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A Weighting Factors Autotuning Strategy for Model Predictive Torque control of Permanent Magnet Synchronous Motor Based on Adaptive Multi-objective Black Hole Algorithm

A Weighting Factors Autotuning Strategy for Model Predictive...

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第42届中国控制会议

作者： Zixiang Zhou Tao Wu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems System Integration and Industrial Robotics Laboratory

The rational design of weighting factors in the cost function for finite control set model predictive torque control(FCS-MPTC) has been a matter of great interest in power electronics and electrical drives. In order to solve this problem, a weighting factors autotuning strategy for FCS-MPTC of permanent magnet synchronous motor(PMSM) based on the adaptive multi-objective black hole algorithm(AMOBH) is proposed. In this paper, the design process of the FCS-MPTC algorithm is first analyzed in detail. Then, an AMOBH algorithm that can take into account both population convergence and population diversity is introduced, and based on this algorithm, the design problem of the weighting factors is successfully transformed into a multiobjective optimization problem by means of reconstructing the cost function and designing the motor operation information collected in real time as the objective functions of the multi-objective optimization algorithm. Simulation results show that the proposed method can find a set of weighting factor combinations suitable for different working condition requirements, and these weighting factors can effectively improve the operation performance of the PMSM system.

关键词： Model Predictive Torque control Weighting Factors Multi-objective Optimization Algorithm Permanent Magnet Synchronous Motor

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Temperature Rising Model of Tubular Permanent Magnet Linear Synchronous Motor Based on Transfer Learning-Deep Neural Network

Temperature Rising Model of Tubular Permanent Magnet Linear ...

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第42届中国控制会议

作者： YiFei Wang Tao Wu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems System Integration and Industrial Robotics Laboratory

High temperature rise of permanent magnet linear synchronous motor can lead to irreversible demagnetization of the motor permanent magnet, which can negatively affect the motor performance. To address this problem, a thermal modeling analysis method based on Transfer learning-Deep neural network(TL-DNN) was proposed in this paper. Its specific implementation steps include(1) corresponding to different heat source inputs, the equivalent thermal circuit method and the finite element analysis was adopted based on the structure and main parameters of PMLSM including overall average temperature rise, coil temperature rise and permanent magnet temperature rise from the data sets of the motor;(2) TL-DNN was used to fit the functional relationship between the input source features and the output targets based on the characteristics of the sample data sets. In order to verify the accuracy of the prediction model with small sample data sets, this paper divided the proportion of the data set and compared the results with other classic nonparametric models(random forest, support vector machine, and deep neural network). The results show that the TL-DNN model outperforms other machine learning models and has better robustness and generalization ability when the training datasets are insufficient, and achieves an organic combination of physical field path model and data-driven model, which provides a feasible solution for PMLSM modeling in the case of small samples.

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Open-/Closed-loop Active Learning for Data-driven Predictive control

arXiv

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arXiv 2024年

作者： Feng, Shilun Shi, Dawei Shi, Yang Zheng, Kaikai The State Key Laboratory of Intelligent Control and Decision of Complex Systems MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing Institute of Technology Beijing100081 China The Department of Mechanical Engineering Faculty of Engineering University of Victoria VictoriaBCV8N 3P6 Canada

An important question in data-driven control is how to obtain an informative dataset. In this work, we consider the problem of effective data acquisition of an unknown linear system with bounded disturbance for both open-loop and closed-loop stages. The learning objective is to minimize the volume of the set of admissible systems. First, a performance measure based on historical data and the input sequence is introduced to characterize the upper bound of the volume of the set of admissible systems. On the basis of this performance measure, an open-loop active learning strategy is proposed to minimize the volume by actively designing inputs during the open-loop stage. For the closed-loop stage, an closed-loop active learning strategy is designed to select and learn from informative closed-loop data. The efficiency of the proposed closed-loop active learning strategy is proved by showing that the unselected data cannot benefit the learning performance. Furthermore, an adaptive predictive controller is designed in accordance with the proposed data acquisition approach. The recursive feasibility and the stability of the controller are proved by analyzing the effect of the closed-loop active learning strategy. Finally, numerical examples and comparisons illustrate the effectiveness of the proposed data acquisition strategy. Copyright © 2024, The Authors. All rights reserved.

关键词： Active learning

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Relative Localization for UAV-UGV Based on Correlation of Bearing and Distance

Relative Localization for UAV-UGV Based on Correlation of Be...

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IEEE International Conference on Mechatronics and Automation

作者： Jia Guo Kang Hu Jinting Liu State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology No.5 Zhongguancun South Street Beijing China

To ensure reliable localization for Unmanned Aerial Vehicles(UAVs) in the presence of uncertain speed of the Unmanned Ground Vehicle (UGV), this study examines the relative localization problem of UAV-UGV using distance and bearing measurements. A Correlation of Bearing and Distance-based Relative Localization (CBDRL) algorithm is proposed in this paper under this scenario. The estimation of altitude, distance, and angle are simplified into a representation of the relative positioning between the UAV and UGV. The relative height difference is measured using the barometer in the algorithm. To determine the relative distance, Time of Arrival (TOA) ranging and Ultra Wide Band (UWB) communication are utilized. The relative direction measurement is then determined using the correlations of bearing and distance. We integrate these observations with height, direction, and distance data in an Extended Kalman Filter(EKF) to provide accurate and reliable relative position estimates that allow the UAV to track the target. The simulation results indicate that the CBDRL method developed in this study is superior to previous relative localization algorithms that rely on multi-sensor fusion, and can significantly enhance the accuracy of UAV positioning provided that range and angle measurements are precise enough.

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Early Forest Fire Detection Based on Deep Learning 3

Early Forest Fire Detection Based on Deep Learning

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3rd International Conference on Industrial Artificial Intelligence, IAI 2021

作者： Li, Mengna Zhang, Youmin Mu, Lingxia Xin, Jing Yu, Ziquan Liu, Han Xie, Guo Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Shaanxi Xi'an710048 China

ISBN: (纸本)9781665435178

Early fire detection is very important for preventing forest fires. In this paper, a new image-based fire detection algorithm, named as h-EfflcientDet, is proposed to complete the task of early forest fire detection. h-EfflcientDet is based on a popular deep learning approach EfficientDet (scalable and efficient object detection) by replacing the nonlinear activation function swish of the EfficientDet with the hard version of swish and combing also with an efficient feature fusion network BIFPN (bidirectional feature pyramid network), which can improve significantly the efficiency of the fire detection model. The experiment results show that the proposed h-EfficientDet can detect the fire in real-time with the detection speed of 21 FPS. The detection accuracy is up to 98.35% with a low miss detection rate. © 2021 IEEE.

关键词： Fires

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Analysis of LoRa for Electronic Shelf Labels Based on Distributed Machine Learning

Analysis of LoRa for Electronic Shelf Labels Based on Distri...

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Chinese control Conference (CCC)

作者： Malak Abid Ali Khan Hongbin Ma Ying Jin Jingxiang Ma Zia Ur Rehman Mizanur Rahman School of Automation Beijing Institute of Technology Beijing China State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China

Deforestation is the primary source of global warming; traditional shelf labels use paper to display the price of the products, and human forces play a pivotal role in updating the tags where the pandemic has strictly limited its power. Various technologies provide connectivity and a fast-updating system to eliminate the paper-based approach. LoRa is one of the contenders to design the system for electronic shelf labels (ESLs). In this paper, LoRa has been used to minify data losses and guarantee the successful decoding of the carrier signals. The data parallelism at the network server (NS) is used to distribute the data packets among the gateways (GWs) for concurrent transmissions to the end devices (EDs). The EDs are placed in different ranges using machine clustering to avoid intra-SF interference and collision. The data rate (DR) and spreading factors (SFs) have been proposed to improve the performance of pure and slotted ALOHA for the properly allocated tags. The orthogonality principles follow industrial, scientific, and medical regulations (ISM) to avoid data traffic congestion. GWs under different duty cycles (DC) and bandwidth (BW) are examined to minify the network saturation and reduce the energy harvesting of the tags.

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Adaptive control of Discrete-time Nonlinear systems Using ITF-ORVFL

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IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 556-563页

作者： Xiaofei Zhang Hongbin Ma Wenchao Zuo Man Luo School of Automation Beijing Institute of TechnologyBeijing 100081 School of Vehicle and Mobility Tsinghua UniversityBeijing 100084China School of Automation Beijing Institute of Technologyand also with the State Key Laboratory of Intelligent Control and Decision of Complex Systems(Beijing Institute of Technology)Beijing 100081China School of Automation Beijing Institute of TechnologyBeijing 100081and he is with Beijing Institute of Electronic System EngineeringBeijing 100854China School of Automation Beijing Institute of TechnologyBeijing 100081and she is with Ant GroupBeijing 310013China

Random vector functional ink(RVFL)networks belong to a class of single hidden layer neural networks in which some parameters are randomly *** network structure in which contains the direct links between inputs and outputs is unique,and stability analysis and real-time performance are two difficulties of the control systems based on neural *** this paper,combining the advantages of RVFL and the ideas of online sequential extreme learning machine(OS-ELM)and initial-training-free online extreme learning machine(ITFOELM),a novel online learning algorithm which is named as initial-training-free online random vector functional link algo rithm(ITF-ORVFL)is investigated for training *** link vector of RVFL network can be analytically determined based on sequentially arriving data by ITF-ORVFL with a high learning speed,and the stability for nonlinear systems based on this learning algorithm is *** experiment results indicate that the proposed ITF-ORVFL is effective in coping with nonparametric uncertainty.

关键词： Adaptive control initial-training-free online learning algorithm random vector functional link networks

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Dynamic Event-Triggered control for Nonlinear systems:A Small-Gain Approach

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系统科学与复杂性学报（英文版） 2020年第4期33卷 930-943页

作者： ZHAO Qingtao SUN Jian BAI Yongqiang School of Automation Beijing Institute of Technology Beijing 100081 China Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing 100081 China

An event-triggered control (ETC) system transmits data packages and updates control inputs only when the predefined criterion is *** this way,network communication and computing resources are scheduled more reasonably in contrast to the traditional periodic sampling ***-gain approach proposed in recent literatures is a new modeling method to deal with nonlinear ETC *** from traditional ETC models,stability criteria are proposed in the form of input to state stability (ISS) gain to design the triggering *** paper introduces additional dynamic variables in this model and proposes a small-gain based dynamic event-triggered *** conditions to guarantee the stability of the system are derived with the help of cyclic-small-gain theorem and Zeno behaviors are avoided to ensure the feasibility of this method in practical *** simulations are given to demonstrate the effectiveness of our approach.

关键词： Cyclic-small-gain theorem event-triggered control input-to-state stability (ISS) nonlinear systems

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Capture the Flag Based Assignment Algorithm for Tracking Task of Multi-UAVs 5

Capture the Flag Based Assignment Algorithm for Tracking Tas...

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5th International Symposium on Autonomous systems, ISAS 2022

作者： Dalei, Song Aoliang, Dai Xiaoqian, Wang Hongjun, Ma Donghui, Wei Shenyang University of Technology Shagnhai China School of Information Northeastern University Shagnhai China Beijing Research Institute of Mechanical Electrical Technology Key Laboratory of Complex System Control and Intelligent Collaboration Technology Beijing China

ISBN: (纸本)9781665487085

In order to solve the problem of real-time target allocation in the multi-UAVs task for cooperative tracking the moving targets on ground, based on the behavior model of capture the flag, this paper proposes a dynamic assignment algorithm, which has fast convergence performance, for cluster UAVs. Based on satisfaction and total flight distance, the proposed algorithm constructs the assignment target function, and the optimal value of the target function is achieved quickly and the uniform saturation tracking is realized for the target group through the behavior model of grabbing flags. Finally, compared with traditional genetic algorithm, the simulation results show that the algorithm has a significant improvement in convergence speed and single assignment imply operation speed with the increase of the number of assigned targets and cluster UAVs © 2022 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Genetic Algorithm for Block Relocation in Automated Warehouse 5

Genetic Algorithm for Block Relocation in Automated Warehous...

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5th International Conference on Artificial Intelligence Technologies and Applications, ICAITA 2023

作者： Wang, Gang Lian, Mingchang Ding, Wei Chen, Songhang Chen, Hao Quanzhou Institute of Equipment Manufacturing Haixi Institutes Chinese Academy of Sciences Quanzhou362200 China College of Computer and Cyber Security Fujian Normal University Fujian Fuzhou350001 China Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China Fujian Fuzhou350108 China Fujian Provincial Key Laboratory of Intelligent Identification and Control of Complex Dynamic System Quanzhou362200 China

ISBN: (纸本)9781643684840

This paper is based on the research background of block relocation in the automated warehouse and proposes a solution utilizing AGV to relocate goods within the warehouse. Building upon the automated warehouse layout and block relocation process, we formulate a mathematical model to minimize the the number of AGV moving goods and the total travel distance of the AGV. To solve the model, we develop an enhanced genetic algorithm, which zones the block relocation process into multiple stages. Each stage integrates conventional heuristic algorithms to enhance the efficiency of the solution process. The improved genetic algorithm can effectively solve the problem, significantly enhancing the automation level of the warehouse. © 2024 The Authors.

关键词： Genetic algorithms

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