Advanced Control for Applications最新文献_第2页

Vehicle vibration test platform structure design and control strategy optimization 车辆振动测试平台结构设计与控制策略优化

Advanced Control for Applications Pub Date : 2024-04-28 DOI: 10.1002/adc2.214

Zhiqiang Xi, Yongzheng Guo, Yiliu Wang, Kui Liu, Haiyang Yang, Zhanzheng Guo, Shuai Zhang

{"title":"Vehicle vibration test platform structure design and control strategy optimization","authors":"Zhiqiang Xi, Yongzheng Guo, Yiliu Wang, Kui Liu, Haiyang Yang, Zhanzheng Guo, Shuai Zhang","doi":"10.1002/adc2.214","DOIUrl":"https://doi.org/10.1002/adc2.214","url":null,"abstract":"For the needs of vehicle vibration test platform with high precision, large load capacity and fast response, the three-dimensional model design and analysis of vehicle vibration test platform are carried out; in order to improve the motion performance of the platform, a vibration test plat-form control strategy combining hybrid heuristic algorithm and PID control is proposed. Based on the designed 3D model parameters, the single-channel mathematical model of the servo-electric cylinder is derived and a hybrid heuristic algorithm PID optimization model is established to compare and analyze the control performance of the platform with the Ziegler-Nichols method PID. The results show that the step system overshoot is 3.80% and the dynamic performance of the system is significantly improved when the hybrid heuristic algorithm PID control is used. The simulation system model of vehicle vibration test platform control is established, and the operation results show that the platform is closer to the input signal in the spatial position change curve when the hybrid heuristic algorithm PID control is used. Its maximum displacement error is 0.09 mm, and the motion accuracy of the system is improved by 61% compared with the Ziegler-Nichols method PID control.","PeriodicalId":100030,"journal":{"name":"Advanced Control for Applications","volume":"6 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adc2.214","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142160271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Active underactuation fault-tolerant backstepping attitude tracking control of a satellite with interval error constraints 带间隔误差约束的卫星主动欠动容错反步态跟踪控制

Advanced Control for Applications Pub Date : 2024-04-25 DOI: 10.1002/adc2.215

Mohammad Zarourati, Mehran Mirshams, Morteza Tayefi

{"title":"Active underactuation fault-tolerant backstepping attitude tracking control of a satellite with interval error constraints","authors":"Mohammad Zarourati, Mehran Mirshams, Morteza Tayefi","doi":"10.1002/adc2.215","DOIUrl":"https://doi.org/10.1002/adc2.215","url":null,"abstract":"Underactuation poses a significant challenge to space mission control and performance. This article investigates the non-linear attitude tracking control problem for a remote sensing satellite underactuated by a reaction wheel (RW) actuator fault. First, a timeline close to the in-orbit reality of an underactuation fault is presented. Then, the fault detection and diagnosis strategy is performed in a finite-time decision window. The failed actuator is excluded from the control loop by forming the proposed reconfiguration window to transition from a 3 RWs configuration to 2 RWs. The underactuation fault-tolerant control is designed according to the active method, where the adaptive robust control law employed for fault-free conditions is switched to the underactuated attitude tracking control (UATC). The structure of UATC is based on kinematic and adaptive backstepping dynamic controllers. The effect of unknown bounded external disturbances is considered with an adaptive estimation term. The asymptotic stability of the closed-loop control system is proved via Lyapunov theory in the presence of parametric uncertainty. Due to the underactuation, a new approach proposed in the prescribed performance function is interval error constraints, which include the pointing accuracy and stability requirements in imaging time intervals. Finally, the results of the multidisciplinary simulation and experimental test confirm the applicability of the underactuation fault-tolerant control.","PeriodicalId":100030,"journal":{"name":"Advanced Control for Applications","volume":"6 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adc2.215","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142160200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Electrical line fault prediction using a novel grey wolf optimization algorithm based on multilayer perceptron 使用基于多层感知器的新型灰狼优化算法预测电气线路故障

Advanced Control for Applications Pub Date : 2024-04-24 DOI: 10.1002/adc2.213

Yufei Zhang

{"title":"Electrical line fault prediction using a novel grey wolf optimization algorithm based on multilayer perceptron","authors":"Yufei Zhang","doi":"10.1002/adc2.213","DOIUrl":"10.1002/adc2.213","url":null,"abstract":"Grey wolf optimization algorithm (GWO) has achieved great results in the optimization of neural network parameters. However, it has some problems such as insufficient precision, poor robustness, weak searching ability and easy to fall into local optimal solution. Therefore, a grey wolf optimization algorithm combining Levy flight and nonlinear inertia weights (LGWO) is proposed in this paper. The combination of Levy flight and nonlinear inertia weight is to improve the search efficiency and solve the problem that the search ability is weak and it is easy to fall into the local optimal solution. In summary, LGWO solves the problems of insufficient precision, poor robustness, weak searching ability and easy to fall into local optimal. This paper uses Congress on Evolutionary Computation benchmark function and combines algorithms with neural network for power line fault classification prediction to verify the effectiveness of each strategy improvement in LGWO and its comparison with other excellent algorithms (sine cosine algorithm, tree seed algorithm, wind driven optimization, and gravitational search algorithm). In the combination of neural networks and optimization algorithms, the accuracy of LGWO has been improved compared to the basic GWO, and LGWO has achieved the best performance in multiple algorithm comparisons.","PeriodicalId":100030,"journal":{"name":"Advanced Control for Applications","volume":"6 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adc2.213","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140664484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Lyapunov stability analysis and FIL implementation for boundary-based hybrid controller in boost converter 升压转换器中基于边界的混合控制器的 Lyapunov 稳定性分析和 FIL 实现

Advanced Control for Applications Pub Date : 2024-04-24 DOI: 10.1002/adc2.216

Hardik Patel, Ankit Shah

引用次数: 0

Concrete structure assembly technology based on 3D intelligent image analysis 基于三维智能图像分析的混凝土结构装配技术

Advanced Control for Applications Pub Date : 2024-04-21 DOI: 10.1002/adc2.211

Limei Cao, Xiao Song

{"title":"Concrete structure assembly technology based on 3D intelligent image analysis","authors":"Limei Cao, Xiao Song","doi":"10.1002/adc2.211","DOIUrl":"10.1002/adc2.211","url":null,"abstract":"At present, there is no standard practice for temporary connection of component installation. How to make temporary connections to components more efficient and accurate is the key to the construction of cast-in-place connection parts in concrete prefabricated buildings. In order to improve the assembly technology of concrete structures, this paper combines three-dimensional intelligent image analysis technology for simulation, analyzes and compares several common electronic image stabilization algorithms, discusses the video image stabilization technology based on gray projection method in detail, and gives experimental results. Moreover, this paper analyzes and compares common moving target tracking algorithms, such as feature-based tracking, region matching-based tracking, dynamic contour-based tracking, and 3D model-based tracking. In addition, this paper studies the target tracking algorithm based on the Camshift algorithm, and constructs the assembly model of the intelligent concrete structure with the support of the algorithm. Through experimental verification, it is known that the performance distribution of the concrete structure assembly model based on 3D intelligent image analysis in experimental evaluation is between [81, 89], the experimental study shows that the concrete structure assembly model based on 3D intelligent image analysis can effectively improve the assembly effect of concrete structure.","PeriodicalId":100030,"journal":{"name":"Advanced Control for Applications","volume":"6 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adc2.211","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140679058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design of intelligent behavior analysis software based on speaker identity classification algorithm in microgrid mode 基于扬声器身份分类算法的微电网模式下智能行为分析软件设计

Advanced Control for Applications Pub Date : 2024-04-18 DOI: 10.1002/adc2.209

Weijie Guo

{"title":"Design of intelligent behavior analysis software based on speaker identity classification algorithm in microgrid mode","authors":"Weijie Guo","doi":"10.1002/adc2.209","DOIUrl":"10.1002/adc2.209","url":null,"abstract":"Digital technology still has a low level of intelligence in the microgrid mode of teaching behavior analysis, resulting in the traditional manual observation and recording stage still being used for speaker identity classification, and the efficiency of teaching behavior analysis is also low. In response to the above issues, the research is based on the teacher-student analysis method and proposes a dual clustering algorithm based on the general background model Gaussian mixture model for speaker identity classification, thereby realizing the development and design of intelligent behavior analysis software. The research results indicate that the average recall rate of behavior transition points in the classroom teaching discourse corpus of the intelligent behavior analysis software is 89.03%, which is better than traditional analysis methods. Therefore, the intelligent behavior analysis software constructed by the dual clustering algorithm has high effectiveness and practicality. The research proposes a method model and implements intelligent visualization for classroom teaching behavior analysis, improving the efficiency of analyzing current microgrid teaching behavior.","PeriodicalId":100030,"journal":{"name":"Advanced Control for Applications","volume":"6 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adc2.209","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140687904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on trajectory control of multi-degree-of-freedom industrial robot based on visual image 基于视觉图像的多自由度工业机器人轨迹控制研究

Advanced Control for Applications Pub Date : 2024-04-17 DOI: 10.1002/adc2.210

Ruiling Hu

引用次数: 0

Machine learning-based data fusion method for wireless sensor networks 基于机器学习的无线传感器网络数据融合方法

Advanced Control for Applications Pub Date : 2024-04-14 DOI: 10.1002/adc2.208

Chunda Liang, Qi Yao

{"title":"Machine learning-based data fusion method for wireless sensor networks","authors":"Chunda Liang, Qi Yao","doi":"10.1002/adc2.208","DOIUrl":"10.1002/adc2.208","url":null,"abstract":"For wireless sensor networks (WSNs), sensor nodes lose a certain amount of energy during the information collection and transmission process, and sensor nodes powered by non-replaceable batteries have limited energy and need to be controlled for energy consumption. In the face of the energy consumption issue in WSN data transmission, research has been conducted to analyze data fusion methods in order to reduce energy consumption. Based on machine learning techniques, a Deep Stacked Auto-Encoder (DSAE) model is constructed and trained using a layer-wise greedy approach. By combining this model with WSN, an algorithm based on the DSAE model, called Deep Stacked Auto-Encoder Data Fusion Algorithm (DSAEDFA), is obtained to do data fusion. The results show that compared to other algorithms, the proposed fusion algorithm has better fusion performance. When the number of iterations is set to 500, the DSAEDFA has 281 surviving nodes, which is 10 more than the Back-Propagation Data Fusion Algorithm (BPDFA) and 144 more than the Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm. When the number of failed nodes is 40, the DSAEDFA has a network survival time of 2562 rounds, which is 746 rounds longer than the LEACH algorithm. The research method effectively extends the lifespan of wireless sensor networks and reduces data transmission energy consumption. Compared to previous methods, the proposed method consider the factors of node residual energy and distance on the basis of traditional routing protocols, making the selection of cluster heads more reasonable. The proposed method can organically combine the DSAE model with the clustering model, optimize the data fusion method, and improve the performance of the algorithm. In addition, by combining the DSAE model, a machine learning technique with clustering models has been expanded in terms of the application scope.","PeriodicalId":100030,"journal":{"name":"Advanced Control for Applications","volume":"6 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adc2.208","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140705511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Nonlinear control of electro-hydraulic screw conveyor system for shield machine based on disturbance observer and back-stepping method 基于扰动观测器和后步法的盾构机电动液压螺旋输送系统非线性控制

Advanced Control for Applications Pub Date : 2024-04-10 DOI: 10.1002/adc2.206

Liu Xuanyu, Cheng Xunlei

引用次数: 0

Research on adaptive particle swarm optimization particle filter target tracking algorithm in wireless sensor networks 无线传感器网络中的自适应粒子群优化粒子滤波器目标跟踪算法研究

Advanced Control for Applications Pub Date : 2024-04-02 DOI: 10.1002/adc2.205

Chun-Yan Jiang, Jing Wu, Rong Gou, Jing-Fang Fu

{"title":"Research on adaptive particle swarm optimization particle filter target tracking algorithm in wireless sensor networks","authors":"Chun-Yan Jiang, Jing Wu, Rong Gou, Jing-Fang Fu","doi":"10.1002/adc2.205","DOIUrl":"10.1002/adc2.205","url":null,"abstract":"With regard to target tracking in wireless sensor networks, we are faced with problems like deficient occlusion handling and tracking failures during rapid movements due to complex and diverse circumstances. In order to effectively improve the accuracy of particle filter tracking caused by particle degradation, we propose an adaptive particle swarm optimization (APSO) particle filter algorithm. This algorithm uses particle filters to predict the target location in a particular area and introduces the particle swarm optimization (PSO) algorithm, of which both the evolutionary speed and the convergence accuracy are further improved by investigating the particle distribution through an entropy analysis, employing three different inertial weighting strategies and dynamic double mutation strategy, and exploiting the capabilities of the adaptive balancing algorithm in global and local searching. The simulation results show that the improved algorithm has a reduced root mean square error, shorter time consumption, faster speed, reduced target tracking error, and higher average success rate, so this algorithm exhibits sound real-time performance and accuracy in terms of occlusion handling and tracking loss.","PeriodicalId":100030,"journal":{"name":"Advanced Control for Applications","volume":"6 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adc2.205","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140753712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0