IEEE Transactions on Control Systems Technology最新文献_第7页

On Synchronous Motor-Driven Mechanical Systems: Analysis and Linear Parameter-Varying Control 同步电机驱动机械系统：分析与线性变参数控制

IF 4.4 2区计算机科学

IEEE Transactions on Control Systems Technology Pub Date : 2026-03-01 Epub Date: 2026-03-12 DOI: 10.1109/TCST.2026.3667211

Zoltán Téczely;Csaba Fazekas;Bálint Kiss

{"title":"On Synchronous Motor-Driven Mechanical Systems: Analysis and Linear Parameter-Varying Control","authors":"Zoltán Téczely;Csaba Fazekas;Bálint Kiss","doi":"10.1109/TCST.2026.3667211","DOIUrl":"https://doi.org/10.1109/TCST.2026.3667211","url":null,"abstract":"The actuator dynamics problem is a challenging aspect of controller design considering mechatronic systems. While it is tempting to neglect such effects, industrial applications tend to bear the burden of nonlinear motor characteristics. Ideally, the motor time constant is negligible compared with the system dynamics, but when the system enters marginal operating regions, it may increase abruptly, preventing fast and smooth actuation and may lead to oscillations, reduced performance, or even instability. In its simplest form, the permanent magnet synchronous motor (PMSM) is equivalent to a linear system under coordinate transformation coupled with linearizing feedback. However, to account for the imperfections of field-oriented control (FOC), the motor model is redefined. This transformation leads to a less favorable flat output selection scheme, causing exact linearization to be a less viable option. Therefore, an alternative approach is proposed, in which the nonlinear motor character is mapped into a linear parameter-varying (LPV) control framework. The goal of this article is to design a robust control architecture boasting comparable performance to a baseline industrial solution while guaranteeing stability and improved robustness to parameter variations via a smoother actuation character. The presented method is validated on an electric power steering system equipped with a PMSM, where the high-level controller runs on AutoBox and all other elements are identical to a real-life system.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"34 3","pages":"1311-1322"},"PeriodicalIF":4.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148505545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Acoustic Noise Rejection Mode of Atomic Force Microscope Imaging: A Data-Driven Control Approach 原子力显微镜成像的噪声抑制模式：一种数据驱动控制方法

IF 3.9 2区计算机科学

IEEE Transactions on Control Systems Technology Pub Date : 2026-03-01 Epub Date: 2026-03-23 DOI: 10.1109/TCST.2026.3673873

Jiarong Chen;Qingze Zou

{"title":"Acoustic Noise Rejection Mode of Atomic Force Microscope Imaging: A Data-Driven Control Approach","authors":"Jiarong Chen;Qingze Zou","doi":"10.1109/TCST.2026.3673873","DOIUrl":"https://doi.org/10.1109/TCST.2026.3673873","url":null,"abstract":"In this article, we propose an imaging mode of atomic force microscope (AFM) that is robust to environmental disturbances such as acoustic noise. AFM operation is sensitive to external disturbances such as acoustic noise, as disturbances to the probe-sample interaction directly result in distortions in the images obtained. Although conventional passive noise cancellation has been employed, the passive-noise apparatus limits the function of AFM and its use in emerging applications. Moreover, residual noise still persists. In this work, a noise rejection mode (NRM) is proposed to combat the external disturbance effect during the imaging process—regardless of the disturbance location and without identifying the disturbance dynamics. Specifically, the set point of the feedback control loop for tracking the sample topography is adaptively adjusted online such that the AFM system is insensitive and thereby, robust to the environmental noise. Then, an adaptive feedforward controller is augmented to counteract the noise-caused probe vibration. Moreover, the proposed NRM can be integrated to high-speed imaging. Experimental implementation is presented and discussed to illustrate the proposed technique.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"34 3","pages":"1481-1493"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147828873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Disturbance-Adaptive Data-Driven Predictive Control: Trading Comfort Violation for Savings in Building Climate Control 扰动自适应数据驱动的预测控制：在建筑气候控制中以舒适违规换取节约

IF 3.9 2区计算机科学

IEEE Transactions on Control Systems Technology Pub Date : 2026-03-01 Epub Date: 2026-02-23 DOI: 10.1109/TCST.2026.3664129

Jicheng Shi;Christophe Salzmann;Colin N. Jones

{"title":"Disturbance-Adaptive Data-Driven Predictive Control: Trading Comfort Violation for Savings in Building Climate Control","authors":"Jicheng Shi;Christophe Salzmann;Colin N. Jones","doi":"10.1109/TCST.2026.3664129","DOIUrl":"https://doi.org/10.1109/TCST.2026.3664129","url":null,"abstract":"Model predictive control (MPC) has demonstrated significant potential in improving energy efficiency in building climate control, outperforming traditional controllers commonly used in modern building management systems (BMSs). Among MPC variants, data-driven predictive control (DPC) offers the advantage of modeling building dynamics directly from data, thereby substantially reducing commissioning efforts. However, inevitable model uncertainties and measurement noise can result in comfort violations, even with dedicated MPC setups. This article introduces a disturbance-adaptive DPC (DAD-DPC) framework that ensures asymptotic satisfaction of predefined violation bounds without knowing the uncertainty and noise distributions. The framework employs a data-driven pipeline based on Willems’ Fundamental Lemma and conformal prediction for application in building climate control. The proposed DAD-DPC framework was validated through four building cases using the high-fidelity building optimization testing framework (BOPTEST) simulation platform and an occupied campus building, Polydome. DAD-DPC successfully regulated the average comfort violations to meet predefined bounds. Notably, the 5% violation DAD-DPC setup achieved 30.1%/11.2%/27.1%/20.5% energy savings compared to default controllers across four cases. These results demonstrate the framework’s effectiveness in balancing energy consumption and comfort violations, offering a practical solution for building climate control applications.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"34 3","pages":"1419-1434"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147828824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Physics-Guided Gated Recurrent Units for Inversion-Based Feedforward Control 基于反转前馈控制的物理导向门控循环单元

IF 3.9 2区计算机科学

IEEE Transactions on Control Systems Technology Pub Date : 2026-03-01 Epub Date: 2026-02-25 DOI: 10.1109/TCST.2026.3662610

Mingdao Lin;Max Bolderman;Mircea Lazar

{"title":"Physics-Guided Gated Recurrent Units for Inversion-Based Feedforward Control","authors":"Mingdao Lin;Max Bolderman;Mircea Lazar","doi":"10.1109/TCST.2026.3662610","DOIUrl":"https://doi.org/10.1109/TCST.2026.3662610","url":null,"abstract":"Inversion-based feedforward control relies on an accurate model that describes the inverse system dynamics. The gated recurrent unit (GRU), which is a recent architecture in recurrent neural networks (RNNs), is a strong candidate for obtaining such a model from data. However, due to their closed-box nature, GRUs face challenges such as limited interpretability and vulnerability to overfitting. Recently, physics-guided neural networks (PGNNs) have been introduced, which integrate the prior physical model structure into the prediction process. This approach not only improves training convergence, but also facilitates the learning of a physics-based model. In this work, we integrate a GRU in the PGNN framework to obtain a PG-GRU, based on which we adopt a two-step approach to feedforward control design. First, we adopt stable inversion techniques to design a stable linear model of the inverse dynamics. Then, a GRU trained on the residual is tailored to inverse system identification. The resulting PG-GRU feedforward controller is validated by means of real-life experiments on a two-mass spring-damper system, where it demonstrates roughly a twofold improvement compared to the linear feedforward and a preview-based GRU feedforward in terms of the integral absolute error (IAE).","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"34 3","pages":"1641-1648"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147828825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sliding-Mode-Based Control Design for the Butterfly Robot 基于滑模的蝶形机器人控制设计

IF 3.9 2区计算机科学

IEEE Transactions on Control Systems Technology Pub Date : 2026-03-01 Epub Date: 2026-01-27 DOI: 10.1109/TCST.2026.3654633

Maksim Surov

引用次数: 0

Gaussian-Process-Based Adaptive Tracking Control With Dynamic Active Learning for Autonomous Ground Vehicles 基于高斯过程的自主地面车辆动态主动学习自适应跟踪控制

IF 4.4 2区计算机科学

IEEE Transactions on Control Systems Technology Pub Date : 2026-03-01 Epub Date: 2025-11-19 DOI: 10.1109/TCST.2025.3632358

Kristóf Floch;Tamás Péni;Roland Tóth

{"title":"Gaussian-Process-Based Adaptive Tracking Control With Dynamic Active Learning for Autonomous Ground Vehicles","authors":"Kristóf Floch;Tamás Péni;Roland Tóth","doi":"10.1109/TCST.2025.3632358","DOIUrl":"https://doi.org/10.1109/TCST.2025.3632358","url":null,"abstract":"This article proposes an active-learning-based adaptive trajectory tracking control method for autonomous ground vehicles to compensate for modeling errors and unmodeled dynamics. The nominal vehicle model is decoupled into lateral and longitudinal subsystems, which are augmented with online Gaussian processes (GPs) using measurement data. The estimated mean functions of the GPs are used to construct a feedback compensator, which, together with a linear parameter-varying (LPV) state feedback controller designed for the nominal system, gives the adaptive control structure. To assist exploration of the dynamics, the article proposes a new, dynamic active learning method to collect the most informative samples to accelerate the training process. To analyze the performance of the overall learning tool-chain provided controller, a novel iterative, counterexample-based algorithm is proposed for calculating the induced <inline-formula> <tex-math>$mathcal {L}_{2}$ </tex-math></inline-formula> gain between the reference trajectory and the tracking error. The analysis can be executed for a set of possible realizations of the to-be-controlled system, giving a robust performance certificate of the learning method under variation of the vehicle dynamics. The efficiency of the proposed control approach is shown on a high-fidelity physics simulator and in real experiments using a 1/10 scale F1TENTH electric car.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"34 2","pages":"800-812"},"PeriodicalIF":4.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148484604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Distributed Cubature Kalman Filter Based on MEEF With Adaptive Cauchy Kernel for State Estimation 基于自适应柯西核MEEF的分布式稳态卡尔曼滤波状态估计

IF 4.4 2区计算机科学

IEEE Transactions on Control Systems Technology Pub Date : 2026-03-01 Epub Date: 2025-11-10 DOI: 10.1109/TCST.2025.3627630

Duc Viet Nguyen;Haiquan Zhao;Jinhui Hu

{"title":"Distributed Cubature Kalman Filter Based on MEEF With Adaptive Cauchy Kernel for State Estimation","authors":"Duc Viet Nguyen;Haiquan Zhao;Jinhui Hu","doi":"10.1109/TCST.2025.3627630","DOIUrl":"https://doi.org/10.1109/TCST.2025.3627630","url":null,"abstract":"Nowadays, with the development of multisensor networks, the distributed cubature Kalman filter (DCKF) is one of the well-known existing schemes for state estimation, for which the influence of the non-Gaussian noise, abnormal data, and communication burden are urgent challenges. In this article, a DCKF based on adaptive minimum error entropy (MEE) with fiducial points (AMEEF) criterion adaptive MEE with fiducial points-based DCKF (AMEEF-DCKF) is proposed to overcome the above limitations. Specifically, first, in order to solve the influence of various types of non-Gaussian noise and abnormal data, the AMEEF optimization criterion is designed, in which the kernels used are Cauchy kernels with adaptive bandwidth. At the same time, the designed optimization criterion has enhanced the numerical stability and optimized the kernel bandwidth value. Next, in order to address the communication burden problem in multisensor networks, where a leader and a follower are distinguished, a distributed algorithm is constructed to achieve an average consensus among these sensors, called leader–follower average consensus (LFACs). Additionally, the convergence proof of the average consensus algorithm and the computational complexity analysis of the AMEEF-DCKF algorithm are also presented. Finally, through a 10-node sensor network, the effectiveness of the proposed algorithm is demonstrated in estimating the state of the power system and navigating land vehicles in complex environments.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"34 2","pages":"644-656"},"PeriodicalIF":4.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148485398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Control-Informed Wave Prediction for Wave Energy Conversion 波浪能量转换的控制通知波浪预测

IF 3.9 2区计算机科学

IEEE Transactions on Control Systems Technology Pub Date : 2026-03-01 Epub Date: 2026-02-06 DOI: 10.1109/TCST.2026.3655752

Siyuan Zhan;John V. Ringwood

{"title":"Control-Informed Wave Prediction for Wave Energy Conversion","authors":"Siyuan Zhan;John V. Ringwood","doi":"10.1109/TCST.2026.3655752","DOIUrl":"https://doi.org/10.1109/TCST.2026.3655752","url":null,"abstract":"Energy maximization (EM) control for wave energy converters (WECs) is a noncausal control approach that explicitly or implicitly involves feedforward from future wave predictions. In the current noncausal control paradigm, the wave prediction problem and the control problem are considered separately. However, there is a lack of theoretical studies that elucidate the mechanism by which inaccuracies in wave prediction deteriorate EM control performance. This brief aims to address this gap by integrating control considerations into the development of wave predictors. Based on the assumption of a linear WEC, the brief first revisits the linear noncausal optimal control (LNOC) problem, establishing the connections between the control and wave prediction problems. Assuming a known wave spectrum, it is shown that the linear Gaussian process (GP)-based excitation force predictor, which optimizes the conditional covariance of excitation force prediction, also maintains statistical optimality in the WEC EM control problem. Subsequently, a novel time-series-based approach for direct acausal estimation is presented, which achieves the same performance as the optimal predictor but with significantly fewer parameters to determine. The implementation in practical scenarios with an unknown wave spectrum is also discussed. Finally, a numerical simulation, based on a benchmarked point absorber WEC (PA-WEC), is presented to demonstrate the implementation process and the efficacy of the developed predictor.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"34 3","pages":"1584-1591"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11373273","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147828909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Real-Time Nonlinear Model Predictive Control of Large Multirotor Aircraft 大型多旋翼飞机的实时非线性模型预测控制

IF 3.9 2区计算机科学

IEEE Transactions on Control Systems Technology Pub Date : 2026-03-01 Epub Date: 2026-03-18 DOI: 10.1109/TCST.2026.3672184

Jan Zwiener;Johannes Stephan;Christoph Seiferth

引用次数: 0

Multilayer Nested Learning Optimization-Based Coordinated Control for Biped Reconfigurable Robots Under Walking Task Constraints 行走任务约束下基于多层嵌套学习优化的双足可重构机器人协调控制

IF 4.4 2区计算机科学

IEEE Transactions on Control Systems Technology Pub Date : 2026-03-01 Epub Date: 2025-11-24 DOI: 10.1109/TCST.2025.3631986

Qiang Pan;Yuanchun Li;Bing Ma;Bo Dong;Mingchao Zhu;Tianjiao An

{"title":"Multilayer Nested Learning Optimization-Based Coordinated Control for Biped Reconfigurable Robots Under Walking Task Constraints","authors":"Qiang Pan;Yuanchun Li;Bing Ma;Bo Dong;Mingchao Zhu;Tianjiao An","doi":"10.1109/TCST.2025.3631986","DOIUrl":"https://doi.org/10.1109/TCST.2025.3631986","url":null,"abstract":"This article presents an online neuro-dynamic programming (NDP)-based optimal coordinated control method of biped reconfigurable robots (BRRs) under multilayer nested learning structure. Based on the requirements of walking task, the dynamic model of BRRs is established, decomposed into two operating modes: the swing phase and the support phase. To ensure stability during the support phase, a polyhedral friction cone approximation policy is employed to describe the distribution of plantar force, with corresponding reference values obtained through a quadratic programming (QP) algorithm. A novel cost function is constructed, incorporating the interaction forces between BRRs, torso, and ground, effectively reflecting the BRRs’ stability and coordinated control performance. Then, a functional link neural network (FLNN) identifier is designed to compensate for inherent model uncertainties. Leveraging the NDP framework, a near-optimal coordinated controller is obtained through the actor–critic dual-layer neural network (NN), enabling high-performance and stable planar walking of BRRs. Furthermore, the Lyapunov stability method is utilized to demonstrate that the tracking error of closed-loop BRRs is uniformly ultimately bounded (UUB). Finally, the effectiveness of the multilayer nested learning-based optimal coordinated control method is validated through experiments conducted on the self-designed claw-type BRR platform.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"34 2","pages":"773-787"},"PeriodicalIF":4.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148484745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0