IFAC Journal of Systems and Control最新文献_第2页

Data-driven design of dynamic quantizers applicable to nonminimum phase systems 非最小相位系统动态量化器的数据驱动设计

IF 1.8

IFAC Journal of Systems and Control Pub Date : 2026-03-01 Epub Date: 2026-01-23 DOI: 10.1016/j.ifacsc.2026.100369

Yusuke Fujimoto , Yuki Minami

引用次数: 0

Joint battery state of charge and parameter estimation using Gaussian integral based Kalman filtering 基于高斯积分卡尔曼滤波的联合电池充电状态和参数估计

IF 1.8

IFAC Journal of Systems and Control Pub Date : 2026-03-01 Epub Date: 2026-02-04 DOI: 10.1016/j.ifacsc.2026.100382

Jaffar Ali Lone , Rohit Kumar Singh , Shovan Bhaumik

{"title":"Joint battery state of charge and parameter estimation using Gaussian integral based Kalman filtering","authors":"Jaffar Ali Lone , Rohit Kumar Singh , Shovan Bhaumik","doi":"10.1016/j.ifacsc.2026.100382","DOIUrl":"10.1016/j.ifacsc.2026.100382","url":null,"abstract":"<div><div>Accurate estimation of battery state of charge (SOC) and model parameters is essential for ensuring the safety, reliability, and efficiency of modern battery management systems. Conventional filtering methods such as the extended Kalman filter rely on local approximations that can degrade in accuracy when nonlinearities are significant. This paper proposes a Gaussian integral-based filtering framework for joint SOC and parameter estimation of lithium-ion batteries modeled with an equivalent circuit representation. The key advantage lies in exploiting the polynomial structure of the SOC–OCV (open-circuit voltage) relation, which enables exact evaluation of Gaussian integrals for mean and covariance propagation. The model is validated against experimental data, and performance is assessed under an urban dynamometer driving schedule. The obtained results demonstrate that the proposed filter achieves more accurate SOC estimation and parameter tracking than the other conventional Kalman filter variants, confirming its effectiveness as a practical solution for real-time battery management under dynamic operating conditions.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"35 ","pages":"Article 100382"},"PeriodicalIF":1.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Uniform error bounds for quantized dynamical models 量子化动力学模型的统一误差界

IF 1.8

IFAC Journal of Systems and Control Pub Date : 2026-03-01 Epub Date: 2026-02-03 DOI: 10.1016/j.ifacsc.2026.100373

Abdelkader Metakalard , Fabien Lauer , Kevin Colin , Marion Gilson

引用次数: 0

Motion data-driven exercise design for the simultaneous enhancement of physical capability and psychological state 运动数据驱动的运动设计，同时增强身体能力和心理状态

IF 1.8

IFAC Journal of Systems and Control Pub Date : 2026-03-01 Epub Date: 2025-12-01 DOI: 10.1016/j.ifacsc.2025.100349

Takao Sato, Yoshiharu Kawahara, Natsuki Kawaguchi, Yusuke Tsunoda

引用次数: 0

Multimodal haptic feedback guidance and discrimination in vision-assisted teleoperation 视觉辅助遥操作中的多模态触觉反馈引导与识别

IF 1.8

IFAC Journal of Systems and Control Pub Date : 2026-03-01 Epub Date: 2025-12-16 DOI: 10.1016/j.ifacsc.2025.100353

Sohaib Ahmad Sirwal , Babar Ahmad , Majid Hameed Koul

{"title":"Multimodal haptic feedback guidance and discrimination in vision-assisted teleoperation","authors":"Sohaib Ahmad Sirwal , Babar Ahmad , Majid Hameed Koul","doi":"10.1016/j.ifacsc.2025.100353","DOIUrl":"10.1016/j.ifacsc.2025.100353","url":null,"abstract":"<div><div>Haptic feedback is essential for intuitive teleoperation, yet designing systems that improve performance without increasing cognitive load remains a critical challenge. This study investigates how the quality of vibrotactile feedback within a multimodal framework influences operator performance and control strategy. A vision-assisted haptic teleoperation system that combines position-error-based force feedback with vibrotactile cues derived from real-time contour detection is proposed. Using a low-cost dual Novint Falcon setup, a user study compared binary vibration with a graded mode employing PWM-based signals to encode proximity. The results demonstrated that graded feedback allowed participants to complete tasks 17% faster with approximately 5% lower RMSE while applying a comparable force. Subjective evaluations also revealed a 32% reduction in mental demand and a 35% reduction in frustration at NASA-TLX, in addition to significantly greater confidence and perceived performance. These findings show that proportional anticipatory feedback allows operators to shift from reactive error correction to more fluid and efficient predictive control strategies. The results infer that the quality and intuitiveness of haptic information is decisive in developing effective telepresence systems, with graded multimodal cues providing clear advantages over binary feedback in the surgical, industrial, and assistive domains.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"35 ","pages":"Article 100353"},"PeriodicalIF":1.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Symbolic learning of interpretable reduced-order models for jumping quadruped robots 跳跃四足机器人可解释降阶模型的符号学习

IF 1.8

IFAC Journal of Systems and Control Pub Date : 2026-03-01 Epub Date: 2026-01-08 DOI: 10.1016/j.ifacsc.2025.100360

Gioele Buriani , Jingyue Liu , Maximilian Stölzle , Cosimo Della Santina , Jiatao Ding

{"title":"Symbolic learning of interpretable reduced-order models for jumping quadruped robots","authors":"Gioele Buriani , Jingyue Liu , Maximilian Stölzle , Cosimo Della Santina , Jiatao Ding","doi":"10.1016/j.ifacsc.2025.100360","DOIUrl":"10.1016/j.ifacsc.2025.100360","url":null,"abstract":"<div><div>Reduced-order models are central to motion planning and control of quadruped robots, yet existing templates are often hand-crafted for a specific locomotion modality. This motivates the need for automatic methods that extract task-specific, interpretable low-dimensional dynamics directly from data. We propose a methodology that combines a linear autoencoder with symbolic regression to derive such models. The linear autoencoder provides a consistent latent embedding for configurations, velocities, accelerations, and inputs, enabling the sparse identification of nonlinear dynamics (SINDy) to operate in a compact, physics-aligned space. A multi-phase, hybrid-aware training scheme ensures coherent latent coordinates across contact transitions. We focus our validation on quadruped jumping—a representative, challenging, yet contained scenario in which a principled template model is especially valuable. The resulting symbolic dynamics outperform the state-of-the-art handcrafted actuated spring-loaded inverted pendulum (aSLIP) baseline in simulation and hardware across multiple robots and jumping modalities.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"35 ","pages":"Article 100360"},"PeriodicalIF":1.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Joint state and parameter estimation in quantum systems using cubature Kalman filtering 利用培养卡尔曼滤波的量子系统联合状态和参数估计

IF 1.8

IFAC Journal of Systems and Control Pub Date : 2026-03-01 Epub Date: 2026-01-14 DOI: 10.1016/j.ifacsc.2026.100363

Eram Taslima, Shyam Kamal, R.K. Saket

引用次数: 0

Regulating population density through antithetic feedback control of cell growth 通过对偶反馈控制细胞生长调节种群密度

IF 1.8

IFAC Journal of Systems and Control Pub Date : 2026-03-01 Epub Date: 2026-02-09 DOI: 10.1016/j.ifacsc.2026.100386

Giovanni Campanile , Vittoria Martinelli , Davide Salzano , Davide Fiore

引用次数: 0

Qualitative behavior analysis of a model underlying the Warburg effect Warburg效应模型的定性行为分析

IF 1.8

IFAC Journal of Systems and Control Pub Date : 2026-03-01 Epub Date: 2026-02-16 DOI: 10.1016/j.ifacsc.2026.100387

Pasquale Palumbo , Susanna Brotti , Raghvendra Singh

引用次数: 0

Stability-constrained policy optimization under unknown rewards 未知奖励下的稳定约束策略优化

IF 1.8

IFAC Journal of Systems and Control Pub Date : 2026-03-01 Epub Date: 2026-01-21 DOI: 10.1016/j.ifacsc.2026.100366

Thomas Banker, Nathan P. Lawrence, Ali Mesbah

{"title":"Stability-constrained policy optimization under unknown rewards","authors":"Thomas Banker, Nathan P. Lawrence, Ali Mesbah","doi":"10.1016/j.ifacsc.2026.100366","DOIUrl":"10.1016/j.ifacsc.2026.100366","url":null,"abstract":"<div><div>A major challenge in reinforcement learning (RL) is guaranteeing an agent’s closed-loop stability under unknown, possibly sparse, reward functions. While model-free RL is flexible to a variety of systems and rewards, model-based control strategies such as optimization-based control naturally accommodate prior system models to provide guarantees on safety and stability. However, these models may not be representative of the true global performance objective, resulting in suboptimal policies. In this paper, we present a policy search RL approach that decouples the stability requirement from the global performance objective. The key idea is to use an optimization-based policy structure as an effective stabilizing parameterization with which the agent can learn to maximize an unknown reward in a model-free fashion. Specifically, the agent employs a predictive control architecture and implicitly learns a stabilizing terminal cost, which is constructed through fixed-point iterations of the discrete algebraic Riccati equation. By implicitly differentiating this fixed-point, derivatives of the stability condition inform policy gradients. The proposed approach is shown to design high-performance, stabilizing policies for various sparse, global performance objectives. Furthermore, the proposed approach can account for uncertainty in the dynamics using the stochastic discrete algebraic Riccati equation to promote robust stability. This work demonstrates a principled policy search RL approach, integrating prior models and system observations in an agent’s design, towards safe and reliable decision-making under uncertainty.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"35 ","pages":"Article 100366"},"PeriodicalIF":1.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0