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第42届中国控制会议

作者： Feng Liu Feifan Zhou Wei Wang Gang Cai Hua O Wang Xiuqin Yang School of Automation China University of Geosciences 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 Department of Mechanical Engineering Boston University School of Education Minzu University of China

This article investigates a fractional-order coupled Hindmarsh-Rose neural networks ***,the existence and stability of an equilibrium point in the system are ***,the periodic bifurcation behavior of the system on a twoparameter plane is studied,and numerical simulations show the existence of both non-chaotic and chaotic plus periodic bifurcation behavior on the two-parameter ***,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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Finite-time sliding-mode control for semi-Markov systems with delayed impulses

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IEEE/CAA Journal of Automatica Sinica 2025年

作者： Ren, Fangmin Wang, Xiaoping Li, Yangmin Zeng, Zhigang The School of Artificial Intelligence and Automation The Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Hubei Key Laboratory of Braininspired Intelligent Systems Huazhong University of Science and Technology Wuhan430074 China The Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Hong Kong999077 Hong Kong

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Stability and Bifurcation control of Fractional BAM Neural Networks with Diffusion and Time Delays

Stability and Bifurcation control of Fractional BAM Neural N...

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Chinese control Conference (CCC)

作者： Feng Liu Wei Wang Gang Cai Feifan Zhou 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 MA USA School of Education Minzu University of China Beijing China

Based on fractional calculus theory and reaction-diffusion equation theory, a fractional-order time-delay reaction-diffusion neural network with Neumann boundary conditions is investigated. By constructing the phase space basis based on the Laplace operator eigenvector, the system equation is linearized to obtain the characteristic equation. Then, the characteristic equation is analyzed, and the local stability of the system at the equilibrium point is discussed. And taking the time delay as the bifurcation parameter, the stability changes of the system at the equilibrium point and the generation conditions of the Hopf bifurcation are studied when the time delay changes. Moreover, a state feedback controller is designed to control the bifurcation of the system. Finally, the theoretical derivation is verified by numerical simulation.

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Dynamic Analysis and control of Fractional Order Memristor Hopfield Neural Network

Dynamic Analysis and Control of Fractional Order Memristor H...

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Chinese control Conference (CCC)

作者： Feng Liu Gang Cai Feifan Zhou Wei Wang Hua 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 MA USA School of Education Minzu University of China Beijing China

In this paper, a novel smooth magnetron is introduced to construct a fractional memristor Hopfield neural network (fractional order M-HNN). The local stability of equilibrium point are analyzed theoretically. Taking the memristor coupling strength coefficient and the fractional order as bifurcation parameters, the phase trajectory diagram, the bifurcation diagram of the system are drawn to analyze the influence on the dynamic behavior of the neural network. When the system parameters are fixed, the hyperchaos phenomenon of the fractional order M-HNN model is revealed. Finally, the PD controller is applied to the model to enhance the stability of the system.

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Broad-Deep Network-Based Fuzzy Emotional Inference Model with Personal Information for Intention Understanding

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IFAC-PapersOnLine 2023年第2期56卷 7071-7076页

作者： Min Li Luefeng Chen Min Wu Min Ding Kaoru Hirota Witold Pedrycz 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 Tokyo Institute of Technology Yokohama 226-8502 Japan Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6R 2G7 Canada

A broad-deep fusion network-based fuzzy emotional inference model with personal information (BDFEI) is proposed for emotional intention understanding in human-robot interaction. It aims to understand students’ intentions in the university teaching scene. Specifically, feature extraction is carried out by convolution and maximum pooling, and then the ridge regression algorithm is used for emotional behavior recognition, which reduce the influence of the complex structure and slow network updates in deep learning. Multivariate analysis of variance is used to select the key personal information affecting the intention and obtain the coefficient of influence degree. Finally, fuzzy inference is used to understand the intention. According to the recognition results, the accuracy on FABO database of our proposal is 1.89%, 12.21% and 0.78% higher than those of the Residual Network combined with geodesic flow kernel (ResNet-101+GFK), a fuzzy deep neural network with sparse autoencoder (FDNNSA), and an affect recognition on a video-skeleton of bimodal information with a hierarchical classification fusion strategy (HCFS), respectively, indicating that our proposal can effectively capture the emotional intention of students in the teaching scene.

关键词： Convolution Neural Networks Broad Learning Emotional Intention Human-Robot Interaction

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A Novel Brain-Inspired Hierarchical Perception-Association Circuit Based on Memristor Arrays

SSRN

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SSRN 2023年

作者： Chen, Liangyu Wang, Xiaoping Chen, Zhanfei Yang, Chao Zeng, Zhigang School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Education Ministry of China Wuhan430074 China Hubei Key Laboratory of Brain-inspired Intelligent Systems Wuhan430074 China

In the brain, primary sensory cells can efficiently perceive multimodal stimuli, and then associative memory cells perform an advanced bidirectional associative memory function with perceived information. Here, a brain-inspired hierarchical perception-association circuit based on memristor arrays is proposed. Firstly, a memristive reservoir computing circuit is designed to simulate a low-level perceptual function, which mainly comprises dynamic analog reservoirs, memristor arrays, and analog integrators, enabling efficient spatio-temporal information processing. Secondly, a spiking bidirectional associative memory memristive circuit, as the core of the whole circuit, is proposed to mimick a high-level associative function, which mainly consists of memristor arrays, current amplifiers, and leaky integrate-and-fire neuron circuits, implementing multimodal associative learning. The simulation results in LTspice show that the modified neuron circuit exhibits 66× improvement in energy efficiency, and the proposed circuit can precisely perform the letter association and vision-audio association tasks in a spiking fashion with an average firing energy consumption of 230 pJ/spike, which has a great potential to be embedded in mobile robotic platforms. © 2023, The Authors. All rights reserved.

关键词： Memristors

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Reconstruction of spectral reflectance based on fusion convolution neural network 9

Reconstruction of spectral reflectance based on fusion convo...

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9th International Conference on Computing and Pattern Recognition, ICCPR 2020

作者： Zhang, Lijun Wang, Xiangguo Zhang, Jingjing Qiao, Jinlong Li, Penghui School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China

ISBN: (纸本)9781450387835

An improved method for spectral reflectance reconstruction from the RGB response of the digital camera is proposed by deep convolution neural network. The proposed method learns a fusion mapping theory that represents the basic characteristics of spectral reflectance, including spectral nonlinear mapping and spatial similarity. The deep residual network greatly deepens the network structure and improves the performance of the model, enabling the recovery network to make full use of the sample structure information to accurately recover the spectral reflectance. The performance of the method is compared with several existing methods in the cases of the same experimental data set. Experimental results show that the proposed method has the best accuracy in estimating spectral reflectance and colorimetric values in most cases in comparison with existing methods. © 2020 ACM.

关键词： Deep neural networks

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Sound Field control by Using Equivalent Input Disturbance method

Sound Field Control by Using Equivalent Input Disturbance me...

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Industrial Cyber-Physical systems (ICPS)

作者： Toshiki Watanabe Jinhua She Zewen Wang Department of Sustainable Engineering Tokyo university of technology Japan 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 Laboratory for Future Interdisciplinary Research of Science and Technology (FIRST) Tokyo Institute of Technology Yokohama Kanagawa Japan

Low-frequency noise, such as road noise from automobiles and interior noise from bullet train cars, is detrimental to passenger comfort. Active noise control provides us with a way of suppressing low-frequency noise. It generates a sound of the same amplitude and opposite phase as noise and uses it to cancel the noise. Conventional active noise control is complicated to apply. Thus, the objective of this study is to establish a sound field control system by using the equivalent-input-disturbance method. System identification was performed to obtain an ideal plant of a duct. The control system is constructed based on the equivalent-input-disturbance approach with a low-order controller. The experimental results showed a suppression effect of about 50% of the sound field.

关键词： Roads Noise reduction Ducts Channel estimation Auditory system Cyber-physical systems control systems

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The Analysis of Muscle-Synergy Similarity between Walking and Pedaling Based on Variational Mode Decomposition and Non-Negative Matrix Factorization

The Analysis of Muscle-Synergy Similarity between Walking an...

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IEEE International Conference on Engineering, Technology and Innovation

作者： Wangyang Ge Jinhua She Shijie Guo Feng Wang Juan Zhao Seiichi Kawata 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 School of Engineering Tokyo University of Technology Hachioji Tokyo Japan Engineering Research Center of the Ministry of Education for Intelligent Rehabilitation Equipment and Detection Technologies Hebei Key Laboratory of Robot Sensing and Human-Robot Interaction Tianjin China School of Mechanical Engineering Hebei University of Technology Tianjin China

ISBN: (数字)9798350362435

ISBN: (纸本)9798350362442

Pedaling lower-limb rehabilitation robots are widely used in various stages of lower-limb rehabilitation because of their high safety and low requirements for patients' lower-limb strength. Researchers found that the activation sequence of the surface electromyographic (sEMG) signals of the muscles of the lower limb during pedaling is very similar to that of walking, which makes it possible to simulate the muscle force generation during walking by changing the pedal load during pedaling training. However, most pedaling lower-limb rehabilitation robots use constant loads, and it is of great importance to study the relationship between load and muscle synergy and to design load phase angle trajectories with higher muscle synergy consistency in the walking-pedaling motion to optimize the training effect of pedal-type rehabilitation robots. In this paper, we used variational mode decomposition (VMD) and nonnegative matrix factorization (NMF) to extract the muscle synergy features in each band of sEMG during walking and pedaling and to study the similarity of pedaling and walking and the influence of pedal load on muscle synergy consistency.

关键词： Legged locomotion Training Technological innovation Time series analysis Muscles Assistive robots Feature extraction Trajectory Safety Matrix decomposition

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Active Compliance Variable Impedance control of Lower Supernumerary Robotic Limb*

Active Compliance Variable Impedance Control of Lower Supern...

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International Conference on advanced Robotics and Mechatronics (ICARM)

作者： Jun Huo Xinyi Wang Bo Yang Qingyang Yan Zhaofan Yuan Jian Huang Hubei Key Laboratory of Brain-inspired Intelligent Systems and the Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China China Telecom Research Institute Guangdong China China Ship Development and Design Centre Wuhan China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

In human-robot systems, ensuring user safety amidst external disturbances remains paramount. An efficient variable impedance control (VIC) technique is designed to enhance human-robot interaction, particularly in scenarios with external force disturbances. By dynamically adapting impedance parameters in response to detected changes in contact force, VIC achieves heightened system flexibility. The robust stability of the VIC system is rigorously established using the Lyapunov approach, with a parameter update law tailored for a lower limb supernumerary robotic limb robot. Through simulations and experiments, the efficacy of the system in maintaining a specific safety margin is demonstrated. Moreover, the technique offers a pragmatic solution to accommodate individual variations in walking gait and cross-user disparities in interaction. The proposed method contributes to advancing the safety and adaptability of cross-user human-robot systems.

关键词： Legged locomotion Adaptation models Mechatronics Robust stability Force Human-robot interaction Safety Impedance Robots Pragmatics

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