Control Engineering Practice最新文献_第2页

A natural indirect adaptive controller for a satellite-mounted manipulator 星载机械臂的自然间接自适应控制器

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2025-05-16 DOI: 10.1016/j.conengprac.2025.106395

Jacopo Giordano , Angelo Cenedese , Andrea Serrani

引用次数: 0

BKMPC-ESO: A data-driven bilinear model predictive control framework for soft robots with unknown nonlinear dynamics compensation BKMPC-ESO：具有未知非线性动力学补偿的软机器人数据驱动双线性模型预测控制框架

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2025-05-14 DOI: 10.1016/j.conengprac.2025.106390

Shengchuang Guan , Zhaobing Liu

{"title":"BKMPC-ESO: A data-driven bilinear model predictive control framework for soft robots with unknown nonlinear dynamics compensation","authors":"Shengchuang Guan , Zhaobing Liu","doi":"10.1016/j.conengprac.2025.106390","DOIUrl":"10.1016/j.conengprac.2025.106390","url":null,"abstract":"<div><div>In this paper, we introduce a novel control framework, termed BKMPC-ESO, which integrates bilinear Koopman model predictive control (BKMPC) with an extended state observer (ESO) for the modeling and control of soft robots. This framework specifically addresses the challenges posed by modeling errors and unknown disturbances, which often degrade the control performance of soft robots. It leverages the data-driven bilinear Koopman model to merge the computational efficiency of linear models with the predictive precision of nonlinear models, thereby adapting to the dynamics of diverse systems. Furthermore, the ESO is incorporated for real-time estimation of modeling errors and external disturbances, with these estimates being dynamically compensated within the MPC. This approach effectively mitigates the limitations of the offline bilinear Koopman model in capturing real-time parameter variations and external disturbances, enhancing the system’s control precision. Notably, the proposed BKMPC approach guarantees recursive feasibility and stability across an extended prediction horizon, with the stability of the ESO being rigorously validated through theoretical analysis. The efficacy of our framework is exemplified through its application on a three-dimensional (3D) soft manipulator. It is able to adeptly track a variety of reference trajectories, ranging from simple to complex, thereby highlighting the framework’s potential to significantly enhance the performance capabilities of soft robotic systems.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"162 ","pages":"Article 106390"},"PeriodicalIF":5.4,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143942280","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

Simplicial complexes graph convolution networks with higher-order features learning for limited samples diagnosis 有限样本诊断中具有高阶特征学习的简单复图卷积网络

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2025-05-13 DOI: 10.1016/j.conengprac.2025.106391

Xian-Jie Zhang , Hai-Feng Zhang , Kai Zhong , Xiao-Ming Zhang

{"title":"Simplicial complexes graph convolution networks with higher-order features learning for limited samples diagnosis","authors":"Xian-Jie Zhang , Hai-Feng Zhang , Kai Zhong , Xiao-Ming Zhang","doi":"10.1016/j.conengprac.2025.106391","DOIUrl":"10.1016/j.conengprac.2025.106391","url":null,"abstract":"<div><div>With the advancement of industrial automation, there is an increasing focus on research concerning limited fault samples. Although meta-learning and other methods can address this issue, they often necessitate the incorporation of additional data and are unable to directly diagnose faults using only unlabeled data along with a small amount of labeled data. In response, this article proposes the use of simplicial complexes graph convolutional networks for fault diagnosis, which simultaneously account for both higher-order and lower-order topological structures among samples. This approach effectively addresses the challenge of limited samples by extracting relevant information from unlabeled data without the need to introduce new knowledge. Initially, simplices of varying dimensions are employed within a constructed simple graph to represent different relationships among samples. Subsequently, the simplicial complexes convolutional network is introduced to capture the higher-order information, while the graph convolutional network is utilized to obtain the lower-order information. The combined feature information is then input into a classifier for fault diagnosis. Finally, experiments conducted on two datasets characterized by small sample sizes or imbalanced samples demonstrate the method’s commendable diagnostic performance, as well as its robustness and practicality.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"162 ","pages":"Article 106391"},"PeriodicalIF":5.4,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143936555","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

Enhancing trajectory tracking and energy efficiency in Pneumatic Servo Translational Parallel Manipulator with Frac-SMC-RFESO Control 采用fracc - smc - rfeso控制提高气动伺服平移并联机器人的轨迹跟踪和能效

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2025-05-13 DOI: 10.1016/j.conengprac.2025.106372

Lian-Wang Lee , Jin-Yu Yang , I-Hsum Li

{"title":"Enhancing trajectory tracking and energy efficiency in Pneumatic Servo Translational Parallel Manipulator with Frac-SMC-RFESO Control","authors":"Lian-Wang Lee , Jin-Yu Yang , I-Hsum Li","doi":"10.1016/j.conengprac.2025.106372","DOIUrl":"10.1016/j.conengprac.2025.106372","url":null,"abstract":"<div><div>Pneumatic-driven systems often face significant energy consumption challenges, primarily due to compressed air leakage and inefficient utilization. To address these issues, this paper presents a comprehensive solution encompassing both hardware and software designs for a Pneumatic Servo Translational Parallel Manipulator (PS-TPM). On the software side, the proposed energy-efficient controller, Frac-SMC-RFESO, integrates fractional-order sliding mode control with a reduced fractional-order extended state observer (RFESO) to optimize the PS-TPM’s performance. This integration endows the Frac-SMC-RFESO with exceptional adaptability, enabling it to effectively manage the system’s complex dynamics and nonlinearities while minimizing energy consumption. Furthermore, by utilizing the RFESO, the controller estimates system uncertainties and disturbances within the PS-TPM, further enhancing trajectory tracking performance. In its mechanical design, the PS-TPM incorporates double-acting magnetic rodless cylinders paired with proportional directional control valves, improving sealing to prevent air leakage and thereby enhancing energy efficiency. Experimental results highlight the exceptional performance of the Frac-SMC-RFESO controller, achieving superior trajectory tracking while reducing energy consumption by 16% to 47% compared to the RLESO-SMC (an integer-order sliding mode controller with a reduced-order linear extended state observer), LADRC (a linear active disturbance rejection controller), and the PID controller.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"162 ","pages":"Article 106372"},"PeriodicalIF":5.4,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143936554","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

Model predictive control of high moisture extrusion cooking 高水分挤压蒸煮的模型预测控制

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2025-05-12 DOI: 10.1016/j.conengprac.2025.106387

Adrian Ticǎ , Vivek S. Pinnamaraju , Eric Stirnemann , Erich J. Windhab

{"title":"Model predictive control of high moisture extrusion cooking","authors":"Adrian Ticǎ , Vivek S. Pinnamaraju , Eric Stirnemann , Erich J. Windhab","doi":"10.1016/j.conengprac.2025.106387","DOIUrl":"10.1016/j.conengprac.2025.106387","url":null,"abstract":"<div><div>High Moisture Extrusion Cooking (HMEC) has become a promising technology for producing plant-based meat alternatives. By using HMEC, food manufacturers can create meat-like textures from plant proteins, offering a sustainable solution with reduced carbon footprint to consumers. However, at the current stage of development, the automation level in HMEC is insufficient to ensure operational autonomy, reliability, and product quality expected by industry demands. This paper presents a predictive control framework designed to transform experience-based handled HMEC into a more reliable process operation, improving its production performance and facilitating industrial up-scaling. The proposed control structure is hierarchical, comprising two layers. At the upper layer, a model predictive control (MPC) algorithm determines the optimal set-points for the controllers at the lower layer. The predictive framework is built on the existing HMEC control architecture and can be further extended to achieve fully optimized production. Leveraging linear dynamic models, the approach mainly focuses on the protein melt control aiming to enhance production performance by minimizing the tracking error of process quantities correlated to product quality. The practical feasibility of the designed control solution has been proven on a pilot-scale extruder. Validation results have shown improved operational stability and reproducibility, while effectively tracking set-points for consistent meat-like fibrous structure formation and desired textural characteristics.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"162 ","pages":"Article 106387"},"PeriodicalIF":5.4,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143936553","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

High efficiency DC–DC converter for renewable energy integration and energy storage applications: A review of topologies and control strategies 用于可再生能源集成和储能应用的高效DC-DC变换器：拓扑结构和控制策略综述

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2025-05-11 DOI: 10.1016/j.conengprac.2025.106371

Mohamed Mezouari, Meriem Megrini, Ahmed Gaga

{"title":"High efficiency DC–DC converter for renewable energy integration and energy storage applications: A review of topologies and control strategies","authors":"Mohamed Mezouari, Meriem Megrini, Ahmed Gaga","doi":"10.1016/j.conengprac.2025.106371","DOIUrl":"10.1016/j.conengprac.2025.106371","url":null,"abstract":"<div><div>The growing demand for efficient energy systems drives the need for advanced power electronics, with DC–DC converters playing a pivotal role in renewable energy integration and energy storage applications. These converters, particularly bidirectional types, are essential for managing the flow of energy in modern power grids and electric vehicle systems. This paper provides a comprehensive review of the latest developments in DC–DC converter technologies, focusing on their topologies, control strategies, and applications in renewable energy systems. The study highlights various converter configurations, including non-isolated and isolated topologies, and evaluates state-of-the-art control techniques such as Artificial Intelligence-Based Control, Model Predictive Control (MPC), and Sliding Mode Control (SMC) for optimizing efficiency and reliability. The importance of bidirectional converters in enabling seamless energy flow for smart grids and energy storage is emphasized, with a particular focus on their role in Grid-to-Vehicle (G2V), Vehicle-to-Grid (V2G), and Vehicle-for-Grid (V4G) systems. Additionally, a detailed analysis of the challenges and opportunities in this field is presented, with identified research gaps paving the way for future advancements in DC–DC converter technologies. This study presents a performance analysis and comparison of control strategies for DC–DC converters, providing an in-depth examination of their impact on the performance of bidirectional DC–DC converters and offering valuable insights for optimizing future energy systems and enhancing the integration of renewable energy sources.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"162 ","pages":"Article 106371"},"PeriodicalIF":5.4,"publicationDate":"2025-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143931399","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

Adaptive event-triggered set-membership formation of heterogeneous multi-agent systems with application to omnidirectional robots 异构多智能体系统的自适应事件触发集隶属度形成及其在全向机器人中的应用

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2025-05-10 DOI: 10.1016/j.conengprac.2025.106384

Zehui Xiao , Daotong Zhang , Jie Tao

{"title":"Adaptive event-triggered set-membership formation of heterogeneous multi-agent systems with application to omnidirectional robots","authors":"Zehui Xiao , Daotong Zhang , Jie Tao","doi":"10.1016/j.conengprac.2025.106384","DOIUrl":"10.1016/j.conengprac.2025.106384","url":null,"abstract":"<div><div>This article focuses on the leader-following formation control for heterogeneous multi-agent systems against actuator saturation and switching topologies. To save limited agent resources, a novel adaptive event-triggered strategy is evolved to regulate the update frequency of controller, such that some unnecessary data processing can be avoided. Different from the conventional adaptive strategy where the tracking error is simply regarded as the triggering benchmark, a transformation law is introduced in this work to develop an adaptive triggering benchmark, which provides greater potential for the triggering condition to save computational resources to a greater extent. Then, the formation control protocols are proposed by introducing the set-membership concept. Subsequently, considering the saturation constraints, optimization problems of the set-membership formation control are established to ensure that the tracking errors are always restrained to the desired ellipsoids. For achieving the leader-following formation, an online recursive algorithm based on the optimization problems is consequently provided such that the set-membership controllers meeting the ellipsoidal state sets can be continuously obtained. Finally, the superiority and applicability of the proposed method were validated through numerical simulations and its implementation on an omnidirectional robot platform.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"162 ","pages":"Article 106384"},"PeriodicalIF":5.4,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143927564","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

Capturing sequence similarity using local spatiotemporal manifold-regularized dynamic network for semi-supervised industrial quality prediction 基于局部时空流形正则化动态网络的半监督工业质量预测序列相似性捕获

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2025-05-09 DOI: 10.1016/j.conengprac.2025.106374

Kai Wang, Xiang Lei, Sijia Wang, Xiaofeng Yuan, Chunhua Yang

{"title":"Capturing sequence similarity using local spatiotemporal manifold-regularized dynamic network for semi-supervised industrial quality prediction","authors":"Kai Wang, Xiang Lei, Sijia Wang, Xiaofeng Yuan, Chunhua Yang","doi":"10.1016/j.conengprac.2025.106374","DOIUrl":"10.1016/j.conengprac.2025.106374","url":null,"abstract":"<div><div>Due to the dynamics and label-scarcity of most industrial processes, semi-supervised dynamic quality prediction models have gradually become a research hotspot. For semi-supervised learning, the widely applied manifold regularization ignores the significant dynamics of process variables and the slow-varying properties of quality variables, leading to the failure of its manifold similarity assumption that similar inputs yield similar outputs. Moreover, its computational burden is heavy for applications. To deal with these issues, this study proposes a new local spatiotemporal manifold regularization (LSTMR) method. Specifically, LSTMR designs local spatial similarity and local temporal similarity with full consideration of the dynamics and slow-varying characteristics. The enhanced manifold regularization is obtained through similarity weighting to mine the latent information from unlabeled data. Meanwhile, the computational burden is significantly reduced by omitting unnecessary similarity calculation for sequences pairs. Finally, a dual-attention dynamic learning network (DADLnet) assisted by LSTMR is constructed for quality prediction. The DADLnet’ prediction objective is achieved by applying the prediction error term for labeled data and the LSTMR term for unlabeled data. The applications to an actual alumina digestion process exhibit the superiority of LSTMR-DADLnet.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"162 ","pages":"Article 106374"},"PeriodicalIF":5.4,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143923867","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

Learning robust residuals for attack diagnosis of advanced driver assist systems 基于鲁棒残差的高级驾驶辅助系统攻击诊断研究

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2025-05-06 DOI: 10.1016/j.conengprac.2025.106366

Vishnu Renganathan , Daniel Jung , Ekim Yurtsever , Qadeer Ahmed

{"title":"Learning robust residuals for attack diagnosis of advanced driver assist systems","authors":"Vishnu Renganathan , Daniel Jung , Ekim Yurtsever , Qadeer Ahmed","doi":"10.1016/j.conengprac.2025.106366","DOIUrl":"10.1016/j.conengprac.2025.106366","url":null,"abstract":"<div><div>Complex autonomous and Cyber–Physical Systems (CPS) require reliable attack diagnostics with robustness to external disturbances, noise, and parametric uncertainties that ensure minimum time delay to detect cyber or physical attacks. Even using data-driven techniques for this motive poses a challenge because collecting training data that encompasses all possible attack signatures is difficult. Some attacks may have multiple realizations due to varying operating conditions. The proposed solution to this problem is to add physical insights to the data-driven model and use sparse regression to learn the underlying dynamics of the system. To tackle the problem of uncertainty in data due to external disturbances, noise, and parametric uncertainties, the model is learned multiple times using bootstraps of data, and parameter aggregation is performed to get an aggregated model. Then, using this aggregated model, robust residuals are designed to detect and isolate the attacks. Data from the lane keep assist system of an actual car is used to validate the model, and simulations are used to expand the data to varying operating conditions and perform multiple attacks on the system. The proposed approach for attack detection is compared to the baseline model-based diagnostic techniques like structural residuals and Extended Kalman Filter (EKF). In this work, the security implications of the system are analyzed, and robust residuals are designed with minimum knowledge about the underlying system dynamics, thus promoting the need for security by design.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"162 ","pages":"Article 106366"},"PeriodicalIF":5.4,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143911614","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

Safe docking of a payload-carrying spacecraft using state constrained adaptive control 基于状态约束自适应控制的航天器安全对接

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2025-05-06 DOI: 10.1016/j.conengprac.2025.106363

Viswa Narayanan Sankaranarayanan, Avijit Banerjee, Sumeet Satpute, George Nikolakopoulos

{"title":"Safe docking of a payload-carrying spacecraft using state constrained adaptive control","authors":"Viswa Narayanan Sankaranarayanan, Avijit Banerjee, Sumeet Satpute, George Nikolakopoulos","doi":"10.1016/j.conengprac.2025.106363","DOIUrl":"10.1016/j.conengprac.2025.106363","url":null,"abstract":"<div><div>In this article, we design an adaptive controller for the position and heading control for a payload-carrying spacecraft to perform docking with a target docking station. We address the problem by identifying the state constraints required to safely dock the spacecraft and imposing these constraints on an adaptive tracking controller. To make the controller adapt to different types of payloads, the adaptive controller is designed without any explicit a priori knowledge of the system dynamics or bound for the uncertainties. Furthermore, to accommodate a wide range of initial conditions, the constraints are chosen to be time-varying. Thus, unlike conventional controllers, the proposed controller enforces the safety of the spacecraft during docking by imposing state constraints while adapting to unknown drastic dynamic variations. The controller is validated in simulation for docking a 6 DoF spacecraft in the orbital space. Additionally, for technology readiness, we have performed the hardware validation of the controller using a payload-carrying planar floating robot and a prototype docking station. Compared to the state-of-the-art controllers, the proposed controller guarantees predefined time-varying state constraints while significantly improving the performance. The video of the experimental results is presented here: <span><span>https://youtu.be/tJtJBibzHhI</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"162 ","pages":"Article 106363"},"PeriodicalIF":5.4,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143906675","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