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Multi-Motor Speed Synchronization control for Prefabricated Vegetable Vortex Cleaning Line Based on MFSMC

Multi-Motor Speed Synchronization Control for Prefabricated ...

引用

data driven control and learning systems (ddcls)

作者： Zhigang Cui Wenlong Yao Ronghu Chi Xianghao Chen The College of Automation and Electronic Engineering Qingdao University of science and Technology Qingdao

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

A multi-motor speed synchronization control method is proposed for the prefabricated vegetable vortex cleaning line in this paper. the focus of this method is to enhance the system's synchronization performance, disturbance rejection capability, dynamic response, and stability of the motors. this method is based on Model-Free Sliding Mode control (MFSMC) combined with an enhanced deviation coupling control structure. Its objective is to tackle the challenges arising from significant variations in motor speed tracking errors, notable differences in speed and acceleration, and the complex system structure that results in unstable cleaning effects. Initially, Permanent Magnet Synchronous Motors (PMSM) are selected as the controlled objects. An improved sliding mode manifold function is introduced to minimize the rate of change in motor speed tracking errors. Utilizing this, a data-driven model-free control technique is employed to design a model-free adaptive sliding mode controller for the motors. Additionally, the deviation coupling structure is enhanced to mitigate the negative effects caused by notable differences in speed and acceleration, along with the complex system structure on the synchronization performance. Finally, a simulation analysis is performed employing three motors to evaluate the effectiveness of the offered control method. the results obtained demonstrate the superior performance of motors controlled by the proposed control method exhibit enhanced response speed, reduced overshoot, improved disturbance rejection capability, and superior speed synchronization performance.

关键词： Couplings Adaptation models Transient response Motors Cleaning Mathematical models Synchronization

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Identification Modelling of the Hammerstein Nonlinear systems Utilizing Adaptive Neural Fuzzy Networks and State Space Model

Identification Modelling of the Hammerstein Nonlinear System...

引用

data driven control and learning systems (ddcls)

作者： YanZong Zhu Yuesong Yang Feng Li Zhou Zhou School of Electrical and Information Engineering Jiangsu University of Technology Changzhou China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In this article, an identification modelling method for the Hammerstein nonlinear systems utilizing adaptive neural fuzzy networks and state space model is proposed. the proposed Hammerstein system consists of a five-layer adaptive neural fuzzy network approximating the static nonlinear block and a state space model representing the linear dynamical block with output noises, and identification of the adaptive neural fuzzy networks and the state space model are implemented independently by using designed combined signals, which include binary and random signals. Due to binary signals do not excite nonlinear systems, the recursive extended least squares algorithm is employed to identify the parameters of the state-space model and the colored noises model, which improves identification accuracy. Secondly, based on random signals input-output measurements, the cluster method and the recursive extended least squares algorithm are used to identify the adaptive neural fuzzy networks. the simulation results demonstrate the effective identification of the Hammerstein nonlinear system using the proposed parameter identification technique and the adaptive neural fuzzy networks can model the nonlinearity of the Hammerstein system.

关键词： Adaptation models Adaptive systems Parameter estimation Accuracy Simulation Neural networks Clustering algorithms

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Study on Multi-Timescale Scheduling Strategy Based on Large-Capacity Liquid Flow Battery for Renewable Energy Consumption

Study on Multi-Timescale Scheduling Strategy Based on Large-...

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data driven control and learning systems (ddcls)

作者： Dianyang Li Nan Xiao Keming Zhan Yuchen Jiang Junpeng Gao Feiran Yang Power Dispatching Control Center State Grid Liaoning Electric Power Co. LTD Shenyang China College of Information Science and Engineering Northeastern University Shenyang China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In order to achieve the full consumption of renewable energy and the reduction of peak and valley loads in the active power system, a multi-objective optimal scheduling strategy for the active power system, i.e., the “source-storage-load” strategy, which takes into account the participation of controllable loads and energy storage, is proposed. After comprehensively considering the characteristics of the liquid-fluid battery energy storage system and the multi-timescale characteristics of various types of demand response resources on the load side, a multi-timescale scheduling model is established by taking the system operating cost, the penalty cost for wind abandonment, and the penalty cost for loss of load as the objective function, formulating a day-ahead scheduling plan, and guaranteeing a higher accuracy of the forecasting data through intra-day rolling. the simulation results show that the model reduces the wind abandonment rate while ensuring the economic operation of the system. It effectively improves the degree of new energy consumption by the “source-storage-load” system, which is of great significance for the development and practical application of large-capacity liquid current battery energy storage system.

关键词： Renewable energy sources Energy consumption Liquids Costs Optimal scheduling Wind power generation Regulation

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Local Directional Centrality Clustering Based on K-nearest Neighbor Outlier Detection and Shared Neighborhood Strategy

Local Directional Centrality Clustering Based on K-nearest N...

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data driven control and learning systems (ddcls)

作者： Qing Liu Zihang Feng Liping Yan Yuanqing Xia National Key Laboratory of Autonomous Intelligent Unmanned Systems School of Automation Beijing Institute of Technology Beijing China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Clustering, as an important prerequisite method in data analysis, can uncover potential information in the data and then proceed to the next step of data analysis and processing. the recently proposed boundary-seeking Clustering algorithm using the local Direction Centrality (CDC) is a very effective method for clustering data with heterogeneous density and weak connectivity. However, it still has some shortcomings. On the one hand, the reachable distance is not enough to comprehensively distinguish weak connection situations, which can easily lead to cross cluster connection errors. On the other hand, K-nearest neighbor search is prone to cross cluster search, leading to misjudgment of boundary points and resulting in connection errors. this paper proposes a local direction centrality clustering algorithm based on K-nearest neighbor outlier detection and shared neighborhood strategy (SODCDC) for sparse and weakly connected data. this algorithm uses a K-nearest neighbor outlier detection strategy to relieve K-nearest neighbor cross cluster search and reduces the probability of boundary point misjudgment. At the same time, it uses a shared neighborhood strategy to further prevent cross cluster connections of weakly connected data. Experiments on some datasets have shown that compared to the original algorithm, the proposed algorithm performs better under the commonly used evaluation metrics.

关键词： Measurement learning systems data analysis Clustering algorithms Manuals Nearest neighbor methods Search problems

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Performance Evaluation and Order Selection for Pattern Modeling Methods through Granger Causality Analysis

Performance Evaluation and Order Selection for Pattern Model...

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data driven control and learning systems (ddcls)

作者： Shaotong Zhu Niannian Zheng Xiaoli Luan Fei Liu Jiangnan University Wuxi P. R. China Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Wuxi P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In the realm of processes modeling and control, the monitoring of processes is crucial for ensuring safety, and a key task in multivariate statistical process monitoring is to extract the operating patterns of the process. Dynamic strategies, exemplified by dynamic-inner principal components analysis (DIPCA), dynamic-inner partial least squares (DIPLS), and dynamic-inner canonical correlation analysis (DICCA), are employed to tackle temporal correlation and the ever-changing nature of processes using AR structures. Dynamic controlled principal components analysis (DCPCA) additionally considers dynamic controlled characteristics, showcasing remarkable accuracy in both the monitoring of processes and dynamic prediction. Notwithstanding the precision demonstrated by DCPCA, the task of ascertaining optimal control input orders remains intricate, often necessitating reliance on empirical methodologies or trial-and-error approaches. this research introduces an innovative methodology, drawing inspiration from Granger causality analysis, to establish causal relationships between control inputs and patterns. Causal influence (CI) was employed to assess the impact of introducing control inputs and the effects of control inputs at different orders, aiding the model in selecting the optimal control input order. theoretically supported performance evaluation and order selection, providing assurance for practical applications.

关键词： Performance evaluation Process monitoring Correlation Process modeling Cause effect analysis Process control Optimal control

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Improved YOLOv8 Based Small Object Detection for Intelligent Robotic Arm in Complex Environments

Improved YOLOv8 Based Small Object Detection for Intelligent...

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data driven control and learning systems (ddcls)

作者： Zhiqing Li Dinglinag Ren College of Information Science and Technology Beijing University of Chemical Technology Beijing P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Small objects in complex environments may be very close to the background, which brings great challenges to computer vision based intelligent robotic arms. To address these problems, an improved YOLOv8 based small object detection method is proposed in this paper. Firstly, the YOLOv8 prediction heads are reconstructed to improve the prediction performance of small objects. An adaptive multi-scale convolutional attention module (MSCA) is introduced in the backbone which leverages multi-scale features to apply spatial attention with element multiplication. Part of convolutional modules is replaced with GhostNet blocks. these GhostNet blocks generate numerous ghost feature maps with cost-effective linear transformations based on intrinsic feature maps. Finally, the coordinate regression loss function is also optimized with Wasserstein divergence. Experimental results show that the improved YOLOv8 achieves a 0.093 improvement in mean average precision (mAP) compared to vanilla YOLOv8. For practical deployment applications, a sophisticated spatially-aware intelligent robotic arm is proposed by combining the improved YOLOv8 and laser ranging. the practical application results demonstrate that the proposed method has high feasibility and stability.

关键词： Program processors Embedded systems Robot kinematics Computational modeling Object detection Manipulators Real-time systems

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Full Wavelength Selection and Deep Spectra Feature Extraction Network for Near-Infrared Spectroscopy Quantitative Analysis

Full Wavelength Selection and Deep Spectra Feature Extractio...

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data driven control and learning systems (ddcls)

作者： Jinlong Liu Xiaoli Luan Fei Liu Key Laboratory for Advanced Process Control of Light Industry of the Ministry of Education Jiangnan University Wuxi Jiangsu P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Near-infrared spectroscopy (NIRS) has been widely used in the chemical industry and has attracted much attention because of its fast and non-destructive characteristics. However, spectra are susceptible to interference from external environmental factors, leading to spectral overlap and noise problems. To solve these problems, spectral wavelength selection can be performed. Traditional selection methods require manually choosing the appropriate method based on spectral data and combining it with appropriate regression modeling, which is complicated and time-consuming. In contrast, this study proposes a novel spectral wavelength selection method that intelligently filters the entire wavelength based on the correlation between spectra and output variables. Simultaneously, it utilizes a deep spectra feature extraction module to obtain a more abstract spectral representation applied in regression prediction. Evaluation results on the training and test sets demonstrate the outstanding performance of this model: RMSE of 0.007796 and 0.01036 for the training and test sets, respectively, R2 of 0.9918 and 0.9842, with an impressive RPD of 7.9703. these metrics indicate that this model meets the rigorous requirements for high-performance models in the chemical engineering domain.

关键词： Training Analytical models Statistical analysis Noise Predictive models Feature extraction Prediction algorithms

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On-Orbit Identification of Unstable Micro-Propulsion System Parameters Using Drag-Free control

On-Orbit Identification of Unstable Micro-Propulsion System ...

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data driven control and learning systems (ddcls)

作者： Nuo Xu Xiwang Xia Zhaoxiong Li Xiongfeng He Ning Guo Pengcheng Wang Yonghe Zhang Innovation Academy for Microsatellites Chinese Academy of Sciences Shanghai P. R. China University of Chinese Academy of Sciences Beijing P. R.China Chinese Academy of Sciences Key Laboratory for Satellite Digitization Technology Shanghai P. R.China Science and Technology on Vacuum Technology and Physics Lab Lanzhou Institute of Physics Lanzhou P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Space gravitational wave detection spacecraft is equipped with a drag-free control and micro-propulsion system, and adopts thrust closed-loop feedback control structure. An on-orbit high accuracy estimation algorithm of micro-thruster parameters based on drag-free control model is proposed in this paper in order to deal with the problem of unsteady nominal thrust caused by thruster failure or aging and and extend thrusters lifespan. In this method, the recursive gain and covariance are designed to estimate the parameter matrix of system. Adaptive forgetting factors are added to the covariance matrix for designing a fading memory window to determine the influence of prior data on the results. At the same time, the thruster state is deconstructed into three parts: thrust gain, thrust direction and lever arm to facilitate the redistribution of control force and torque. Simulations show that the on-orbit thrust identification errors of the proposed method in different micro-propulsion states are all at sub-micro-newton level, and the noise is less than $0.1\mu N/\sqrt{Hz}$ which has capability to meet the requirements corresponding to drag-free control system.

关键词： Space vehicles Fading channels Accuracy Torque Attitude control Heuristic algorithms Simulation

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the Instance Segmentation and Grasping Sequence Method for Stacked Workpieces

The Instance Segmentation and Grasping Sequence Method for S...

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data driven control and learning systems (ddcls)

作者： Chunfang Liu Chen Chen Faculty of Information and Technology Beijing University of Technology Beijing P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

To obtain the grasping sequence of the robotic arm for grasping target objects in a stacked workpiece industrial scenario, this paper proposes an instance segmentation and grasping sequence method for stacked workpieces. Firstly, a Mask R-CNN instance segmentation network based on boundary refinement is designed to obtain more accurate instance mask information. Additionally, a Euclidean clustering method based on smoothness parameters is integrated to correct extreme cases of mis-segmentation by the improved Mask R-CNN instance segmentation network from the perspective of three-dimensional space (where adjacent closely stacked workpieces are mis-segmented as the same workpiece). Secondly, a stacking workpiece grasping sequence filtering mechanism is proposed. this mechanism determines the occlusion status of the target workpiece to be grasped by judging the relationship between visible masks and complete masks obtained through instance segmentation. Furthermore, based on the grasping judgment scores, the workpieces in unoccluded states are arranged in sequence, filtering out target workpieces that satisfy the principle of grasping unoccluded state workpieces first, in a top-to-bottom manner. Experimental results demonstrate that the proposed method can effectively infer the grasping sequence in stacked workpiece environments, thereby driving the robotic arm to perform grasping tasks.

关键词： Instance segmentation learning systems Accuracy Filtering Service robots Stacking Grasping

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A Multi-Scale Cross Transformer Network-Based Fault Diagnosis Method for Industrial Process

A Multi-Scale Cross Transformer Network-Based Fault Diagnosi...

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data driven control and learning systems (ddcls)

作者： Yuan Xu Rui-Ze Fan Wei Ke Yan-Lin He Qun-Xiong Zhu Ming-Qing Zhang Yang Zhang College of Information Science & Technology Beijing University of Chemical Technology Beijing China Engineering Research Center of Intelligent PSE Ministry of Education of China Beijing China Faculty of Applied Sciences Macao Polytechnic University Macao SAR P.R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

the process of industrial production is becoming increasingly complex in modern times, posing higher demands for safety. It's worth noting that data from modern industrial processes often exhibit multivariate, temporal and multiscale characteristics. To address these problems, this paper presents a novel fault diagnosis method based on the Multi-Scale Cross Transformer model. We integrate encoder, convolution and patching mechanisms, making it capable of extracting temporal features from different time scales, effectively capturing short-term dynamics and long-term trends. Additionally, a two-stage attention mechanism is introduced to automatically learn correlations across both time and space dimensions. Experiments conducted on the Tennessee-Eastman process demonstrate the superiority of our proposed method. It shows over 10% accuracy enhancement in diagnostic performance compared to state-of-the-art methods. Furthermore, the method can gradually map data to a discriminative feature space with better intra-class compactness and inter-class separability, reflecting its reliability and validity.

关键词： Fault diagnosis Correlation Attention mechanisms Accuracy Process control Production Transformers

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