IEEE Control Systems LettersIEEE控制系统快报
IEEE Control Systems Letters(英文缩写 IEEE CONTR SYST LETT),ISSN 2475-1456,eISSN 2475-1456,中文译名:IEEE控制系统快报 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-10-01 至 2026-09-30，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 未收录 | N/A |
| 2022 | 3.000 | N/A |
| 2023 | 2.400 | Q2 |
| 2024 | 2.000 | Q3 |
| 2025 | 1.900 | Q3 |
IEEE Control Systems Letters 最新收录文献
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1. Analysis of Bifurcation-Induced Indecisive Limit Cycles in Hybrid Opinion Dynamics with Social Attention-Controlled Consensus.
PMID:日期:2026-06-30Shared beliefs in social interactions evolve through opinion exchanges governed by hybrid time scales and heterogeneous update rules. In this letter, we rigorously analyze hybrid opinion dynamics (HOD) of an interacting continuous-discrete dyad. By characterizing the HOD via a stroboscopic map, we analytically establish that high reactivity triggers a local flip bifurcation that destabilizes consensus. Following this loss of stability, the opinion trajectories enter a saturated plateau-switching regime and converge to a stable indecisive limit cycle representing a persistent cyclic deadlock. Leveraging Floquet theory and a Lyapunov-based cycle-to-cycle contraction, we prove the asymptotic stability of these oscillations. Finally, we propose a social-attention-based control law that guarantees a transition from cyclic deadlock to consensus.
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2. Bio-Inspired Event-Based Visual Servoing for Ground Robots.
PMID:日期:2026-06-15Biological sensory systems are inherently adaptive, filtering out constant stimuli and prioritizing relative changes, likely enhancing computational and metabolic efficiency. Inspired by active sensing behaviors across a wide range of animals, this paper introduces a principled 1D event-based visual servoing framework for ground robots operating in structured environments. Utilizing a Dynamic Vision Sensor (DVS), we demonstrate that by applying a fixed spatial kernel to the asynchronous event stream generated from structured logarithmic intensity-change patterns, the resulting net event flux analytically isolates specific combinations of kinematic states. We establish a generalized theoretical bound for this event rate estimator and show that linear and quadratic spatial profiles isolate the robot's velocity and position-velocity product, respectively. Leveraging these properties, we employ a multi-pattern stimulus to directly synthesize a nonlinear state feedback term entirely without traditional state estimation. To overcome the inescapable loss of linear observability at equilibrium inherent in event sensing, we propose a bio-inspired active sensing limit-cycle controller. Experimental validation on a 1/10-scale autonomous ground vehicle confirms the efficacy, extreme low-latency, and computational efficiency of the proposed direct-sensing approach.
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3. Trajectory Landscapes for Therapeutic Strategy Design in Agent-Based Tumor Microenvironment Models.
PMID:日期:2026-05-26Multiplex tissue imaging (MTI) enables high-dimensional, spatially resolved measurements of the tumor microenvironment (TME), but most clinical datasets are temporally undersampled and longitudinally limited, restricting direct inference of underlying spatiotemporal dynamics and effective intervention timing. Agent-based models (ABMs) provide mechanistic, stochastic simulators of TME evolution; yet their high-dimensional state space and uncertain parameterization make direct control design challenging. This work presents a reduced-order, simulation-driven framework for therapeutic strategy design using ABM-derived trajectory ensembles. Starting from a nominal ABM, we systematically perturb biologically plausible parameters to generate a set of simulated trajectories and construct a low-dimensional trajectory landscape describing TME evolution. From time series of spatial summary statistics extracted from the simulations, we identify a switched Markov State Model (MSM) that captures metastable states and the transitions between them, and whose modes are indexed by the parameter regimes most predictive of terminal-state outcome within the sampled ensemble. To connect simulation dynamics with clinical observations, we map patient MTI snapshots onto the landscape and assess concordance with observed spatial phenotypes and clinical outcomes. We further show that conditioning the MSM on the most predictive parameters yields group-specific transition models to formulate a finite-horizon Markov Decision Process (MDP) and use it for reachability analysis and finite-horizon intervention design. The resulting framework enables simulation-grounded therapeutic policy design for partially observed biological systems without requiring longitudinal patient measurements, taking a step towards adaptive, state-aware therapeutic strategies in oncology.
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4. Blood Pressure Prediction During Blood Transfusion: A Population-Informed Multi-Modal Sequential Inference Approach.
PMID:日期:2025-12-03Blood pressure (BP) management is a critical component of blood transfusion, but no mature technology capable of predicting BP response to blood transfusion exists. This paper concerns the development and preliminary in vivo testing of a BP prediction method applicable to hemorrhage and blood transfusion. Key obstacles are (i) large inter-individual variability in the BP response to blood transfusion, (ii) unknown hemorrhage, and (iii) input/state-dependent observability. To cope with these challenges, we developed a multi-modal sequential inference-enabled BP prediction method built upon a mathematical model of patient physiology parameterized by population-informed prior. The method infers patient-specific physiological state and hemorrhage , and uses them to predict future BP in a patient receiving blood transfusion. The in vivo testing of the method using the data collected from large animals undergoing hemorrhage and blood transfusion showed that it could adequately predict mean arterial BP with median absolute errors for 5-min and 15-min predictions of 3.1 mmHg and 7.4 mmHg as well as adequately infer physiological state and hemorrhage: all the hemorrhage events were detected with <3.5 min delay, with median F1 score of 85%. In sum, the prediction of BP response to blood transfusion may be feasible, even in the presence of unknown hemorrhage.
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5. Control of Subpopulation Fractions in a Population of Bistable Cells.
PMID:日期:2025-09-24Bistable genetic circuits have long been studied in systems and synthetic biology, with notable recent work focused on controlling the phenotypic composition of populations with such circuits. Here, we build on previous literature to theoretically characterize a mechanism by which cells with bistable circuits can use quorum sensing to drive the population to arbitrary phenotypic compositions. We in particular investigate the ability of a proportional controller to accomplish this task on ideal and non-ideal plants, providing performance guarantees and, under additional assumptions, guarantees of almost-global convergence to a desirable population equilibrium.
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6. {"_":"One-Shot Averaging for Distributed TD() Under Markov Sampling.","i":["λ"]}
PMID:日期:2024-06-11We consider a distributed setup for reinforcement learning, where each agent has a copy of the same Markov Decision Process but transitions are sampled from the corresponding Markov chain independently by each agent. We show that in this setting, we can achieve a linear speedup for TD(), a family of popular methods for policy evaluation, in the sense that N agents can evaluate a policy N times faster provided the target accuracy is small enough. Notably, this speedup is achieved by "one shot averaging," a procedure where the agents run TD() with Markov sampling independently and only average their results after the final step. This significantly reduces the amount of communication required to achieve a linear speedup relative to previous work.
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7. A Moments-Based Analytical Approach for Cell Size Homeostasis.
PMID:日期:2024-06-07This contribution explores mechanisms that regulate the dynamics of single-cell size, maintaining equilibrium around a target set point. Using the formalism of Stochastic Hybrid Systems (SHS), we consider continuous exponential growth in cell size (as determined by volume/mass/surface area). This continuous-time evolution is interspersed by cell division events that occur randomly as per a given size-dependent rate, and upon division, only one of the two daughter cells is tracked. We show that a size-independent division rate does not provide cell size homeostasis, in the sense that the variance in cell size increases unboundedly over time. Next, we consider a division rate proportional to cell size that yields the size control observed in several bacteria - a constant size is added on average between birth and division regardless of the newborn size. For this scenario, we obtain exact formulas for the steady-state moments (mean, variance, and skewness) of cell size. Expanding the SHS model, we explore a biologically relevant scenario where the time between successive division events is further divided into multiple discrete stages with size-dependent stage transitions. Exact moment computations demonstrate that increasing the number of stages reduces cell size variability (noise). We also find formulas considering uneven size partitioning between daughters during division, and where the division rate follows a power law of the cell size leading to deviations from adder size control. This letter provides a method for estimating model parameters from observed cell size distributions and enhances our understanding of mechanisms underlying cell size regulation.
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8. Modeling Control of Supercoiling Dynamics and Transcription Using DNA-Binding Proteins.
PMID:日期:2024-05-27Nearly all natural and synthetic gene networks rely on the fundamental process of transcription to enact biological feedback, genetic programs, and living circuitry. In this work, we investigate the efficacy of controlling transcription using a new biophysical mechanism, control of localized supercoiling near a gene of interest. We postulate a basic reaction network model for describing the general phenomenon of transcription and introduce a separate set of equations to describe the dynamics of supercoiling. We show that supercoiling and transcription introduce a shared reaction flux term in the model dynamics and illustrate how the modulation of supercoiling can be used to control transcription rates. We show the supercoiling-transcription model can be written as a nonlinear state-space model, with a radial basis function nonlinearity to capture the empirical relationship between supercoiling and transcription rates. We show the system admits a single, globally exponentially stable equilibrium point. Notably, we show that mRNA steady-state levels can be controlled directly by increasing a length-scale parameter for genetic spacing. Finally, we build a mathematical model to explore the use of a DNA binding protein, to define programmable boundary conditions on supercoiling propagation, which we show can be used to control transcriptional bursting or pulsatile transcriptional response. We show there exists a stabilizing control law for mRNA tracking, using the method of control Lyapunov functions and illustrate these results with numerical simulations.
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9. Viability Under Degraded Control Authority.
PMID:日期:2023-12-12In this letter, we solve the problem of quantifying and mitigating control authority degradation in real time. Here, our target systems are controlled nonlinear affine-in-control evolution equations with finite control input and finite- or infinite-dimensional state. We consider two cases of control input degradation: finitely many affine maps acting on unknown disjoint subsets of the inputs and general Lipschitz continuous maps. These degradation modes are encountered in practice due to actuator wear and tear, hard locks on actuator ranges due to over-excitation, as well as more general changes in the control allocation dynamics. We derive sufficient conditions for identifiability of control authority degradation, and propose a novel real-time algorithm for identifying or approximating control degradation modes. We demonstrate our method on a nonlinear distributed parameter system, namely a one-dimensional heat equation with a velocity-controlled moveable heat source, motivated by autonomous energy-based surgery.
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10. Algebraic Decomposition of Model Predictive Control Problems.
PMID:日期:2023-03-03This paper is concerned with the application of model predictive control (MPC) to large-scale linear dynamical systems with linear inequality constraints. A decomposition is proposed of such problems into sets of independent MPCs of lower dimensions that preserves all information about the system, cost function, and constraints. Different from previous work, the constraints are incorporated in the decomposition procedure, which is attained by generalizing a previously developed technique to simultaneously block diagonalize a set of matrices. This approach is applied to practical examples involving large-scale systems with inequality constraints. It is shown that the computational complexity and the CPU time required to solve the transformed MPC problems are lower than those required by the solution of the original MPC problem.