Control barrier functions (CBFs) provide real-time safety guarantees through conditions enforced pointwise in time. However, synthesizing a valid CBF is difficult and controllers obtained from pointwise CBF-based optimization are typically myopic. To address myopia, this article introduces predicted-flow control barrier functions (P-CBFs), which generalize the CBF concept from a function of the current state to a functional of a predicted flow under a parametrized control plan over a finite prediction horizon. For safety, a P-CBF can certify that the predicted flow is in a safe set over the entire prediction horizon. However, candidate P-CBFs suffer from the same challenge as candidate CBFs, namely, control constraints make it difficult to guarantee that the P-CBF is valid. This article resolves the validity challenge by introducing a terminal candidate P-CBF requiring that the predicted flow end in a backup safe set at the terminal time, and a planning-time shift that modulates the prediction horizon, providing an additional degree of freedom to ensure feasibility. Then, the real-time control and the evolution of the control-plan parameter and planning-time shift are determined jointly by a single convex optimization that is guaranteed to be feasible provided a backup safe set is known. This safe optimal flow control not only guarantees safety of the state trajectory but also provides a safety certificate of the predicted flow over the entire prediction horizon at each instant of real time. The method unifies finite-horizon integral-cost optimization with safety certification. This optimization reduces to a quadratic program (QP) if the control constraints are a convex polyhedron. The QP implementation, termed <italic>FlowBarrier</i>, is validated on a nonholonomic ground robot navigating a dense environment. FlowBarrier is compared to nonlinear model predictive control and two CBF-based safety filter methods across 100 trials, where FlowBarrier achieves the highest goal-reaching rate, zero safety violations, and the lowest computation time.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"5 ","pages":"560-580"},"PeriodicalIF":2.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11707668","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149188221","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 Control Systems Society Publication Information","authors":"","doi":"10.1109/OJCSYS.2025.3628513","DOIUrl":"https://doi.org/10.1109/OJCSYS.2025.3628513","url":null,"abstract":"","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"4 ","pages":"C3-C3"},"PeriodicalIF":0.0,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11303147","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778370","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":"Quick Updates for the Perturbed Static Output Feedback Control Problem in Linear Systems With Applications to Power Systems","authors":"MirSaleh Bahavarnia;Ahmad F. Taha","doi":"10.1109/OJCSYS.2025.3642596","DOIUrl":"https://doi.org/10.1109/OJCSYS.2025.3642596","url":null,"abstract":"This paper introduces a method for efficiently updating a nominal stabilizing static output feedback (SOF) controller in perturbed linear systems. As operating points and state-space matrices change in dynamic systems, accommodating updates to the SOF controller are necessary. Traditional methods address such changes by re-solving for the updated SOF gain, which is often (<italic>i</i>) computationally expensive due to the NP-hard nature of the problem or (<italic>ii</i>) infeasible due to the limitations of its semi-definite programming relaxations. To overcome this, we leverage the concept of <italic>minimum destabilizing real perturbation</i> (MDRP) to formulate a norm minimization problem that yields fast, reliable controller updates. This approach accommodates a variety of known perturbations, including abrupt changes, model inaccuracies, and equilibrium-dependent linearizations. We remark that the application of our proposed approach is limited to the class of SOF controllers in perturbed linear systems. We also introduce geometric metrics to quantify the proximity to instability and rigorously define stability-guaranteed regions. Extensive numerical simulations validate the efficiency and robustness of the proposed method. Moreover, such extensive numerical simulations corroborate that although we utilize a heuristic optimization method to compute the MDRP, it performs quite well in practice compared to an existing approximation method in the literature, namely the hybrid expansion-contraction (HEC) method. We demonstrate the results on the SOF control of multi-machine power networks with changing operating points, and demonstrate that the computed quick updates produce comparable solutions to the traditional SOF ones, while requiring orders of magnitude less computational time.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"5 ","pages":"49-63"},"PeriodicalIF":0.0,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11298551","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886598","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 Physics-Informed Neural Networks-Based Model Predictive Control Framework for $SIR$ Epidemics","authors":"Aiping Zhong;Baike She;Philip E. Paré","doi":"10.1109/OJCSYS.2025.3641369","DOIUrl":"https://doi.org/10.1109/OJCSYS.2025.3641369","url":null,"abstract":"This work introduces a physics-informed neural networks (PINNs)-based model predictive control (MPC) framework for susceptible-infected-recovered (<inline-formula><tex-math>$SIR$</tex-math></inline-formula>) spreading models. Existing studies in MPC design for epidemic control often assume either 1) measurable states of the dynamics, where the parameters are learned, or 2) known parameters of the model, where the states are learned. In this work, we address the joint real-time estimation of states and parameters within the MPC framework using only noisy infected states, under the assumption that 1) only the recovery rate is known, or 2) only the basic reproduction number is known. Under the first assumption, we propose MPC-PINNs and two novel PINNs algorithms, all of which are integrated into the MPC framework. First, we introduce MPC-PINNs, which are designed for the <inline-formula><tex-math>$SIR$</tex-math></inline-formula> model with control. We then propose log-scaled PINNs (MPC-LS-PINNs), which incorporate a log-scaled loss function to improve robustness against noise. Next, we present split-integral PINNs (MPC-SI-PINNs), which leverage integral operators and state coupling in the neural network training process to effectively reconstruct the complete epidemic state information. Building upon these methods, we further extend our framework for the second assumption. We establish the necessary conditions and extend our PINNs algorithms, where MPC-SI-PINNs are simplified as split-PINNs (MPC-S-PINNs). By incorporating these algorithms into the MPC framework, we simultaneously estimate the epidemic states and parameters while generating optimal control strategies. Comparative experiments against an extended Kalman filter, ideal MPC, and different neural network structures, together with validation on real COVID-19 data from Italy, demonstrate the effectiveness of the proposed methods under different settings.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"5 ","pages":"31-48"},"PeriodicalIF":0.0,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11282991","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830942","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}