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2022 Chinese Automation Congress, CAC 2022

作者： Liu, Yuyu Liu, Ding Song, Zezhong Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi`an University of Technology Xi'an710048 China

ISBN: (纸本)9781665465335

The growth of Czochralski(CZ) monocrystalline silicon is a nonlinear dynamic time-varying system with complex physical changes and multi-field and multi-phase coupling. In order to meet the needs of information development, it is urgent to develop large-scale, high-quality monocrystalline silicon. However, it is extremely difficult to establish an accurate control system model. Therefore, this paper considers the complex physical changes of multi-field coupling and non-linear processes in the growth of CZ monocrystalline silicon, a lumped parameter model for the growth of monocrystalline silicon is established based on energy balance model, fluid mechanics model, and geometry model, according to the internal mechanism of the monocrystalline silicon growth and the transfer mechanism of internal material flow. According to the model, a CZ monocrystalline silicon growth simulation control system is established under the Matlab/Simulink environment, and the real-time simulation data of each state variable of crystal growth is obtained. So that the control characteristics of each state variable in the crystal growth process can be analyzed. Finally, the validity of the established model is checked by the records evaluation of the semiconductor industry production process. Finally, the validity of the established model is verified based on the data analysis of the semiconductor industry production process. The results show that the model has high accuracy and can better describe the dynamic behavior of the actual monocrystalline silicon growth system. © 2022 IEEE.

关键词： MATLAB

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A control Method of Robotic Arm Based on Improved Deep Deterministic Policy Gradient 20

A Control Method of Robotic Arm Based on Improved Deep Deter...

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20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

作者： Shao, Yanpeng Zhou, Haibo Zhao, Shuaishuai Fan, Xiaoyan Jiang, Jiayi Tianjin University of Technology National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin300384 China Tianjin University of Technology Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin300384 China

ISBN: (纸本)9798350320831

With the continuous development of Machine Learning, Reinforcement Learning has achieved excellent results in many fields. An improved Deep Deterministic Policy Gradient (DDPG) algorithm is proposed in this paper to optimize Reinforcement Learning control performance, because of the issue of sparse rewards during training in continuous action spaces such as robotic arms, leading to slow convergence and low success rates. The algorithm merges the DDPG algorithm with the Hindsight Experience Relay (HER) algorithm, enabling the agent to learn from the failed experience when learning. Moreover, a double experience replay buffer is introduced, comprising a primary buffer and a secondary buffer containing high-reward experiences, which enhances the sampling probability of high-quality samples. At long last, a PyBullet simulation environment was utilized to execute a simulation experiment. The robotic arm using the improved DDPG algorithm achieved a success rate of nearly 100% in about 220 epochs of training, and the algorithm convergence speed was greatly improved. The trained robotic arm can reach any target point in the workspace. The efficacy of the improved DDPG algorithm is demonstrated by the outcomes, providing a valuable reference for further exploration into the intelligent control of robotic arms for object grasping. © 2023 IEEE.

关键词： Robotic arms

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Research on control Strategy of Manipulator Based on Deep Reinforcement Learning 23

Research on Control Strategy of Manipulator Based on Deep Re...

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3rd International Conference on Artificial Intelligence, Automation and Algorithms, AI2A 2023

作者： Xu, Nuo Shao, Yanpeng Fan, Xiaoyan National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin300384 China Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China

ISBN: (纸本)9798400707605

Abstract: Today, the traditional control algorithm of the manipulator is difficult to meet in the complex and changing working environment, and the development of artificial intelligence has achieved remarkable results in the field of manipulator control. As a branch of artificial intelligence, deep reinforcement learning combines the perception ability of deep learning with the decision-making ability of reinforcement learning, which can greatly improve the work efficiency and adaptability of the manipulator. This paper expounds on the principle of deep reinforcement learning, compares and analyzes the application examples of the single manipulator and multi-manipulator control algorithms based on deep reinforcement learning, and demonstrates the achievements of deep reinforcement learning in the motion control of manipulators. The intelligent control research provides a reference. © 2023 ACM.

关键词： Reinforcement learning

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control Strategy of Permanent Magnet Synchronous Motor and Brushed DC Servo Motor Based on ADRC 20

Control Strategy of Permanent Magnet Synchronous Motor and B...

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20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

作者： Liu, Liang Liu, Haikui Nie, Guangyin Gao, Yang Tianjin University of Technology Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin300384 China Tianjin University of Technology National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin300384 China

ISBN: (纸本)9798350320831

Adaptive disturbance rejection control (ADRC) is a rapidly evolving and promising control method that has been widely applied in various industrial fields and may yield better results than traditional method. In order to promote the controller performance, ADRC technology was employed to construct a three-loop structure for the controllers of permanent magnet synchronous motor (PMSM) and brushed DC Servo Motor (BDSM). Firstly, the mathematical models of the both kinds of motors were presented and combined with the ADRC, respectively. Secondly, the simulation models based on ADRC were constructed in MATLAB/SIMULINK, respectively. Finally, comparative experiments were carried out by ADRC and Proportional-Integral (PI) methods. The simulation results showed that ADRC outperforms PI control in terms of response speed, overshoot reduction, and anti-interference capability, which verified the proposed method can exhibit superior dynamic properties in the induction motor control. © 2023 IEEE.

关键词： controllers

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Automated Creation of Topological Structure Models of Parallel Mechanisms for Kinematic Performance Evaluation 8

Automated Creation of Topological Structure Models of Parall...

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8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Wu, Hongye Lin, Ping Ma, Yue Li, Bin Liu, Qi Li, Hongqing School of Mechanical Engineering Tianjin University of Technology Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control Tianjin300384 China Tianjin University of Technology National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin300384 China

ISBN: (纸本)9798350300178

Considering the diversity of topological structures of parallel mechanisms (PMs), this paper presents an approach for the automated generation of topological structure models based on CAD technology. In this method, joints and links are treated as fundamental elements in the skeleton model of a mechanical system, leading to the development of a library of fundamental components readily available to compose a variety of limbs and PMs. A connection graph and its data structure are then developed for describing, in a hierarchical manner, the assembly information among the fundamental components. Equipped with the knowledge and information mentioned above, the limbs and PMs can finally be assembled automatically by identifying and imposing the geometrical constraints that have been already embedded in the models. The merit of this method lies in that 3D schematic diagrams (termed topological structure models) of various limbs and PMs can be created in an illustrative manner by properly selecting a set of fundamental components and then setting their connections, leading to the digitalized and visible topological structure models that are readily for kinematic performance evaluation. Combined with the CAD-CAE integration technology for the mechanical structure design and static/dynamic performance evaluation, a digital design and simulation framework can be formulated for the rapid design of parallel robotic equipment. © 2023 IEEE.

关键词： Topology

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Dimensional synthesis and static/dynamic analysis of a robot in automobile hubs polishing 20

Dimensional synthesis and static/dynamic analysis of a robot...

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20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

作者： Chen, Long Xi, Manru Ma, Yue Li, Bin Liu, Qi Li, Changrui Tianjin University of Technology Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin300384 China Tianjin University of Technology National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin300384 China

ISBN: (纸本)9798350320831

Focusing on the polishing operation of automobile hubs, this paper proposed a polishing robot by combining a 1T2R parallel mechanism (PM) and an X-Y worktable. The dimensional synthesis and static/dynamic performance analysis of the 1T2R PM are also presented with the key points as follows. Firstly, the inverse displacement model and velocity model of the PM are built to formulate the motion/force transmissibility indices on the basis of the generalized Jacobian matrix. Secondly, the dimensional synthesis problem is formulated to determine the optimized normalized dimensional parameters by maximizing the global force/motion transmissibility of the PM subject to geometric constraints without mechanical interference. Finally, the finite element (FE) model of the 1T2R PM is built with the aid of the SAMCEF, and the static stiffness and lower-order natural frequencies of the PM are evaluated. The research results of this paper provide dimensions and mechanical structures for developing the physical prototype of the automobile hub polishing robot. © 2023 IEEE.

关键词： Finite element method

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Unequal Error Protection Rateless Codes for Spatial Audio Transmission 24

Unequal Error Protection Rateless Codes for Spatial Audio Tr...

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24th IEEE International Conference on Communication Technology, ICCT 2024

作者： Yin, Junpeng Fu, Yusun Huang, Haobo Tang, Jinhui Shanghai Jiao Tong University Ningbo Artificial Intelligence Institute Ningbo China Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai China Ministry of Education of China School of Electronic Information and Electrical Engineering The Key Laboratory of System Control and Information Processing Shanghai China Shanghai Jiao Tong University Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (纸本)9798350363760

Spatial audio is an important component of the metaverse. With the rapid increase of the spatial resolution and the number of objects, the data volume of the spatial side information has grown larger. However, the low bit rate transmission of spatial side information in the object-based audio coding methods leads to the low-quality reconstruction of spatial side information with limited bandwidth. To address this issue, this paper constructs a cascade design of spatial audio coding and unequal error protection (UEP) rateless codes. Furthermore, the further protection of specific spatial side information is proposed for different scenarios, which effectively addresses the limitation of traditional spatial audio coding in side information protection. For the situation where the code length K of different importance levels is different, we propose an adaptive degree distribution (ADD), adding K to the selection probability as an influencing factor, which has better performance than other degree distribution for short code length. ADD is used to replace the multiple degree distribution strategy of the traditional unequal error protection scheme, and the block error rate (BER) is reduced nearly 10 times compared with the traditional scheme. © 2024 IEEE.

关键词： Audio signal processing

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Global-Local Context Enhanced Yolo for Small Object Detection in Uav Images

SSRN

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SSRN 2025年

作者： Wei, Lixin Han, Kangshuai Li, Xin Che, Haijun Hu, Ziyu Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment Yanshan University Hebei Qinhuangdao066000 China Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Hebei Qinhuangdao066000 China

Object detection using Unmanned Aerial Vehicles (UAVs) has emerged as a criti cal application across diverse domains. However, the wide-angle views of drones often result in images containing a high density of small objects, posing chal lenges for object detection such as few learnable features, significant occlusion, and an imbalanced distribution of positive and negative samples. To address these issues, this paper introduces AGLC-YOLO, an enhanced version of the YOLOv7 architecture specifically designed for detecting small objects in UAV images. AGLC-YOLO integrates global and local context information through a Attention guide Global-Local Context Information Extraction (AGLC) module. This module employs parallel dilated convolutions to capture local context infor mation and a transformer-based structure to extract global dependencies, which are then fused using an improved attention mechanism. The network also adds an additional small object detection head to enrich the small object information in the model. Additionally, AGLC-YOLO utilizes an auxiliary bounding box in con junction with the Inner-Wise Intersection over Union (Inner-WIoU) loss function to accelerate the bounding box regression process and improve detection accu racy. Experimental results on the VisDrone and ManipalUav datasets demonstrate that AGLC-YOLO achieves significant improvements over the baseline YOLOv7 model, with anincrease of 3%inAP50and2.7%inAP95ontheVisDronedataset, and 1.9% in AP50 and 2% in AP95 on the ManipalUav dataset. © 2025, The Authors. All rights reserved.

关键词： Convolution

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Data-Driven Robust Optimization for Energy Chemical Processes under Uncertainties: A Review and Tutorial 4

Data-Driven Robust Optimization for Energy Chemical Processe...

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4th International Conference on Industrial Artificial Intelligence, IAI 2022

作者： Ning, Chao Li, Longyan Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Shanghai200240 China

ISBN: (数字)9781665451208

ISBN: (纸本)9781665451208

In recent years, data-driven robust optimization (DDRO) is becoming a popular and effective paradigm to address the challenging issue of uncertainty in energy chemical processes. This paper provides an overview of recent advances in the field of DDRO, with a primary focus on its methods and applications in process industries. Firstly, a brief introduction to various robust optimization model formulations and solution algorithms is presented. Secondly, research achievements of machine-learning enabled uncertainty sets, the corresponding DDRO, and variant techniques are summarized and analyzed in a systematic manner. Additionally, tutorial-like numerical examples are used to illustrate merits of DDRO compared with conventional robust optimization. Finally, fruitful applications of DDRO in energy chemical processes are encapsulated and categorized from domain perspectives. © 2022 IEEE.

关键词： Optimization

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Event-Triggered Online Learning Distributionally Robust Energy Management of Ammonia-Based Multi-Energy Microgrid 4

Event-Triggered Online Learning Distributionally Robust Ener...

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4th International Conference on Industrial Artificial Intelligence, IAI 2022

作者： Li, Longyan Ning, Chao Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China

ISBN: (数字)9781665451208

ISBN: (纸本)9781665451208

This paper proposes a novel uncertainty-aware energy management framework for Multi-energy Microgrid (MEMG), which comprehensively comprises electricity, heat, natural gas, hydrogen, and ammonia. In particular, green ammonia is produced from hydrogen, which is derived from electrolysis powered by renewable energy. The proposed framework seamlessly integrates day-ahead optimal scheduling with data-driven model predictive control. To offer a just-in-time resilience to uncertainties of renewable energy and load, we further develop event-triggered online learning distributionally robust model predictive control (ET-OLDRMPC). Specifically, an event trigger mechanism is designed to enable the controller to intelligently switch between certainty-equivalence and distributionally robust schemes as per their respective advantageous regimes, thereby ensuring operation safety while mitigating unnecessary conservatism. For the distributionally robust scheme, we leverage a nonparametric Bayesian model to construct online ambiguity sets of uncertainty distributions, which encode statistical multimodality and local moment information. The effectiveness of the proposed framework is validated in a case study. © 2022 IEEE.

关键词： Ammonia

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