Control Engineering Practice最新文献

Robust motion planning for autonomous vehicles based on environment and uncertainty-aware reachability prediction

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2025-03-27 DOI: 10.1016/j.conengprac.2025.106319

Jian Zhou , Yulong Gao , Björn Olofsson , Erik Frisk

{"title":"Robust motion planning for autonomous vehicles based on environment and uncertainty-aware reachability prediction","authors":"Jian Zhou , Yulong Gao , Björn Olofsson , Erik Frisk","doi":"10.1016/j.conengprac.2025.106319","DOIUrl":"10.1016/j.conengprac.2025.106319","url":null,"abstract":"<div><div>Planning and navigation in real-time traffic is challenging, since the driving environment (e.g., road network and infrastructure) is complex and the accurate prediction of surrounding vehicles is hard. To address this, this paper proposes an environment and uncertainty-aware robust motion-planning strategy. The method achieves environment awareness by considering road-geometry constraints in the reachability prediction of surrounding vehicles, and uncertainty awareness by online learning the intended control set of the surrounding vehicles. By integrating this dual awareness, the method effectively predicts the forward reachability of surrounding vehicles, which is applied in the design of collision-avoidance constraints in the optimal motion-planning strategy. The motion planner then computes the reference trajectory for the autonomous ego vehicle using a receding-horizon approach to fit variations in the dynamic traffic. The effectiveness of the strategy is demonstrated through simulations in roundabout scenarios by comparing with alternative methods, further validated in a traffic scenario from a dataset recorded in the real world. Additionally, the feasibility of real-time implementation is verified through hardware experiments using car-like mobile robots.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"160 ","pages":"Article 106319"},"PeriodicalIF":5.4,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143715532","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

Enhanced robust performance oriented integrated planning and control of a robotic manipulator with online instantaneous violent disturbances

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2025-03-27 DOI: 10.1016/j.conengprac.2025.106326

Zixuan Huo, Mingxing Yuan, Junsheng Huang, Shuaikang Zhang, Xuebo Zhang

{"title":"Enhanced robust performance oriented integrated planning and control of a robotic manipulator with online instantaneous violent disturbances","authors":"Zixuan Huo, Mingxing Yuan, Junsheng Huang, Shuaikang Zhang, Xuebo Zhang","doi":"10.1016/j.conengprac.2025.106326","DOIUrl":"10.1016/j.conengprac.2025.106326","url":null,"abstract":"<div><div>Robotic manipulators suffer from various modeling uncertainties, which generally deteriorate motion control performance. These uncertainties are commonly treated as a lumped disturbance which is then addressed by those disturbance estimation and attenuation control (DEAC) approaches. Although existing DEAC algorithms have shown their effectiveness of rejecting normal disturbances with moderate amplitudes and slow variations, they cannot work well in the presence of an instantaneous violent disturbance (IVD). An IVD is typically characterized by the short duration and large amplitude which exceeds the control input limit of a robotic joint. Consequently, the actual trajectory of a robotic manipulator will deviate from its desired trajectory significantly. Given this issue, an enhanced robust performance oriented two-loop framework which integrates minimum-time trajectory planning and nonlinear control is proposed in this paper. Specifically, a nonlinear adaptive robust controller is synthesized in the inner loop to handle both structured and unstructured uncertainties, while a synchronized trajectory planning algorithm is devised in the outer loop to force the deviated trajectory converging to the desired trajectory in minimum time. Comparative experiments on a robotic manipulator under IVDs show that the deviated trajectory is recovered fastest by the proposed approach.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"160 ","pages":"Article 106326"},"PeriodicalIF":5.4,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704872","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

Shifting strategy for electric heavy trucks with automated manual transmission based on extended Kalman filtering and reinforcement learning

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2025-03-26 DOI: 10.1016/j.conengprac.2025.106324

Mingwei Zhou, Dongye Sun, Can Wang, Jiezhong Wang

{"title":"Shifting strategy for electric heavy trucks with automated manual transmission based on extended Kalman filtering and reinforcement learning","authors":"Mingwei Zhou, Dongye Sun, Can Wang, Jiezhong Wang","doi":"10.1016/j.conengprac.2025.106324","DOIUrl":"10.1016/j.conengprac.2025.106324","url":null,"abstract":"<div><div>To enhance the energy efficiency of electric vehicles and improve their adaptability to dynamic driving environments, this study utilized multigear automated manual transmission (AMT) electric heavy trucks as the research object and proposed an optimal system efficiency shifting strategy based on extended Kalman filtering and deep deterministic policy gradient (EKF-DDPG) algorithm correction. First, based on the integrated bond graph model, the loss mechanism and dynamic efficiency characteristics of the electric drive system were analyzed, and a shifting strategy based on the optimal system efficiency was developed. Second, considering the influence of vehicle mass and slope, the EKF-DDPG algorithm was used to correct the shifting strategy based on optimal system efficiency offline. Finally, the effectiveness and superiority of the proposed strategy were verified through a combination of simulations and real vehicle experiments. The research results indicate that the proposed strategy achieves real-time control of the optimal output efficiency of the electric drive system, correction of the shift curve to decrease cyclic shifting in dynamic driving environments, and a 4.45% reduction in vehicle energy consumption compared to traditional economical shifting strategy.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"160 ","pages":"Article 106324"},"PeriodicalIF":5.4,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697950","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

Data-driven Koopman model predictive control for the integrated thermal management of electric vehicles

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2025-03-26 DOI: 10.1016/j.conengprac.2025.106323

Youyi Chen, Kyoung Hyun Kwak, Dewey D. Jung, Youngki Kim

{"title":"Data-driven Koopman model predictive control for the integrated thermal management of electric vehicles","authors":"Youyi Chen, Kyoung Hyun Kwak, Dewey D. Jung, Youngki Kim","doi":"10.1016/j.conengprac.2025.106323","DOIUrl":"10.1016/j.conengprac.2025.106323","url":null,"abstract":"<div><div>The thermal management system (TMS) of electric vehicles (EVs) consumes a considerable amount of energy, and hence its optimal control is crucial for enhancing EV driving range. However, the complexity of an integrated TMS and its varying operation modes bring challenges for real-time optimal control. The assumptions and simplifications adopted for developing computationally inexpensive physics-based control-oriented models often result in prediction errors. To address the impact of model errors, this study proposes a Koopman-based model predictive control (MPC) approach for the integrated TMS operation in EVs, which includes a cooling mode change. Koopman prediction models are developed based on the Extended Dynamic Mode Decomposition (EDMD) structure utilizing data collected from high-fidelity MATLAB/Simulink® simulations. For the selection of Koopman models, a corrected Akaike Information Criterion (<span><math><mrow><mi>A</mi><mi>I</mi><msub><mrow><mi>C</mi></mrow><mrow><mi>c</mi></mrow></msub></mrow></math></span>) is applied to thirteen candidates. In addition, the prediction performance of the selected models is evaluated by examining open-loop simulation errors during the cooling mode change with different prediction lengths. These selected Koopman models are then implemented in a Quadratic Programming (QP)-based MPC structure. The corresponding controllers are integrated into the high-fidelity MATLAB/Simulink® plant model and evaluated under four driving conditions. Compared with a nonlinear MPC (NMPC) baseline controller addressing the same optimal control problem, the chosen Koopman controller demonstrates improved temperature regulation performance and a 6.5% reduction in energy consumption. The Koopman controller reduces the computational time for each calculation, decreasing from 247 ms to 54 ms, compared to the NMPC controller.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"160 ","pages":"Article 106323"},"PeriodicalIF":5.4,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704871","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

Visual control of a cable-driven flexible robotic arm with a spinal structure based on video understanding

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2025-03-24 DOI: 10.1016/j.conengprac.2025.106303

Xiang Zhang , Xuesong Wu , Kangjia Fu , Chong Sun , Sunquan Yu , Qi Zhang , Teng Yi , Xiaoqian Chen

{"title":"Visual control of a cable-driven flexible robotic arm with a spinal structure based on video understanding","authors":"Xiang Zhang , Xuesong Wu , Kangjia Fu , Chong Sun , Sunquan Yu , Qi Zhang , Teng Yi , Xiaoqian Chen","doi":"10.1016/j.conengprac.2025.106303","DOIUrl":"10.1016/j.conengprac.2025.106303","url":null,"abstract":"<div><div>Flexible robotic arms have higher degrees of freedom, making them increasingly popular in tasks such as grasping in narrow environments. However, their high-compliance characteristics and occlusion in the environment pose considerable challenges in the precise control consistent with human requirements. This study combines vision-based knowledge representation with a large language model to help a cable-driven flexible robotic arm with a spinal structure better understand human intentions and mimic human actions. In particular, a visual servo system closely coupled with the flexible robotic arm is designed, which can effectively reduce the impact of occlusion on visual positioning. In a narrow experimental environment, the recognition accuracy of the coupled visual dynamic adjustment system improved by 34.8% compared with relying solely on visual recognition from the end of the arm, and by 28.7% compared with using the external camera visual recognition alone. Subsequently, aimed to perform fine manipulations of the flexible robotic arm, a data-driven nonlinear modeling method is proposed and a coarse-to-fine visual grasping control system is designed. Experiments across eight task scenarios validate the precise control and interactivity of the system in narrow environments using a flexible robotic arm with a spinal structure.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"160 ","pages":"Article 106303"},"PeriodicalIF":5.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679368","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

Unit load prediction method based on weighted just-in-time learning with spatio-temporal characteristics for gas boiler power generation process

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2025-03-23 DOI: 10.1016/j.conengprac.2025.106325

Yan Xu, Min Wu, Jie Hu, Sheng Du, Wen Zhang, Fusheng Peng, Huihang Li, Wenshuo Song

{"title":"Unit load prediction method based on weighted just-in-time learning with spatio-temporal characteristics for gas boiler power generation process","authors":"Yan Xu, Min Wu, Jie Hu, Sheng Du, Wen Zhang, Fusheng Peng, Huihang Li, Wenshuo Song","doi":"10.1016/j.conengprac.2025.106325","DOIUrl":"10.1016/j.conengprac.2025.106325","url":null,"abstract":"<div><div>Load management of gas boiler generator units in metallurgical captive power plants heavily relies on operators’ coordination and scheduling. The operating load fluctuates frequently due to varying electricity demands across production processes and its sensitivity to multiple operational parameters. To accurately predict unit load, promptly reflect load changes, and ensure stable operation, we propose a unit load prediction model utilizing a weighted just-in-time learning algorithm with consideration of key parameter spatio-temporal characteristics (WJITL-ST) and a long short-term memory network with temporal pattern attention (TPA-LSTM) mechanism. First, we thoroughly analyze the process mechanism and use the maximum information coefficient to identify and select the variables most relevant to unit load as model inputs. Next, the WJITL-ST method, based on data segment retrieval, selects historical data most similar to the retrieved segments for online local modeling. The TPA-LSTM algorithm is then used to model and predict unit load. Finally, experiments using actual production data from a 150MW gas boiler generator unit in a metallurgical captive power plant show that the proposed method achieves higher prediction accuracy, and demonstrates superior performance under fluctuating operating conditions.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"160 ","pages":"Article 106325"},"PeriodicalIF":5.4,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679367","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

A double-loop robust position and speed control of an electromagnetic actuator against nonlinear model uncertainties

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2025-03-22 DOI: 10.1016/j.conengprac.2025.106318

Han-Sol Lee , Yeongoh Ko , Juhoon Back , Keum-Shik Hong , Chang-Sei Kim

{"title":"A double-loop robust position and speed control of an electromagnetic actuator against nonlinear model uncertainties","authors":"Han-Sol Lee , Yeongoh Ko , Juhoon Back , Keum-Shik Hong , Chang-Sei Kim","doi":"10.1016/j.conengprac.2025.106318","DOIUrl":"10.1016/j.conengprac.2025.106318","url":null,"abstract":"<div><div>Electromagnetic actuation (EMA) has been a reliable and powerful approach for the wireless manipulation of a small-sized robot in recent years. EMA with controllers for microrobot manipulation has been validated in various studies and demonstrated high precision in static environments. Nevertheless, from a control perspective, challenges remain in effectively compensating for lumped disturbances, which include nonlinear model uncertainties and unpredictable dynamics of target objects, thereby ensuring reliable performance in practical applications. In this article, we propose a double-loop robust controller to address these challenges. The proposed control architecture consists of an outer-loop sliding mode control (SMC) and an inner-loop disturbance observer (DOB). This controller functions as a force compensator, where the SMC provides primary feedback control for the motion of the magnetic object, and the DOB estimates and compensates for lumped disturbance forces. The controller's parameters and driving performance were comprehensively analyzed through dynamic simulations of the target object. The potential for practical applications was validated through experiments. The results demonstrated that position control accuracy reached 0.4 mm at a rated speed within 1 mm/s, showing a 71 % improvement compared to conventional control. Additionally, the comparative speed control performance could achieve a maximum speed of 11.4 mm/s resulting in relative effectiveness compared to other control approaches.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"160 ","pages":"Article 106318"},"PeriodicalIF":5.4,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679366","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

A novel automated soft sensor design tool for industrial applications based on machine learning

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2025-03-20 DOI: 10.1016/j.conengprac.2025.106322

Liang Cao , Jianping Su , Emilio Conde , Lim C. Siang , Yankai Cao , Bhushan Gopaluni

{"title":"A novel automated soft sensor design tool for industrial applications based on machine learning","authors":"Liang Cao , Jianping Su , Emilio Conde , Lim C. Siang , Yankai Cao , Bhushan Gopaluni","doi":"10.1016/j.conengprac.2025.106322","DOIUrl":"10.1016/j.conengprac.2025.106322","url":null,"abstract":"<div><div>In modern industrial processes, real-time monitoring and control of key quality variables are crucial but challenging due to measurement limitations and process complexities. Traditional methods for developing soft sensor models are not only time-consuming and labor-intensive but also require substantial expertise in machine learning, and often lack user-friendly interfaces, thereby limiting their accessibility to engineers in the field. To address these issues, this paper introduces an easy-to-use, open and efficient automated soft sensor design tool called Soft Sensor Manager. The Soft Sensor Manager incorporates advanced supervised, semi-supervised, and causal machine learning algorithms to enable effective model development and deployment. It also provides functionalities such as data preprocessing, feature engineering, algorithm selection, hyperparameter optimization, model evaluation and online deployment within a user-friendly interface. The software’s effectiveness was demonstrated through its application in predicting light catalytic cracked oil yield using real industrial data. By automating the soft sensor design process, the Soft Sensor Manager enhances modeling efficiency and model quality, ultimately contributing to improved process monitoring and optimization in industrial settings.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"160 ","pages":"Article 106322"},"PeriodicalIF":5.4,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679362","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

Robust temperature control of a diesel oxidation catalyst using continuous terminal sliding mode with extended state observer

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2025-03-16 DOI: 10.1016/j.conengprac.2025.106316

Tiexiong Huang , Dayong Li , Juan Lu , Xiaoxue Luo , Wei He

{"title":"Robust temperature control of a diesel oxidation catalyst using continuous terminal sliding mode with extended state observer","authors":"Tiexiong Huang , Dayong Li , Juan Lu , Xiaoxue Luo , Wei He","doi":"10.1016/j.conengprac.2025.106316","DOIUrl":"10.1016/j.conengprac.2025.106316","url":null,"abstract":"<div><div>Robust temperature control is essential for the diesel oxidation catalyst (DOC) in modern diesel engines to realize efficient and reliable thermal regenerations in real driving cycles. This paper investigates the design and validation of an improved robust control solution for the DOC-outlet temperature control by applying the error-based active disturbance rejection control (ADRC) method and the non-singular terminal sliding mode (TSM) scheme along with a second-order sliding mode approach. The second-order TSM scheme is introduced to substitute the proportional–derivative (PD) control in the conventional ADRC framework. A finite-time convergence of the sliding mode dynamics is consequently guaranteed and the tracking errors are driven to zeros asymptotically, which accordingly implies that a faster temperature response is expected. Moreover, the undesired chattering effect is eliminated, resulting in an inherently continuous control input signal. The extended state observer (ESO) is synthesized as a feedforward compensator to estimate and reject the total disturbance. Theoretical convergence and stability of the proposed control strategy are analyzed based on the Lyapunov approach. Selection of the controller parameters is further discussed as well. The effectiveness and robustness are finally examined through an extensive comparative study using simulation and experimental tests. Results demonstrate that the proposed continuous TSM control achieves superior temperature control performance, including faster disturbance suppression, more accurate set-point tracking, and greater robustness against internal model uncertainties and external disturbances compared to the conventional ADRC controller.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"159 ","pages":"Article 106316"},"PeriodicalIF":5.4,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143629183","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

Bumpless transfer control for DC-DC buck-boost converter modeled by switched affine systems

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2025-03-16 DOI: 10.1016/j.conengprac.2025.106320

Xiaozeng Xu , Yanzheng Zhu , Fen Wu , Xinkai Chen , Chun-Yi Su

{"title":"Bumpless transfer control for DC-DC buck-boost converter modeled by switched affine systems","authors":"Xiaozeng Xu , Yanzheng Zhu , Fen Wu , Xinkai Chen , Chun-Yi Su","doi":"10.1016/j.conengprac.2025.106320","DOIUrl":"10.1016/j.conengprac.2025.106320","url":null,"abstract":"<div><div>This study focuses on the bumpless transfer control for a DC-DC buck-boost converter, which is modeled as a class of discrete-time switched affine systems. While achieving smooth transitions between operating modes is a well-established goal in the literature, this work introduces a novel approach to tackle these challenges through innovative methodologies. A shifted-point-dependent controller and a state-dependent switching law are developed, ensuring convergence to a union of regions encompassing shifted points and mitigating oscillations during switching events. A novel multi-shifted-point-dependent Lyapunov functional is proposed, integrating bump limitation constraints, and sufficient conditions are derived for the solvability of the bumpless transfer control problem. Furthermore, the methodology is generalized to switched affine systems with uncertainties, and the corresponding robust stability conditions are provided. Finally, the superiority of the proposed approach is demonstrated through a experimental verification of a DC-DC buck-boost converter.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"159 ","pages":"Article 106320"},"PeriodicalIF":5.4,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143629182","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