Control Engineering Practice最新文献_第5页

Dynamic modeling and system identification of user engagement in mHealth interventions using a Bayesian approach for missing data imputation 移动医疗干预中用户参与的动态建模和系统识别，使用贝叶斯方法进行缺失数据的输入

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2025-06-28 DOI: 10.1016/j.conengprac.2025.106460

Mohamed El Mistiri , Steven De La Torre , Benjamin M. Marlin , Misha Pavel , Predrag Klasnja , Donna Spruijt-Metz , Daniel E. Rivera

{"title":"Dynamic modeling and system identification of user engagement in mHealth interventions using a Bayesian approach for missing data imputation","authors":"Mohamed El Mistiri , Steven De La Torre , Benjamin M. Marlin , Misha Pavel , Predrag Klasnja , Donna Spruijt-Metz , Daniel E. Rivera","doi":"10.1016/j.conengprac.2025.106460","DOIUrl":"10.1016/j.conengprac.2025.106460","url":null,"abstract":"<div><div>Digital behavior change interventions (DBCIs) have been found to positively impact health behaviors and are becoming increasingly important as an emerging topic for control systems applications. However, their effectiveness is heavily dependent upon user engagement with both the digital tool (e.g., mHealth app, wearable activity tracker) and the behavior change intervention (e.g., exercise activity planning). In this paper, engagement refers to the unique interactions of a participant with either of these components resulting in digital traces (e.g., app page views). Furthermore, engagement in DBCIs will change over the course of the intervention in response to an individual’s environment, context, and psychological state. Intensive data collection enables modeling engagement in DBCIs as a dynamical system using fluid analogies, and applying prediction-error methods from system identification to estimate models. Missingness represents both a fundamental and practical concern in this application domain. This work addresses missingness using a novel Bayesian imputation method applied to data from the <em>HeartSteps II</em> physical activity intervention study. The benefits of this approach include the ability to impute missing data points more accurately than traditional methods and quantify uncertainty resulting from imputation and data scarcity; the latter is essential to the implementation of robust closed-loop interventions. The methods presented in this work provide insights into critical factors that impact engagement behavior over time and in context, ultimately benefiting the development of digital behavior change interventions relying on control engineering approaches.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"164 ","pages":"Article 106460"},"PeriodicalIF":5.4,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144501139","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

Dynamics parameter estimation for AGVs: A Levenberg–Marquardt-optimization and least-squares-method framework agv动力学参数估计：levenberg - marquardt优化和最小二乘法框架

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2025-06-27 DOI: 10.1016/j.conengprac.2025.106450

Zhe Liu , Cheng Gong , Zhiyang Ju , Zheng Zang , Wenshuo Wang , Jianyong Qi , Xi Zhang , Chenxu Wen , Yuhui Hu , Jianwei Gong

{"title":"Dynamics parameter estimation for AGVs: A Levenberg–Marquardt-optimization and least-squares-method framework","authors":"Zhe Liu , Cheng Gong , Zhiyang Ju , Zheng Zang , Wenshuo Wang , Jianyong Qi , Xi Zhang , Chenxu Wen , Yuhui Hu , Jianwei Gong","doi":"10.1016/j.conengprac.2025.106450","DOIUrl":"10.1016/j.conengprac.2025.106450","url":null,"abstract":"<div><div>Dynamics parameter estimation is of vital importance to establish the accurate dynamics model for autonomous ground vehicles (AGVs). In this paper, a Levenberg–Marquardt-optimization and least-squares-method (LMO-LSM) framework is proposed to estimate vehicle dynamics parameters requiring only conventional sensors. This innovative LMO-LSM framework incorporates the simplified Pacejka magic formula tire model alongside the vehicle lateral dynamics model and is composed of two phases to estimate the twelve parameters. The first phase is to estimate the distances from the vehicle center of gravity to the front and rear axles, the Pacejka parameter calculation coefficients and the Pacejka parameters through Levenberg–Marquardt-optimization, ensuring the predicted lateral acceleration sequence closely aligns with the real lateral acceleration sequence. The second phase is to estimate the yaw moment of inertia through least-squares-method by minimizing the discrepancy between the predicted yaw moment sequence and the real yaw moment sequence. Furthermore, the proposed LMO-LSM framework is tested in the high-fidelity MATLAB/Simulink-CarSim co-simulation and real-world field experiments, validating the effectiveness and practicality of the LMO-LSM framework.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"164 ","pages":"Article 106450"},"PeriodicalIF":5.4,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144491015","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

Predictor neural network-based model-free predictive control using virtual voltage vector for multiparallel power converters 基于预测神经网络的多并联变流器无模型虚拟电压矢量预测控制

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2025-06-26 DOI: 10.1016/j.conengprac.2025.106451

Bohao Zhang , Lin Qiu , Xing Liu , Youtong Fang

{"title":"Predictor neural network-based model-free predictive control using virtual voltage vector for multiparallel power converters","authors":"Bohao Zhang , Lin Qiu , Xing Liu , Youtong Fang","doi":"10.1016/j.conengprac.2025.106451","DOIUrl":"10.1016/j.conengprac.2025.106451","url":null,"abstract":"<div><div>In recent years, multilevel converter has attracted attention due to its advantages such as higher voltage capability and lower voltage distortion rate. To meet high-power demands, the parallel operation of inverters has become a necessary choice. However, parallel operation of inverters can lead to severe zero-sequence circulating current problems, affecting the quality of system output power. The conventional control methods, moreover, require the knowledge of the exact model of the system and suffer from the problem of poor robustness. In this paper, an innovative control scheme is proposed to address this issue. Specifically, this scheme rapidly identifies and models unknown nonlinearities and uncertainties of the system, combines a feedback mechanism for prediction errors to update neural predictors, and introduces virtual voltage vectors to prevent the occurrence of zero-sequence circulating currents. Furthermore, the main contribution of this paper is that the proposed method can smoothly and quickly capture system dynamics, improve the robustness and reliability of the control system in the presence of parameter uncertainties, achieve suppression of zero-sequence circulating currents, and exhibit good current tracking accuracy. Finally, comprehensive simulation and experimental results are presented to verify the efficacy of the proposed control method for multiparallel power converters.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"164 ","pages":"Article 106451"},"PeriodicalIF":5.4,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144481313","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

Prescribed performance UAV tracking control under disturbance using reinforcement learning-based backstepping 基于强化学习的干扰下无人机预定性能跟踪控制

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2025-06-25 DOI: 10.1016/j.conengprac.2025.106393

Meiying Yang , Hai Zhu , Xiaozhou Zhu , Zhe Liu , Wen Yao , Xiaoqian Chen

{"title":"Prescribed performance UAV tracking control under disturbance using reinforcement learning-based backstepping","authors":"Meiying Yang , Hai Zhu , Xiaozhou Zhu , Zhe Liu , Wen Yao , Xiaoqian Chen","doi":"10.1016/j.conengprac.2025.106393","DOIUrl":"10.1016/j.conengprac.2025.106393","url":null,"abstract":"<div><div>For the tracking control problem of unmanned aerial vehicle (UAVs) with nonlinear and strongly coupled dynamics, a reinforcement learning (RL) optimization control method with prescribed performance under disturbances is proposed based on the backstepping framework. This method employs RL to solve the Hamilton–Jacobi–Bellman (HJB) equation in the optimization problem, which involves tracking errors and control inputs. Among them, the actor network is used in the controller to ensure system stability, while the critic network is employed to evaluate system performance through performance index functions. Additionally, a reduced-order extended state observer is designed to estimate external disturbances, and the estimated results are applied to the controller to compensate for the impact of external disturbances on the UAV. During controller design, first-order filtering helps resolve the complex differentiation issues inherent in the backstepping method. For the prescribed performance issues, particularly the tracking error constraint, a performance index function guarantees that the tracking error stays within the desired range. Next, the stability performance of the UAV system is proven using Lyapunov theory. Finally, the effectiveness of the proposed control method is further validated through numerical simulations and physical experiments.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"164 ","pages":"Article 106393"},"PeriodicalIF":5.4,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144471688","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

Reachability-based rendezvous guidance for autonomous refueling of unmanned aerial systems 基于可达性的无人机自主加油交会制导

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2025-06-24 DOI: 10.1016/j.conengprac.2025.106449

Seokwon Lee , Mingu Kim

引用次数: 0

Bounded integral control for uncertain ISS systems with convex input constraints 具有凸输入约束的不确定ISS系统的有界积分控制

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2025-06-21 DOI: 10.1016/j.conengprac.2025.106448

George C. Konstantopoulos, Panos C. Papageorgiou, Charalampos P. Bechlioulis

{"title":"Bounded integral control for uncertain ISS systems with convex input constraints","authors":"George C. Konstantopoulos, Panos C. Papageorgiou, Charalampos P. Bechlioulis","doi":"10.1016/j.conengprac.2025.106448","DOIUrl":"10.1016/j.conengprac.2025.106448","url":null,"abstract":"<div><div>In this paper, a new Bounded Integral Controller (BIC) is proposed to replace the conventional Integral Control (IC) for regulating uncertain Input-to-State Stable (ISS) nonlinear systems and additionally ensuring that the control input evolution remains within a prescribed compact convex set for all time. This is particularly important for controlling multi-input systems with uncertainties or unknown dynamics/parameters and handling input constraints that introduce couplings between the control input elements forming a specific compact and convex set. Given this set, the proposed BIC takes a suitable nonlinear dynamic form and employing ISS and invariant set theory, it is analytically proven that the trajectory of the entire control input vector will remain within the desired set independently of the plant dynamic structure or parameters. Contrary to the original BIC and its recent extensions, which either limit the control input elements independently or restrict them within a ball set (Euclidean norm bound), the proposed approach may constrain the input evolution within any given compact convex set, thus leading to a generalisation of the original BIC. In order to illustrate the theoretical analysis of the proposed BIC and compare its performance with respect to the conventional methods, one academic and two realistic examples from the area of robotics and power systems are investigated using a simulated underwater robot and a power converter in an experimental platform, respectively, each introducing different input constraints.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"164 ","pages":"Article 106448"},"PeriodicalIF":5.4,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330852","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

Real-time privacy-preserving coordination for cross-carrier truck platooning 跨载波卡车队列的实时隐私保护协调

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2025-06-21 DOI: 10.1016/j.conengprac.2025.106452

Yikai Zeng , Ting Bai , Jonas Mårtensson , Meng Wang

{"title":"Real-time privacy-preserving coordination for cross-carrier truck platooning","authors":"Yikai Zeng , Ting Bai , Jonas Mårtensson , Meng Wang","doi":"10.1016/j.conengprac.2025.106452","DOIUrl":"10.1016/j.conengprac.2025.106452","url":null,"abstract":"<div><div>Truck platooning, an autonomous driving technology, reduces fuel consumption and emissions by organizing heavy-duty vehicles (HDVs) into convoys. While single-carrier platooning is feasible, cross-carrier implementations present challenges due to privacy concerns between competing carriers and third parties. This paper presents a real-time, privacy-preserving coordination framework for cross-carrier platooning. The framework safeguards sensitive itinerary data against both peer carriers and third-party service providers. Secure multi-party computation techniques are employed to ensure that planning data remains private, while collaborative decision-making enables effective coordination without the need for a centralized third party. A distributed model predictive control approach dynamically updates truck plans at hubs to optimize platooning opportunities. The framework is evaluated through large-scale simulations using real-world-inspired data, demonstrating its practicality. Results indicate a minor reduction in cost-saving performance but no significant computational overhead from privacy-preserving mechanisms compared to predictive coordination with the third party, highlighting an effective balance between privacy and coordination effectiveness.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"164 ","pages":"Article 106452"},"PeriodicalIF":5.4,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330861","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

Recursive total least squares with improved parameter tracking: Application to model-based vehicle mass estimation 改进参数跟踪的递归总最小二乘：在基于模型的车辆质量估计中的应用

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2025-06-20 DOI: 10.1016/j.conengprac.2025.106429

Hugo Koide , Jérémy Vayssettes , Guillaume Mercère

{"title":"Recursive total least squares with improved parameter tracking: Application to model-based vehicle mass estimation","authors":"Hugo Koide , Jérémy Vayssettes , Guillaume Mercère","doi":"10.1016/j.conengprac.2025.106429","DOIUrl":"10.1016/j.conengprac.2025.106429","url":null,"abstract":"<div><div>Vehicle mass plays an influential role in various dynamical systems for vehicle safety and control. In this work, a novel recursive total least squares (RTLS) solution is presented for the online estimation of gross vehicle mass. The proposed method requires access to engine torque, engine speed, wheel speed, and vehicle IMU acceleration measurements. Different algorithm configurations are considered for mass estimation of internal combustion engine and electric vehicles, with a focused application to passenger cars and light commercial vehicles. The baseline RTLS algorithm is improved by means of regularization, outlier attenuation, parameter projection, and enhanced tracking of jumping parameters, all of which play an important role in optimizing estimator performance for industrial applications. The proposed algorithm is then generalized to account for heterogeneous and heteroscedastic measurement noise with a recursive noise covariance estimation algorithm. The method is tested against two well-known benchmark algorithms from the mass estimation literature with experimental electric vehicle data, and solution sensitivity to model assumptions and model input parameters is discussed. The vehicle experiments show that the proposed method outperforms the benchmark methods in terms of accuracy and convergence characteristics.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"164 ","pages":"Article 106429"},"PeriodicalIF":5.4,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330860","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

Non-parametric system norm estimation of multivariable systems 多变量系统的非参数系统范数估计

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2025-06-19 DOI: 10.1016/j.conengprac.2025.106421

Paul Tacx , Tom Oomen

引用次数: 0

Modeling and validation of a clinker production process model that captures the coupled internal dynamics 捕获耦合内部动力学的熟料生产过程模型的建模和验证

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2025-06-19 DOI: 10.1016/j.conengprac.2025.106438

Melih Turkseven, Muhammad Aslani Moghanloo

{"title":"Modeling and validation of a clinker production process model that captures the coupled internal dynamics","authors":"Melih Turkseven, Muhammad Aslani Moghanloo","doi":"10.1016/j.conengprac.2025.106438","DOIUrl":"10.1016/j.conengprac.2025.106438","url":null,"abstract":"<div><div>Cement clinker production is an energy-intensive process that accounts for a substantial share of global industrial energy consumption. Model predictive control (MPC) is commonly used to regulate this process, requiring a compact control-oriented model that describes the input–output relationships of the production system. A prevalent method for obtaining such a model is to identify direct input–output relationships. However, this approach often overlooks the coupled dynamics of internal process variables, which can limit prediction accuracy. This study introduces a methodology for mapping these dynamically coupled internal variables by modeling the production process as a network of interconnected chambers. A discrete-linear model that captures these couplings is then developed using data collected from an operational clinker plant. The proposed model is evaluated against widely-used linear modeling approaches, with a focus on multi-step-ahead prediction performance, a metric often neglected in the literature. Eight key variables were chosen as targets for prediction, and the proposed model consistently outperformed the alternatives in predicting their variations, particularly when the prediction horizon exceeded five sampling intervals. An MPC implementation of the proposed model is provided for illustrating its potential use.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"164 ","pages":"Article 106438"},"PeriodicalIF":5.4,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322494","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