Control Engineering Practice最新文献_第3页

An improved multi-channel and multi-scale domain adversarial neural network for fault diagnosis of the rolling bearing 用于滚动轴承故障诊断的改进型多通道多尺度域对抗神经网络

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2024-10-21 DOI: 10.1016/j.conengprac.2024.106120

Yongze Jin , Xiaohao Song , Yanxi Yang , Xinhong Hei , Nan Feng , Xubo Yang

{"title":"An improved multi-channel and multi-scale domain adversarial neural network for fault diagnosis of the rolling bearing","authors":"Yongze Jin , Xiaohao Song , Yanxi Yang , Xinhong Hei , Nan Feng , Xubo Yang","doi":"10.1016/j.conengprac.2024.106120","DOIUrl":"10.1016/j.conengprac.2024.106120","url":null,"abstract":"<div><div>To improve the fault diagnosis accuracy of rolling bearings under diverse working conditions, an improved domain adversarial neural network is proposed, the feature extraction module is reconstructed by multi-channel and multi-scale CNN-LSTM-ECA (MMCLE) in the proposed network. The MMCLE module consists of several key components. Firstly, the multi-channel multi-scale Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) are established to extract spatial features and temporal dependencies of the input data. Then, the Efficient Channel Attention (ECA) module is introduced to weight the effective feature channels. Finally, the domain adversarial training is employed to extract common features from both the source and target domains. By minimizing the domain offset between these domains, the faults of rolling bearing under diverse working conditions can be accurately diagnosed. The simulation results show that, based on the proposed MMCLE model, the domain offset issue can be effectively addressed, and the fault diagnosis accuracy can be improved for samples in the target domain under diverse working conditions. The accuracy and feasibility of the proposed method can be effectively verified.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532941","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 high-performance model predictive torque control concept for induction machines for electric vehicle applications 用于电动汽车感应机的高性能模型预测扭矩控制概念

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2024-10-19 DOI: 10.1016/j.conengprac.2024.106128

Georg Janisch , Andreas Kugi , Wolfgang Kemmetmüller

引用次数: 0

A reusable decoder network penalized by smooth group lasso and its applications to large-scale fault diagnosis of machinery 用平滑组套索惩罚的可重复使用解码器网络及其在大规模机械故障诊断中的应用

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2024-10-17 DOI: 10.1016/j.conengprac.2024.106127

Zhiqiang Zhang, Hongji He, Shuiqing Xu, Lisheng Yin, Xueping Dong

{"title":"A reusable decoder network penalized by smooth group lasso and its applications to large-scale fault diagnosis of machinery","authors":"Zhiqiang Zhang, Hongji He, Shuiqing Xu, Lisheng Yin, Xueping Dong","doi":"10.1016/j.conengprac.2024.106127","DOIUrl":"10.1016/j.conengprac.2024.106127","url":null,"abstract":"<div><div>Representation learning approaches have achieved great success in fault diagnosis of large-scale mechanical data, among which the popular auto-encoder method has developed a series of effective variants. In the existing variants, the encoder network is re-employed to encode feature representations of the data, while the decoder network is directly discarded after training, leading to a regrettable waste of computational resources. Instead of proposing advanced variants of the auto-encoder, this paper explicitly penalizes the decoder network with group lasso, thereby transforming waste into treasure. Specifically, the group lasso constrains the column vectors of the decoder network’s weight matrix at the group level, making them reusable for feature selection. Moreover, a smooth function is utilized to approximate the group lasso to prevent numerical oscillations when computing the gradients. The simulated data and experimental gear data are sequentially used to verify the effectiveness of the smooth group lasso through investigations on two representative auto-encoder variants. The results show that the decoder network penalized by smooth group lasso can be re-utilized to guide selection of a subset of key features for training a classifier, exhibiting an extraordinary feature selection capability.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142446180","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

Event-triggered formation control with obstacle avoidance for multi-agent systems applied to multi-UAV formation flying 应用于多无人机编队飞行的具有避障功能的多代理系统事件触发编队控制

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2024-10-16 DOI: 10.1016/j.conengprac.2024.106105

Liang Han , Yue Wang , Ziwei Yan , Xiaoduo Li , Zhang Ren

{"title":"Event-triggered formation control with obstacle avoidance for multi-agent systems applied to multi-UAV formation flying","authors":"Liang Han , Yue Wang , Ziwei Yan , Xiaoduo Li , Zhang Ren","doi":"10.1016/j.conengprac.2024.106105","DOIUrl":"10.1016/j.conengprac.2024.106105","url":null,"abstract":"<div><div>This study investigates time-varying formation control with communication constraint for general discrete-time multi-agent systems (MASs), which aims to control a swarm of agents to maintain a desired formation while avoiding obstacles in the scenario with spatial constraint. The event-triggered mechanism is introduced to effectively reduce the system communication frequency and an artificial potential field function is incorporated into the proposed controller to achieve obstacle avoidance in formation. The obtained results are applied to solve obstacle avoidance problems for multiple unmanned aerial vehicles (UAVs) in formation flight. Physical simulations are completed with four UAV models on a 3-D visualization simulation platform integrated by Robot Operating System (ROS) and Gazebo. Then, practical experiments are carried out with four quadrotors in a complex experimental scenario combined with the motion capture system. The physical simulation and practical experiments are implemented to verify the effectiveness of the theoretical results.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442895","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 model predictive control of path-following for autonomous vehicles towards model mismatch and uncertainty 面向模型不匹配和不确定性的自动驾驶汽车路径跟踪实时模型预测控制

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2024-10-16 DOI: 10.1016/j.conengprac.2024.106126

Wenqiang Zhao , Hongqian Wei , Qiang Ai , Nan Zheng , Chen Lin , Youtong Zhang

{"title":"Real-time model predictive control of path-following for autonomous vehicles towards model mismatch and uncertainty","authors":"Wenqiang Zhao , Hongqian Wei , Qiang Ai , Nan Zheng , Chen Lin , Youtong Zhang","doi":"10.1016/j.conengprac.2024.106126","DOIUrl":"10.1016/j.conengprac.2024.106126","url":null,"abstract":"<div><div>The path following function is a critical component of functional safety for autonomous vehicles, and following precision has garnered increased attention in practical applications. However, control performance can be compromised due to uncertainties in vehicle parameters and discrepancies between the control model and the actual vehicle to be controlled. To address this, a real-time model predictive control for path following of autonomous vehicles is proposed, incorporating an estimation of model mismatch. An adaptive extended Kalman filter is developed to estimate the potential model mismatch terms, and state deviations are compensated accordingly. Subsequently, a parameter-varying model predictive controller is formulated to achieve unbiased path-following control while maintaining robustness to parameter variations. Simulation results demonstrate a significant improvement in lateral following accuracy, with enhancements of 53.85%, 47.83%, and 42.86% compared to the nonlinear model predictive control, robust model predictive control, and learning-based control, respectively. The hardware-in-the-loop and real-road experiments further validate the excellent real-time executability, with a maximum time cost of 12.4 ms, accounting for 62% of the sampling period.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142438250","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

Multi-model integration for predicting circulating load ratio based on clustering SAG milling operating conditions 基于聚类 SAG 磨削操作条件的循环负载率预测多模型集成

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2024-10-15 DOI: 10.1016/j.conengprac.2024.106129

Zhenhong Liao , Ce Xu , Wen Chen , Feng Wang , Jinhua She

{"title":"Multi-model integration for predicting circulating load ratio based on clustering SAG milling operating conditions","authors":"Zhenhong Liao , Ce Xu , Wen Chen , Feng Wang , Jinhua She","doi":"10.1016/j.conengprac.2024.106129","DOIUrl":"10.1016/j.conengprac.2024.106129","url":null,"abstract":"<div><div>Grinding in mineral processing is used to control the ore at the technically feasible and economically optimum particle size to achieve mineral liberation for separation. A circulating-load ratio (CLR) during a semi-autogenous grinding (SAG) milling process is critical for controlling particle size and energy consumption. This paper presents a CLR-prediction model based on clustering SAG milling operating conditions. First, operating parameters affecting the CLR are identified by comprehensively analyzing the complex mechanism and characteristics of a typical industrial SAG milling process. Next, a method is developed to cluster operating conditions of the SAG milling process based on the power consumption and CLR of the process. The method reveals the actual physical significance of each operating condition. Then, support vector regression (SVR) is used to model the CLR in each operating condition. After that, a distance-based model integration strategy is designed to determine the weights of each SVR model to predict the CLR. Finally, integrating the SVR submodels yields a CLR prediction model. Actual run data demonstrated the accuracy and effectiveness of the model in predicting CLR. This method has significant practical value for improving SAG milling efficiency via its utilization in control system design.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142438252","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

Effectiveness of cooperative yaw control based on reinforcement learning for in-line multiple wind turbines 基于强化学习的在线多风力涡轮机协同偏航控制的有效性

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2024-10-15 DOI: 10.1016/j.conengprac.2024.106124

Longyan Wang , Qiang Dong , Yanxia Fu , Bowen Zhang , Meng Chen , Junhang Xie , Jian Xu , Zhaohui Luo

{"title":"Effectiveness of cooperative yaw control based on reinforcement learning for in-line multiple wind turbines","authors":"Longyan Wang , Qiang Dong , Yanxia Fu , Bowen Zhang , Meng Chen , Junhang Xie , Jian Xu , Zhaohui Luo","doi":"10.1016/j.conengprac.2024.106124","DOIUrl":"10.1016/j.conengprac.2024.106124","url":null,"abstract":"<div><div>Wind farm wake interactions are critical determinants of overall power generation efficiency. To address these challenges, coordinated yaw control of turbines has emerged as a highly effective strategy. While conventional approaches have been widely adopted, the application of contemporary machine learning techniques, specifically reinforcement learning (RL), holds great promise for optimizing wind farm control performance. Considering the scarcity of comparative analyses for yaw control approaches, this study implements and evaluates classical greedy, optimization-based, and RL policies for in-line multiple wind turbine under various wind scenario by an experimentally validated analytical wake model. The results unambiguously establish the superiority of RL over greedy control, particularly below rated wind speeds, as RL optimizes yaw trajectories to maximize total power capture. Furthermore, the RL-controlled policy operates without being hampered by iterative modeling errors, leading to a higher cumulative power generation compared to the optimized control scheme during the control process. At lower wind speeds (5 m/s), it achieves a remarkable 32.63 % improvement over the optimized strategy. As the wind speed increases, the advantages of RL control gradually diminish. In consequence, the model-free adaptation offered by RL control substantially bolsters robustness across a spectrum of changing wind scenarios, facilitating seamless transitions between wake steering and alignment in response to evolving wake physics. This analysis underscores the significant advantages of data-driven RL for wind farm yaw control when compared to traditional methods. Its adaptive nature empowers the optimization of total power production across a range of diverse operating regimes, all without the need for an explicit model representation.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142438251","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

Multivariable switching control of a compliant piezoelectric microgripper with force/position interaction interferences 具有力/位置相互作用干扰的顺应式压电微型夹持器的多变量开关控制

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2024-10-12 DOI: 10.1016/j.conengprac.2024.106102

Gaohua Wu , Yiling Yang , Yuguo Cui , Guoping Li , Yanding Wei

{"title":"Multivariable switching control of a compliant piezoelectric microgripper with force/position interaction interferences","authors":"Gaohua Wu , Yiling Yang , Yuguo Cui , Guoping Li , Yanding Wei","doi":"10.1016/j.conengprac.2024.106102","DOIUrl":"10.1016/j.conengprac.2024.106102","url":null,"abstract":"<div><div>This paper presents multivariable switching control of a piezoelectric microgripper regarding its output displacement, gripping force, and manipulated position. Unlike existing microgripper control, it simultaneously regulates force/position variables. Meanwhile, force/position interaction interferences and signal itself overshooting are suppressed. Firstly, a symmetrical microgripper with two independent gripping arms is introduced. Then, a generalized dynamic model is established by considering structural dynamics, electromechanical coupling, and force/position interaction. After that, multivariable switching control is proposed to achieve clamp-carry-release manipulation using dual-input and dual-output (DIDO) perturbation displacement and force/position controllers. Finally, various switching experiments are conducted, demonstrating that force/position interaction interferences are reduced by 83.76 % and 87.51 %, and interference-suppression time is shortened from 0.86 s and 0.70 s to 0.49 s and 0.41 s. Also, overshoots of gripping force and position are eliminated with a smaller settling time. The proposed multivariable switching control exhibits superior regulation performance, guaranteeing manipulation accuracy and stability.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142423369","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 framework for joint vehicle localization and road mapping using onboard sensors 利用车载传感器进行联合车辆定位和道路测绘的框架

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2024-10-10 DOI: 10.1016/j.conengprac.2024.106112

Karl Berntorp, Marcus Greiff

{"title":"A framework for joint vehicle localization and road mapping using onboard sensors","authors":"Karl Berntorp, Marcus Greiff","doi":"10.1016/j.conengprac.2024.106112","DOIUrl":"10.1016/j.conengprac.2024.106112","url":null,"abstract":"<div><div>This paper presents a modeling framework for joint estimation of a host vehicle state and a map of the road based on global navigation satellite system (GNSS) and camera measurements. We model the road using a spline representation based on lower-dimensional Bézier curves parametrized in <em>generalized endpoints</em> (GEPs) with implicit guarantees of continuous lane boundaries. We model the GEPs by a parameter vector having a Gaussian prior representing the uncertainty of the prior map, and provide a systematic way of defining this prior from generic map representations. Both GNSS and camera measurements, such as lane-mark measurements, have noise characteristics that vary in time. To adapt to the changing noise levels and hence improve positioning performance, we formulate the problem as a joint vehicle state, map parameter, and noise covariance estimation problem and present two noise-adaptive linear-regression Kalman filters (LRKFs); (i) an interacting multiple-model (IMM) LRKF and (ii) a variational-Bayes (VB) LRKF. We conduct a Monte-Carlo study and compare the two approaches in terms of estimation precision and computation times. Embedded implementations in an automotive-grade dSpace Micro Autobox-II indicate the real-time validity of both approaches, with turn-around times of between 2–<span><math><mrow><mn>80</mn><mspace></mspace><mi>ms</mi></mrow></math></span>, depending on the problem size and if the map is updated. The results indicate that while the IMM-LRKF shows marginally better estimation accuracy, the VB-LRKF is at least a factor of 2 faster.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142423367","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

Obstacle avoidance method based on reinforcement learning dual-layer decision model for AGV with visual perception 基于强化学习双层决策模型的视觉感知 AGV 避障方法

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2024-10-10 DOI: 10.1016/j.conengprac.2024.106121

Jun Nie , Guihua Zhang , Xiao Lu , Haixia Wang , Chunyang Sheng , Lijie Sun

{"title":"Obstacle avoidance method based on reinforcement learning dual-layer decision model for AGV with visual perception","authors":"Jun Nie , Guihua Zhang , Xiao Lu , Haixia Wang , Chunyang Sheng , Lijie Sun","doi":"10.1016/j.conengprac.2024.106121","DOIUrl":"10.1016/j.conengprac.2024.106121","url":null,"abstract":"<div><div>In this paper, the reinforcement learning dual-layer decision strategy-based obstacle avoidance method for AGV with visual perception is proposed. Initially, the complementary obstacle detection system is established by combining the radar and RGB-D camera. The global environment information is obtained through radar scanning, while the obstacles information within the near-field of view are detected by camera, thereby simplifying the data processing complexities associated with multi-sensor fusion. Subsequently, the perturbation factor is formulated based on the position information of obstacles detected by the RGB-D camera, directly participating in the action value estimation of the Critic Network and enhancing the collision avoidance capability of AGV. Finally, the dual-layer decision method incorporating Deep Deterministic Policy Gradient (DDPG) and Deep Q-Network (DQN) is developed to control the rotate angle, ensuring the credibility of the steering decisions. Experiment results demonstrate that the proposed obstacle avoidance method, utilizing the dual-layer decision model with visual perception, exhibits superior obstacle avoidance performance, faster convergence speed, and stronger stability.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142423368","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