Control Engineering Practice最新文献_第6页

Monitoring large-scale industrial systems for wastewater treatment processes with process noise using data-driven NARX approach 使用数据驱动的NARX方法监测大型工业系统的废水处理过程噪音

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2025-04-02 DOI: 10.1016/j.conengprac.2025.106321

Wentao Liu , Shaoyuan Li

{"title":"Monitoring large-scale industrial systems for wastewater treatment processes with process noise using data-driven NARX approach","authors":"Wentao Liu , Shaoyuan Li","doi":"10.1016/j.conengprac.2025.106321","DOIUrl":"10.1016/j.conengprac.2025.106321","url":null,"abstract":"<div><div>Wastewater treatment processes (WWTPs) are large-scale systems comprising multiple biological reactors, which are essential for preventing water pollution and promoting water reuse. Safety assessment and accurate process monitoring are crucial for maintaining the effluent quality of WWTPs. However, the presence of uncertainties and process noise degrades the performance of fault detection models, posing significant challenges to reliable monitoring. This paper proposes a data-driven fault detection framework for monitoring failures in wastewater treatment processes affected by impulsive noise. The fault detection model employs nonlinear autoregressive with exogenous input (NARX) neural networks to construct the residual generator with the aid of robust continuous mixed <span><math><mi>p</mi></math></span>-norm optimization. Robust continuous mixed <span><math><mi>p</mi></math></span>-norm combines multiple error <span><math><mi>p</mi></math></span>-norms to enhance the cost function with diverse error information, minimizing it to produce adaptive gains that adjust the training gain based on data quality at each step. When impulsive noise occurs, the correction term for parameter estimation approaches zero, enabling the model to achieve greater robustness against impulsive noise compared to existing methods. Additionally, the fault detection model incorporates an adaptive moment estimation-based variable-step algorithm to enhance convergence by adaptively adjusting the learning rate. The proposed method is applied to the benchmark simulation model no. 1, and experimental results demonstrate that it achieves accurate detection rates for monitoring WWTPs.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"161 ","pages":"Article 106321"},"PeriodicalIF":5.4,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760951","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

Deep Reinforcement Learning design of safe, stable and robust control for sloshing-affected space launch vehicles 受晃动影响的航天运载火箭安全稳定鲁棒控制的深度强化学习设计

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2025-04-01 DOI: 10.1016/j.conengprac.2025.106328

Périclès Cocaul , Sylvain Bertrand , Hélène Piet-Lahanier , Lori Lemazurier , Martine Ganet

{"title":"Deep Reinforcement Learning design of safe, stable and robust control for sloshing-affected space launch vehicles","authors":"Périclès Cocaul , Sylvain Bertrand , Hélène Piet-Lahanier , Lori Lemazurier , Martine Ganet","doi":"10.1016/j.conengprac.2025.106328","DOIUrl":"10.1016/j.conengprac.2025.106328","url":null,"abstract":"<div><div>New challenges in spatial missions and the design of new launchers entail a focus on innovative control strategies. Recent developments in Machine Learning (ML) for optimization processes shed light on the possibilities offered for controlling complex nonlinear partially unknown systems. This work focuses on the use of these methods to design control laws stabilizing the sloshing of propellants in tanks during launcher flight. A major hurdle in applying control laws designed by Artificial Intelligence (AI) to safety-critical systems lies in certifying stability and safety. Using Control Lyapunov Function (CLF) and Control Barrier Function (CBF) developed in Control Theory approaches, closed-loop stability and safety in terms of state constraints can be verified. Considering a Deep Reinforcement Learning (DRL) framework, an algorithm is developed to learn a control policy along with stability and safety certificates. The CLF and CBF conditions are integrated in the DRL algorithm, bridging the gap between Control Theory and Machine Learning techniques. A safe and stable DRL controller is then learned on a simulated launcher subject to sloshing with uncertainties and perturbations due to sloshing. A robustness study with Monte Carlo simulations is conducted to evaluate performance under various conditions. Finally, the developed controller is validated on an industrial simulator that more accurately models the real behavior of the launcher. Despite not being trained on this industrial simulator, the controller matches control objectives, demonstrating robustness and performance.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"161 ","pages":"Article 106328"},"PeriodicalIF":5.4,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747988","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

Ultra-local dual-torque model for model-free predictive torque control without weighting factors in induction motor drives 感应电机驱动无模型无权重因子预测转矩控制的超局部双转矩模型

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2025-03-31 DOI: 10.1016/j.conengprac.2025.106327

Anxin Yang , Ziguang Lu , Jiangchao Qin

{"title":"Ultra-local dual-torque model for model-free predictive torque control without weighting factors in induction motor drives","authors":"Anxin Yang , Ziguang Lu , Jiangchao Qin","doi":"10.1016/j.conengprac.2025.106327","DOIUrl":"10.1016/j.conengprac.2025.106327","url":null,"abstract":"<div><div>Finite Control Set Model Predictive Control (FCS-MPC) primarily faces challenges related to parameter sensitivity and weighting factor design. To address these, this article proposes an ultra-local dual-torque model to establish a model-free predictive torque control (PTC) method without weighting factors for induction motor (IM) drives. Derived from the dynamic equations of electromagnetic and reactive torques, the proposed model can simplify the multivariable control of torque and flux in conventional PTC by focusing on univariate torque control. Since the torque prediction is performed directly rather than indirectly, the complexity of developing two independent ultra-local models of stator flux and current in model-free PTC is avoided. The constructed cost function relies on the torque and its dual quantity, avoiding the need for tuning the weighting factor. To enhance robustness against parameter mismatches, a linear extended state observer (LESO) is employed to identify both known and unknown system parts. Additionally, a novel flux observer is designed to mitigate low-speed performance degradation due to stator resistance. The proposed method is compared with conventional PTC and model-free PTC. The simulation and experimental results demonstrate that the proposed method has superior performance in parameter adaptability, torque ripple minimization, current harmonic reduction, and effective very low-speed operation.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"160 ","pages":"Article 106327"},"PeriodicalIF":5.4,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739691","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 propagation and recovery of supply-demand imbalance in a metro system 数据驱动的地铁系统供需失衡传播与恢复

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2025-03-31 DOI: 10.1016/j.conengprac.2025.106339

Yue Gao , Xiaowei Cheng , Yiyang Chen , Junwei Wang

{"title":"Data-driven propagation and recovery of supply-demand imbalance in a metro system","authors":"Yue Gao , Xiaowei Cheng , Yiyang Chen , Junwei Wang","doi":"10.1016/j.conengprac.2025.106339","DOIUrl":"10.1016/j.conengprac.2025.106339","url":null,"abstract":"<div><div>The unforeseen imbalance between train supply and passenger demand in the metro system usually propagates along the running direction, which increases passengers’ travel costs and even seriously affects the metro system's safety and reliability. Data-driven propagation and recovery of supply-demand imbalance is thus important for a metro system. This paper proposes a two-layer imbalance propagation and recovery model for metro systems based on historical traffic data, where the affected time scope, the affected space scope, and the spatial-temporal extra waiting cost are outputted to describe the propagation and recovery mechanism. The lower-layer model calculates the passengers’ latest arrival time matrix for boarding each train. This matrix is an essential input parameter of the upper-layer imbalance propagation model, where the real-time extra waiting cost for each platform under disruptions is estimated. The proposed model is applied to a real-world metro line of Shenzhen metro to analyze its spatial-temporal propagation and recovery characteristics facing imbalance, which is of notable significance to the possible performance optimization and safety assessment for the metro system.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"161 ","pages":"Article 106339"},"PeriodicalIF":5.4,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738616","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 motion planning for autonomous vehicles based on environment and uncertainty-aware reachability prediction 基于环境和不确定性感知可达性预测的自动驾驶汽车鲁棒运动规划

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 面向增强鲁棒性能的在线瞬时暴力干扰下机械臂综合规划与控制

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 基于扩展卡尔曼滤波和强化学习的电动重型自动手动变速器换挡策略

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 数据驱动的Koopman模型预测控制在电动汽车综合热管理中的应用

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 基于视频理解的带脊柱结构的电缆驱动柔性机械臂视觉控制

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 基于时空特征加权实时学习的燃气锅炉发电机组负荷预测方法

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