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Chinese control Conference (CCC)

作者： Jiajun Chu Weihua Cao Chao Gan Yulong Yang School of Automation China University of Geosciences Wuhan P. R. China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan P. R. China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan P. R. China

Deep-sea unmanned exploration equipment is an important tool for exploring and developing the resources in the ocean, and it can survey the deep-sea environment more visually with the help of visual images. However, the complex and variable environment and the low resolution of the underwater lens lead to the poor resolution of the images acquired by the equipment. In this paper, we propose a residual-dense connected method applied to unmanned deep-sea exploration equipment to improve it's image resolution. The method uses dense connections within the residual structure to improve the model detail information acquisition to ensure accuracy and model stability of model. Secondly, through the study of the model performance, a high precision residual-dense connected model with less computational effort is designed. Finally, the model is trained and tested using environmental images in deep-sea conditions, and it is demonstrated that the method can be applied to deep-sea unmanned exploration equipment for fast, accurate, and stable image super-resolution processing.

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Group Consensus of Multi-agent systems with Additive Noises and Multiplicative Noises

Group Consensus of Multi-agent Systems with Additive Noises ...

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Chinese automation Congress (CAC)

作者： Haoxiang Qin Chuanjian Li Xiaofeng Zong School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

In this paper, we study the group consensus problem of multi-agent systems with additive noises and multiplicative noises. The multi-agent systems are composed of two sub networks affected by additive noises and multiplicative noises, respectively. A consensus protocol is designed to guarantee the group consensus. Based on this protocol, the mean square weak consensus condition and mean square strong consensus condition are obtained by algebraic graph theory and stochastic stability theory. Finally, the numerical simulations show the effectiveness of the group consensus protocol in this paper.

关键词： Additive noise Differential equations Numerical simulation Stability analysis Consensus protocol Thermal stability Numerical stability

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Emotion Recognition via Environmental Context and Human Body

Emotion Recognition via Environmental Context and Human Body

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Industrial Cyber-Physical systems (ICPS)

作者： Cheng-Shan Jiang Zhen-Tao Liu School of Automation China University of Geosciences Wuhan P. R. China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan P. R. China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan P. R. China

To promote the humanized interactive experience of the intelligent device and system, emotional intelligence has become a popular research field in the human-machine interaction. The previous research on emotion recognition based on computer vision has mostly been carried out by analysing facial expression or body posture, and psychological studies show that scene context also contributes some important information on emotion recognition. In addition, most context-aware emotion recognition studies focus on exploring the relevance analysis of environmental semantics, but the influence of feature encoder on semantic information embedding has not been fully discussed. In this paper, we proposed a Global Semantic Feature Enhancement-Dual Stream Densely Connected Network (GSFE-DSDCN) to enhance global semantic information learning from the perspectives of dimension and spatial. Densely connected pattern is introduced to concatenate the shallow and deep layers output, which fuses the semantic information of low-dimensional geometric features and high-dimensional abstract context features together. The Global Multi-Scale Feature Recalibration (GMSFR) module expands the receptive field in spatial, which effectively improves the global semantic features extraction capability of feature encoder. We evaluate the proposed method on the EMOTIC data set, and experimental results are shown to be competitive with the state-of-the-art algorithms.

关键词： Human computer interaction Emotion recognition Computer vision Fuses Biological system modeling Semantics Psychology

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control Strategy of Electrolytic Capacitor-Less Multi-port Converter for Suppressing the Influences of Low-Frequency DC-Link Current Ripple

Control Strategy of Electrolytic Capacitor-Less Multi-port C...

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Smart Power & Internet Energy systems (SPIES), International Conference on

作者： Zihong Zhang Zhijian Fang Yuangeng Xia Hanlin Dong Haojiang Yue School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

Multi-port Converter (MPC) gets growing applications due to its advantages of flexible control mode, high power density, bidirectional power delivery capability, etc. However, the inherent instantaneous power difference between each port will generate significant low frequency dc-link current ripple, which degrades the operational stability of the system. To suppress the influence, the mechanism of dc-link current ripple is analyzed. And then, a compound control strategy based on the repetitive controller (RC) plus a PR controller is adopted according to the ripple characteristics for the electrolytic Capacitor-Less Multi-port Converter. The developed current control strategy in this paper can achieve a minimum steady-state error while maintaining a relatively fast transient response. Finally, to validate the proposed strategy, a simulation is also built and tested. The simulation results validate the theoretical analysis.

关键词： Transient response Power system measurements Simulation Power system dynamics Power system stability Frequency conversion Stability analysis

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Safe Reinforcement Learning for Social Motor Coordination in Two Dimensional Space 26

Safe Reinforcement Learning for Social Motor Coordination in...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Yang, Fan Huang, Yuening Zhai, Chao Zhang, Hehong China University of Geosciences School of Automation Wuhan Campus China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Rice University George R. Brown School of Engineering and Computing HoustonTX United States Fuzhou University School of Computer and Data Science Fuzhou China

ISBN: (纸本)9798331521950

This paper presents an interactive motion control method based on reinforcement learning, designed to assist children with autism who have social motor impairments through a mirror game intervention. The virtual teacher uses the Deep Deterministic Policy Gradient (DDPG) algorithm to optimize its actions, guiding the participant to follow a Lissajous trajectory. To ensure safety, a motion-correction mechanism was developed, which automatically adjusts actions when the predicted trajectory surpasses predefined safety boundaries. The reward function considers both the distance between the virtual teacher and the target trajectory, as well as the distance between the virtual teacher and the participant, with dynamic adjustments applied by the motion-correction mechanism. Experimental results demonstrate that the virtual teacher effectively guides the participant towards the target trajectory while adhering to safety constraints. © 2025 IEEE.

关键词： Reinforcement learning

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Exponential Stability Analysis for Delayed Neural Networks via A Cubic Function Negative-Determination Lemma

Exponential Stability Analysis for Delayed Neural Networks v...

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Chinese control Conference (CCC)

作者： Wen-Xuan Zhao Yun-Fan Liu Yu-Long Fan Chuan-Ke Zhang School of Automation China University of Geosciences Wuhan P. R. China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan P. R. China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan P. R. China

This paper concerns with the exponential stability of delayed neural networks via Lyapunov-Krasovskii functional (LKF) method. Initially, an improved augmented delay-product-type LKF containing an additional double integral state is established, which introduces more delayed states and has less conservatism. In the LKF's derivative, the function has high order of delay due to the existence of exponent. Thus, in order to obtain tractable linear matrix inequalities, three state vectors are used to reduce the order of the function to cubic. Secondly, to achieve the negative-definiteness requirement, a negative-determination lemma for cubic functions with less conservatism is employed. Then, a less conservative delay-dependent stability criterion for neural networks with time-varying delays is established. Finally, the validity of the proposed delay-dependent stability criterion is illustrated by two numerical examples.

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Stability Analysis of Discrete Networked systems with Network Induced Delay and Malicious Packet Dropout Based on a Matrix Transformation Method

Stability Analysis of Discrete Networked Systems with Networ...

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Chinese control Conference (CCC)

作者： Meng-Ying Zhou Wen-Hu Chen Chuan-Ke Zhang Ya-Xin Hou Ke-You Xie School of Automation China University of Geosciences Wuhan P. R. China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan P. R. China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan P. R. China

This paper is concerned with the stability of discrete-time networked control systems with network induced delay and malicious packet dropout. Firstly, network induced delay and malicious packet dropout are analyzed, and the data packet dropout is converted into the change rate of time delay. Secondly, the functional of time delay and change rate of time delay is constructed, and some summation terms are generated when calculating the functional forward difference. Moreover, the auxiliary-function-based summation inequality and reciprocally convex matrix inequality are used to estimate the resulting summation terms. Then, a less conservative stability criterion for discrete networked systems with network delay and data packet loss is established. Finally, the validity of the proposed stability criterion is illustrated by a numerical example.

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Path Planning for Distribution Network Robots with Adaptive Simulated Annealing-Enhanced Artificial Potential Field Method 26

Path Planning for Distribution Network Robots with Adaptive ...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Zhang, Baichao Chen, Xin Chen, Jinge Li, Jinbin Wang, Xiaokai Jian, Xu School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China State Grid Hubei Electric Power Co. Ltd. Electric Power Science Research Institute Wuhan430077 China Hubei Fangyuan Dongli Electric Power Science Research Co. Ltd Wuhan430077 China

ISBN: (纸本)9798331521950

In this paper, we propose a hybrid algorithm that combines an improved Artificial Potential Field (APF) method with the Simulated Annealing (SA) algorithm for path planning of an electric power operation robot manipulator in complex distribution grid environments. To address the unreachable target issue inherent in traditional APF, we introduce a distance regulation factor to optimize the repulsive function. This modification allows the manipulator to smoothly approach the target point as it nears, while the repulsion from obstacles gradually decreases. Additionally, to overcome the limitations of the traditional SA algorithm, such as its tendency to get trapped in local minimum solutions and its inefficiency in complex environments, we propose an adaptive temperature rise strategy. This strategy increases the temperature, enhancing the probability of escaping local optimal solutions. When the APF algorithm becomes trapped in a local optimum, the improved SA algorithm is applied to escape the local minimum. Once the local optimum is avoided, the algorithm switches back to APF to continue the path planning process. Simulation results demonstrate that the proposed improved APF-SA algorithm adapts effectively to various complex environments, achieving shorter planning times and higher success rates compared to traditional APF and SA algorithms. It successfully resolves the unreachable target and local minimum problems associated with APF. Finally, the feasibility of the proposed APF-SA fusion algorithm is validated through experiments conducted on an electric power operation robot experimental platform for distribution grid applications. © 2025 IEEE.

关键词： Motion planning

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Stability and Bifurcation control of Fractional-Order Coupled Hindmarsh-Rose Neural Networks

Stability and Bifurcation Control of Fractional-Order Couple...

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Chinese control Conference (CCC)

作者： Feng Liu Feifan Zhou Wei Wang Gang Cai Hua O Wang Xiuqin Yang School of Automation China University of Geosciences Wuhan China Engineering Research Center of Ministry of education for intelligent technology of earth exploration Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Department of Mechanical Engineering Boston University Boston USA School of Education Minzu University of China Beijing China

This article investigates a fractional-order coupled Hindmarsh-Rose neural networks model. Firstly, the existence and stability of an equilibrium point in the system are verified. Then, the periodic bifurcation behavior of the system on a two-parameter plane is studied, and numerical simulations show the existence of both non -chaotic and chaotic plus periodic bifurcation behavior on the two-parameter plane. Finally, a feedback controller was designed to stabilize the bifurcation point of the delayed system and increase the stable range of the system.

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Q -learning based model-free input-output feedback linearization control method ⋆

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IFAC-PapersOnLine 2023年第2期56卷 9534-9539页

作者： Yipu Sun Xin Chen Wenpeng He Ziying Zhang Edwardo F. Fukushima Jinhua She School of Automation China University of Geosciences Wuhan 430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan 430074 China School of Engineering Tokyo University of Technology Hachioji Tokyo 192-0982 Japan School of Arts and Communication China University of Geosciences Wuhan 430074 China

Input-output feedback linearization is a nonlinear control method that relies on a precise dynamical model. Combining Q -learning techniques, an input-output feedback linearization correction framework is presented to accomplish model-free feedback linearization of affine nonlinear systems in order to tackle the problem caused by the unknown dynamics model. This framework formulates a model reference tracking control problem that guides the input-output relationship of the nonlinear system into a linear relationship. Due to the two Lie derivative terms present in the feedback linearized controller, the controller is designed as a dual network structure. To overcome the issue of coupling in the dual-network controller, a model-free Q -learning method is presented to solve the unknown controller network weights. The proposed method is experimentally validated on a single-link flexible joint manipulator system, and the resultant linearized system exhibits dynamics similar to the desired linear system in a new tracking task, proving the effectiveness of the proposed method.

关键词： Input-output feedback linearization unknown nonlinear systems Q-learning model-free nonlinear control method

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