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2024 China Automation Congress, CAC 2024

作者： Wang, Xiaoxue Jin, Feng Zhao, Jun Wang, Wei Chen, Long Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Dalian University of Technology Dalian China

ISBN: (纸本)9798350368604

Blast furnace gas (BFG), a vital byproduct energy resource in the ironmaking process, can be efficiently managed through proper scheduling to minimize emissions and augment productivity. A novel scheduling method for BFG systems based on an improved Conditional Generative Adversarial Network (CGAN) and process rules is proposed. Based on the Wasserstein generative adversarial network with gradient penalty (WGAN-GP) and the process production mechanism under ideal conditions, a scenario generative model is constructed to constrain the training process with the rules of the changes among scenario factors. Labels are used as condition information for CGAN to generate the corresponding labeled scenarios. Then, by matching real-time operational data with the generated scenarios, the scheduling solution is derived for the given condition. Using the actual data collected from a large steel enterprise, the validation experiments confirm that the scheduling scenarios generated by the proposed method are consistent with actual operational patterns. Moreover, it delivers viable and rational scheduling solutions for new operating conditions, safeguarding the safe operation of the BFG system. © 2024 IEEE.

关键词： Blast furnaces

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Colour Variation Minimization Retinex Decomposition and Enhancement with a Multi-Branch Decomposition Network

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Chinese Journal of Electronics 2023年第4期32卷 908-919页

作者： DENG Jiawei YU Zhenming PANG Guangyao Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University School of Information and Communication Guilin University of Electronic Technology

This paper proposes a colour variation minimization retinex decomposition and enhancement with a multi-branch decomposition network(CvmD-net)to remove single image darkness. The network overcomes the problem that retinex deep learning model relies on matching bright images to process dark images. Specifically, our method takes two stages to light up the darkness in initial images: image decomposition and brightness optimization. We propose an input constant feature prior mechanism(ICFP) based on reflection constant features. The mechanism extracts structure and colour from the input images and constrains the reflected images output from the decomposition model to reduce color distortion and artifacts. The noise amplification during decomposition is addressed by a multi-branch decomposition network. Sub-networks with different structures are employed to focus on different prediction tasks. This paper proposes a reference mechanism for input brightness. This mechanism optimizes the output brightness distribution by calculating the reference brightness of the dark *** results on two benchmark datasets, namely,LOL and ZeroDCE, demonstrate that the proposed method can better balance dense noise interference and colour restoration. For the evaluation on real images, we collect Skynet images at night to verify the performance of the proposed approach. Compared with the state-of-the-art non-reference retinex decomposition-enhancement models,this paper has the best brightness optimization.

关键词： Reflectivity Image color analysis Brightness Interference Feature extraction Minimization Reflection

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control of 2-D Semi-Markov Jump Systems:A View from Mode Generation Mechanism

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 258-260页

作者： Yunzhe Men Jian Sun Jie Chen the National Key Laboratory of Autonomous Intelligent Unmanned Systems the School of AutomationBeijing Institute of TechnologyBeijing 100081 the Beijing Institute of Technology Chongqing Innovation Center Chongqing 401120China the Department of Control Science and Engineering Tongji UniversityShanghai 201804 the National Key Laboratory of Autonomous Intelligent Unmanned Systems Beijing Institute of TechnologyBeijing 100081China

Dear Editor,Two-dimensional(2-D) systems have wide applications in image data processing,gas absorption and fluid dynamics analysis [1]-[3].When there exist abrupt changes in 2-D systems,they are usually modeled by 2-D Markov jump systems(MJSs) or 2-D semi-Markov jump systems(SMJSs).This letter investigates the control of 2-D SMJSs based on a novel mode generation mechanism,which could avoid mode ambiguousness phenomenon caused by the evolution of system mode in two different *** criterion that guarantees the almost surely exponential stability of the system is obtained.A thermal process is studied to demonstrate the availability of the proposed method.

关键词： Markov mode system

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A Bio-inspired Mutual-hook Strategy for the Soft Finger to Improve Load-bearing Capacity and Grasping Stability

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Journal of Bionic Engineering 2024年第3期21卷 1290-1304页

作者： Jie Huang Lingjie Gai Xiaofeng Zong Yunquan Li School of Automation China University of GeosciencesWuhan430074China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System Wuhan430074China The Shien-Ming Wu School of Intelligent Engineering South China University of TechnologyGuangzhou510641China

Soft grippers have great potential applications in daily life,since they can compliantly grasp soft and delicate ***,the highly elastic fingers of most soft grippers are prone to separate from each other while grasping objects due to their low stiffness,thus reducing the grasping stability and load-bearing *** tackle this problem,inspired from the venus flytrap plant,this work proposes a mutual-hook mechanism to restrain the separation and improve the grasping performance of soft *** novel soft gripper design consists of three modules,a soft finger-cot,two Soft Hook Actuators(SHAs)and two sliding ***,the soft finger-cot covers on the soft finger,increasing the contact area with the target object,two SHAs are fixed to the left and right sides of the finger-cot,and the sliding mechanisms are designed to make SHAs stretch *** demonstrate that the proposed design can restrain the separation of soft fingers substantially,and the soft fingers with the finger-cots can grasp objects three times heavier than the soft fingers without the proposed *** proposed design can provide invaluable insights for soft fingers to restrain the separation while grasping,thus improving the grasping stability and the load-bearing capacity.

关键词： Soft robotics Soft bionic finger-cot Soft hook actuator Grasping stability Load-bearing capacity

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Distributed Optimization and Scaling Design for Solving Sylvester Equations

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Journal of Systems Science & Complexity 2024年第6期37卷 2487-2510页

作者： CHENG Songsong YU Xin ZENG Xianlin LIANG Shu HONG Yiguang Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and AutomationAnhui UniversityHefei 230601China Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China Department of Control Science and Engineering Tongji UniversityShanghai 201804China Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 201210China

This paper develops distributed algorithms for solving Sylvester *** authors transform solving Sylvester equations into a distributed optimization problem,unifying all eight standard distributed matrix *** the authors propose a distributed algorithm to find the least squares solution and achieve an explicit linear convergence *** results are obtained by carefully choosing the step-size of the algorithm,which requires particular information of data and Laplacian *** avoid these centralized quantities,the authors further develop a distributed scaling technique by using local information *** a result,the proposed distributed algorithm along with the distributed scaling design yields a universal method for solving Sylvester equations over a multi-agent network with the constant step-size freely chosen from configurable ***,the authors provide three examples to illustrate the effectiveness of the proposed algorithms.

关键词： Distributed optimization least squares solution linear convergence rate step-size interval Sylvester equation

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Unified Design Method of Filters for Different Measurement Phases in Space Relative Navigation Tasks

Unified Design Method of Filters for Different Measurement P...

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2024 China Automation Congress, CAC 2024

作者： Li, Qing Lu, Shan Zhang, Shiyuan Zhou, Jingjing Xu, Chen Shanghai Aerospace Control Technology Institute Shanghai China Shanghai Aerospace Control Technology Institute Shanghai Key Laboratory of Aerospace Intelligent Control Technology Shanghai China

ISBN: (纸本)9798350368604

In spacecraft relative navigation, as the relative distance changes, the output varies with different measurement devices. For traditional filtering methods, different filters need to be switched when the measurement output changes, which reduces the computational efficiency. Considering this problem, a simplified converted measurement Kaiman filter (SCMKF) is designed to construct a unified filter. The proposed unified filter simplifies the calculation of the measurement noise covariance matrix, based on the analysis on the factors affecting measurement noise and the accuracy of measurement device. Compared to the traditional filter, the proposed unified filter decreases the computational complexity significantly on the premise of ensuring the calculation accuracy. © 2024 IEEE.

关键词： Wiener filtering

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Aggressive and robust low-level control and trajectory tracking for quadrotors with deep reinforcement learning

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Neural Computing and Applications 2025年第3期37卷 1223-1240页

作者： Chen, Shiyu Li, Yanjie Lou, Yunjiang Lin, Ke Department of Control Science and Engineering Harbin Institute of Technology Shenzhen Shenzhen518055 China Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Shenzhen518055 China

Executing accurate trajectory tracking tasks using a high-performance low-level controller is crucial for quadrotors to be applied in various scenarios, especially those involving uncertain disturbances. However, due to the uncertainties in disturbed environments, developing effective low-level controllers with traditional model-based control schemes is challenging. This paper presents an aggressive and robust reinforcement learning (RL)-based low-level control policy for quadrotors. The policy maps the observed quadrotor state directly to motor thrust commands, without requiring the quadrotor dynamics. Additionally, a trajectory generation pipeline is developed to improve the accuracy of trajectory tracking tasks based on differential flatness. With the learned low-level control policy, extensive simulations and real-world experiments are implemented to validate the performance of the policy. The results indicate that our RL-based low-level control policy outperforms traditional proportional–integral–derivative (PID) control methods and related learning-based policies in terms of accuracy and robustness, particularly in environments with uncertain disturbances. Furthermore, the proposed RL-based control policy exhibits an aggressive response in trajectory tracking, even when the speed of the desired trajectory is increased to 6 m/s. Moreover, the learned policy demonstrates strong vibration suppression capabilities and enables the quadrotor to recover to a hovering state from random initial conditions with shorter response time. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Deep reinforcement learning

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A Noise-Excitation Generative Adversarial Network for Actuator Fault Diagnosis of Multi-legged Robot

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无人系统（英文） 2023年第2期11卷 159-173页

作者： Liling Ma Jian Guo Jiehao Li Junzheng Wang State Key Laboratory of Complex System Intelligent Control and Decision School of Automation Beijing Institute of TechnologyBeijing 100081P.R.China

This research provides a novel approach for detecting multi-legged robot actuator *** most significant concept is to design the Fault Diagnosis Generative Adversarial Network(FD-GAN)to fully adapt to the fault diagnosis problem with insufficient *** found that it is difficult for methods based on classification and prediction to learn failure patterns without enough data.A straightforward solution is to use massive amounts of normal data to drive the diagnostic *** introduce frequency-domain information and fuse multi-sensor data to increase the features and expand the difference between normal data and fault data.A GAN-based framework is designed to calculate the probability that the enhanced data belongs to the normal *** uses a generator network as a feature extractor,and uses a discriminator network as a fault probability evaluator,which creates a new use of GAN in the field of fault *** the many learning strategies of GAN,we find that a key point that can distinguish the two types of data is to use the hidden layer noise with appropriate discrimination as the *** also design a fault location method based on binary search,which greatly improves the search efficiency and engineering value of the entire *** have conducted a lot of experiments to prove the diagnostic effectiveness of our architecture in various road conditions and working *** compared FD-GAN with popular diagnostic *** results show that our method has the highest accuracy and recall rate.

关键词： Actuator fault diagnosis multi-legged robot generative adversarial network attention mechanism

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Study on Dry Electrical Discharge Assisted Grinding of SiCp/Al Composite

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Chinese Journal of Mechanical Engineering 2025年第1期38卷 106-118页

作者： Yanjun Lu Yuming Huang Xiaobu Liu Rong Cheng Shunda Zhan Guangdong Provincial Key Laboratory of Micro/Nano Optomechatronics Engineering College of Mechatronics and Control EngineeringShenzhen UniversityShenzhen 518060China School of Intelligent Manufacturing and Equipment Shenzhen Institute of Information TechnologyShenzhen 518172China

SiC-reinforced aluminum matrix(SiCp/Al)composite is widely utilized in the aerospace,automotive,and electronics industries due to the combination of ceramic hardness and metal ***,the significant disparity in properties between SiC particles and the aluminum matrix results in severe tool wear and diminished surface quality during conventional *** study proposes an environmentally friendly and clean dry electrical discharge assisted grinding process as an efficient and low-damage machining method for SiCp/*** experimental platform was set up to study the impact of grinding and discharge process parameters on surface *** study compared the chip formation mechanism and surface quality between dry electrical discharge assisted grinding and conventional grinding,revealing relationships between surface roughness,grinding force,grinding temperature,and related *** results indicate that the proposed grinding method leads to smaller chip sizes,lower grinding forces and temperatures,and an average reduction of 19.2%in surface roughness compared to conventional *** axial,tangential,and normal grinding forces were reduced by roughly 10.5%,37.8%,and 23.0%,*** optimized process parameters were determined to be N=2500 r/min,vf=30 mm/min,a=10μm,E=15 V,f=5000 Hz,dc=80%,resulting in a surface roughness of 0.161μm.

关键词： Dry electrical discharge assisted grinding SiCp/Al composite Surface quality Grinding force Grinding temperature

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Attention-Modulated Feature Fusion Neural Network for Inverse Modeling of Microwave Filters

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IEEE Microwave and Wireless technology Letters 2025年第4期35卷 376-379页

作者： Guo, Linwei Cao, Weihua Hu, Wenkai Lu, Zhengyang Wu, Min China University of Geosciences School of Automation Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

Inverse modeling is extensively applied in the design and tuning of microwave filters (MFs). Inverse models (IMs) take the features extracted from the high-dimensional electromagnetic parameters as input. How to make full use of features from multiple perspective is a critical issue for improving model accuracy. To solve it, this letter proposes an attention-modulated feature fusion neural network (AMFFNN). AMFFNN achieves multiperspective feature fusion (MPFF) at the input side and independent feature fusion (IFF) at the output side. In addition, feature fusion in AMFFNN is enhanced by attention modules to dynamically identify the importance of each feature. Statistical and comparative results of simulations demonstrate that AMFFNN outperforms existing methods in terms of accuracy, stability, and generalization. © 2023 IEEE.

关键词： Inverse problems

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