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5th International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2018

作者： Cao, Zhengran Li, Chuandong Li, Hongfei Chen, Caiping College of Electronic and Information Engineering National Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Southwest University Chongqing400715 China

ISBN: (纸本)9781538670880

The paper primarily investigates the fixed-time consensus problem of multi-agent systems (MAS) with impulsive effect. We firstly present a novel and suitable fixed-time protocol to solve the fixed-time consensus problems of MAS with impulsive effect. Under the control protocol, it is theoretically demonstrated that fixed-time consensus of MAS could be legitimately solved. Finally, some numerical simulations are given to verify the feasibility and effectiveness of the theoretical results. © 2018 IEEE.

关键词： Multi agent systems

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Nonlinear Mathematical Modeling and Sensitivity Analysis of Hydraulic Drive Unit

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Chinese Journal of Mechanical engineering 2015年第5期28卷 999-1011页

作者： KONG Xiangdong YU Bin QUAN Lingxiao BA Kaixian WU Liujie National Engineering Research Center for Local Joint of Advanced Manufacturing Technology and Equipment Yanshan University Hebei Provincial Key Laboratory of Heavy Machinery Fluid Power Transmission and Control Yanshan University School of Mechanical Engineering Yanshan University

The previous sensitivity analysis researches are not accurate enough and also have the limited reference value, because those mathematical models are relatively simple and the change of the load and the initial displacement changes of the piston are ignored, even experiment verification is not conducted. Therefore, in view of deficiencies above, a nonlinear mathematical model is established in this paper, including dynamic characteristics of servo valve, nonlinear characteristics of pressure-flow, initial displacement of servo cylinder piston and friction nonlinearity. The transfer function block diagram is built for the hydraulic drive unit closed loop position control, as well as the state equations. Through deriving the time-varying coefficient items matrix and time-varying free items matrix of sensitivity equations respectively, the expression of sensitivity equations based on the nonlinear mathematical model are obtained. According to structure parameters of hydraulic drive unit, working parameters, fluid transmission characteristics and measured friction-velocity curves, the simulation analysis of hydraulic drive unit is completed on the MATLAB/Simulink simulation platform with the displacement step 2 mm, 5 mm and 10 mm, respectively. The simulation results indicate that the developed nonlinear mathematical model is sufficient by comparing the characteristic curves of experimental step response and simulation step response under different constant load. Then, the sensitivity function time-history curves of seventeen parameters are obtained, basing on each state vector time-history curve of step response characteristic. The maximum value of displacement variation percentage and the sum of displacement variation absolute values in the sampling time are both taken as sensitivity indexes. The sensitivity indexes values above are calculated and shown visually in histograms under different working conditions, and change rules are analyzed. Then the sensitivity index

关键词： nonlinear mathematical model hydraulic drive unit valve-controlled symmetrical cylinder sensitivity analysis sensitivity index

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Exploring reservoir computing:Implementation via double stochastic nanowire networks

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Chinese Physics B 2024年第3期33卷 572-582页

作者：唐健峰夏磊李广隶付军段书凯王丽丹 College of Artificial Intelligence Southwest UniversityChongqing 400715China Brain-inspired Computing & Intelligent Control of Chongqing Key Laboratory Chongqing 400715China National & Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing 400715China Chongqing Brain Science Collaborative Innovation Center Chongqing 400715China Key Laboratory of Luminescence Analysis and Molecular Sensing Ministry of EducationSouthwest UniversityChongqing 400715China State Key Laboratory of Intelligent Vehicle Safety Technology Chongqing 400715China

Neuromorphic computing,inspired by the human brain,uses memristor devices for complex *** studies show that self-organizing random nanowires can implement neuromorphic information processing,enabling data *** paper presents a model based on these nanowire networks,with an improved conductance variation *** suggest using these networks for temporal information processing via a reservoir computing scheme and propose an efficient data encoding method using voltage *** nanowire network layer generates dynamic behaviors for pulse voltages,allowing time series prediction *** experiment uses a double stochastic nanowire network architecture for processing multiple input signals,outperforming traditional reservoir computing in terms of fewer nodes,enriched dynamics and improved prediction *** results confirm the high accuracy of this architecture on multiple real-time series datasets,making neuromorphic nanowire networks promising for physical implementation of reservoir computing.

关键词： double-layer stochastic(DS)nanowire network architecture neuromorphic computation nanowire network reservoir computing time series prediction

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Dynamic Memristor based Cyclic Echo State Network for Time Series Prediction 4

Dynamic Memristor based Cyclic Echo State Network for Time S...

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4th International Conference on Neural Networks, Information and Communication engineering, NNICE 2024

作者： Li, Guangli Xia, Lei Fu, Jun Duan, Shukai Tang, Jianfeng Wang, Lidan Southwest University College of Artificial Intelligence Chongqing China National Local Joint Engineering Research Center of Intelligent Transmission and Control Technology Chongqing China

ISBN: (纸本)9798350394375

Compared with traditional recurrent neural networks, the training cost of echo-state network computing is lower because the neurons in the reservoir are randomly connected. We only need to train the weight of the connection between the reservoir and the output layer. Due to its high accuracy and low training cost for time series forecasting tasks, it is widely used as a time series forecasting model in many fields. It is very important to provide rich reservoir state for echo state network system, which is the key to hardware implementation. In this paper, we propose a cyclic echo state network (DC-ESN) based on dynamic memristors, which generates rich reservoir states through cyclic structure, and can predict complex time series well even with a single memristor. Compared with the traditional echo state network, the size of the reservoir is reduced and the prediction accuracy is greatly improved. Our work can provide an idea for more efficient memristor based echo state network systems in the future to cope with complex time series prediction tasks. © 2024 IEEE.

关键词： Memristors

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Force Compensation control for Electro-Hydraulic Servo System with Pump-Valve Compound Drive via QFT-DTOC

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Chinese Journal of Mechanical engineering 2024年第2期37卷 228-246页

作者： Kaixian Ba Yuan Wang Xiaolong He Chunyu Wang Bin Yu Yaliang Liu Xiangdong Kong Hebei Provincial Key Laboratory of Heavy Machinery Fluid Power Transmission and Control Qinhuangdao 066004China School of Mechanical Engineering Yanshan UniversityQinhuangdao 066004China National Engineering Research Center for Local Joint of Advanced Manufacturing Technology and Equipment Yanshan UniversityQinhuangdao 066004China

Each joint of a hydraulic-driven legged robot adopts a highly integrated hydraulic drive unit(HDU),which features a high power-weight ***,most HDUs are throttling-valve-controlled cylinder systems,which exhibit high energy *** contrast,pump control systems offer a high ***,their response ability is *** fully utilize the advantages of pump and valve control systems,in this study,a new type of pump-valve compound drive system(PCDS)is designed,which can not only effectively reduce the energy loss,but can also ensure the response speed and response accuracy of the HDUs in robot joints to satisfy the performance requirements of ***,considering the force control requirements of energy conservation,high precision,and fast response of the robot joint HDU,a nonlinear mathematical model of the PCDS force control system is first *** addition,pressure-flow nonlinearity,friction nonlinearity,load complexity and variability,and other factors affecting the system are considered,and a novel force control method based on quantitative feedback theory(QFT)and a disturbance torque observer(DTO)is designed,which is denoted as QFT-DTOC *** method improves the control accuracy and robustness of the force control system,reduces the effect of the disturbance torque on the control performance of the servo motor,and improves the overall force control performance of the ***,experimental verification is performed using the PCDS performance test *** experimental results and quantitative data show that the QFT-DTOC proposed herein can significantly improve the force control performance of the *** relevant force control method can be used as a bottom-control method for the hydraulic servo system to provide a foundation for implementing the top-level trajectory planning of the robot.

关键词： Legged robot Pump-valve compound drive system(PCDS) Force compensation control Quantitative feedback theory(QFT) Disturbance torque observer(DTO)

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control characteristics of D + A combined multi-pump controlled system

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High technology Letters 2018年第3期24卷 303-312页

作者：姚静 Wang Pei Cao Xiaoming Zhang Yang Kong Xiangdong College of Mechanical Engineering Yanshan University The Laboratory of Heavy Machinery Fluid Power Transmission and Control in Hebei Yanshan University National and Local Joint Engineering Center for Advanced Forging Press Forming Technology and Equipment Faculty of Engineering Yanshan University

In order to solve the flow mismatch problem between pumping source output and workload demand,a novel configuration of D + A combined multi-pump controlled hydraulic system,similar to a pump-controlled system,is proposed for a large power hydraulic system in this study. This novel configuration consists of several parallel fixed displacement pumps of different sizes and proportional variable displacement pumps,which is controlled by digital signal( on/off) and analog signal respectively( D + A pumps). The system flow is divided into two parts,one is the total flow from fixed displacement pumps,and the other is the rest desired flow supplied by variable displacement pumps to smooth and improve the demand flow. First,basic design principles and evaluation indicators of the proposed system are introduced. Then,a flow state matrix of the binary-coding digital pumps( 1: 2: 4) is obtained to provide the control signals of pumps. Experimental results show that the system output flow tracks well with acceptable flow deviation,though a little lag behind input signal.

关键词： multi-pump design principles flow state matrix evaluation indicator energy saving

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Spatio- Temporal Neural Network with Contrastive Learning for Vehicle Trajectory Prediction

Spatio- Temporal Neural Network with Contrastive Learning fo...

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2023 International Annual Conference on Complex Systems and intelligent Science, CSIS-IAC 2023

作者： Li, Jiufa Duan, Shukai Wang, Lidan Southwest University College of Artificial Intelligence Chongqing China College of Artificial Intelligence Southwest University National & Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing China

ISBN: (纸本)9798350309003

In the field of autonomous driving vehicles, accurately predicting vehicle trajectories is crucial for ensuring safety and enhancing ride comfort. However, predicting the trajectory of a target vehicle is challenging due to complex interaction dynamics with nearby vehicles in different scenarios. To improve prediction accuracy, we introduce a novel spatiotemporal neural network with contrastive learning capabilities. This method delves deep into potential encodings within interaction scenes from both local and global perspectives and incorporates future trajectory features through a Conditional Variational Autoencoder (CVAE). We rigorously evaluate this approach through ablation experiments using the US-101 and I-80 datasets from the national Highway Traffic Safety Administration's Next Generation Simulation (NGSIM) dataset. The research results demonstrate that our method outperforms the current state-of-the-art techniques in terms of performance. © 2023 IEEE.

关键词： Trajectories

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An Adaptive Physics-Informed Neural Network with Two-Stage Learning Strategy to Solve Partial Differential Equations

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Numerical Mathematics(Theory,Methods and Applications) 2023年第2期16卷 298-322页

作者： Shuyan Shi Ding Liu Ruirui Ji Yuchao Han School of Automation and Information Engineering Xi‘an University of TechnologyXi‘an 710048P.R.China National&Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Xi‘an 710048P.R.China Xi’an University of Technology Shannxi Key Laboratory of Complex System Control and Intelligent Information ProcessingXi‘an 710048P.R.China

Physics-Informed Neural Network(PINN)represents a new approach to solve Partial Differential Equations(PDEs).PINNs aim to solve PDEs by integrating governing equations and the initial/boundary conditions(I/BCs)into a loss ***,the imbalance of the loss function caused by parameter settings usually makes it difficult for PINNs to converge,*** they fall into local *** other words,the presence of balanced PDE loss,initial loss and boundary loss may be critical for the *** addition,existing PINNs are not able to reveal the hidden errors caused by non-convergent boundaries and conduction errors caused by the PDE near the ***,these problems have made PINN-based methods of limited use on practical *** this paper,we propose a novel physics-informed neural network,*** adaptive physics-informed neural network with a two-stage training *** algorithm adds spatio-temporal coefficient and PDE balance parameter to the loss function,and solve PDEs using a two-stage training process:pre-training and formal *** pre-training step ensures the convergence of boundary loss,whereas the formal training process completes the solution of PDE by balancing various loss *** order to verify the performance of our method,we consider the imbalanced heat conduction and Helmholtz equations often appearing in practical *** Klein-Gordon equation,which is widely used to compare performance,reveals that our method is able to reduce the hidden *** results confirm that our algorithm can effectively and accurately solve models with unbalanced loss function,hidden errors and conduction *** codes developed in this manuscript are publicy available at https://***/callmedrcom/ATPINN.

关键词： Physics informed neural networks partial differential equations two-stage learning scientific computing

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Continuous-energy harvesting from soils based on reversible hydrolysis process for self-power memristor system

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Nano Energy 2025年 142卷

作者： Ren, Zhijun Kuang, Dalong Gu, Dengshun Song, Qunliang Wang, Lidan Xu, Cunyun Dai, Zhongjun He, Xiaofeng Jiang, Zezhuan Yan, Jia Hu, Xiaofang Dong, Jun Sun, Bai Chen, Yuanzheng Guo, Hengyu Duan, Shukai Zhou, Guangdong College of Artificial Intelligence College of Resources and Environment School of Materials and Energy National & Local Joint Engineering Research Center of Intelligent Transmission and Control Technology Chongqing Key Laboratory of Brain-inspired Computing and Intelligent Chips Key Laboratory of Luminescence Analysis and Molecular Sensing (Southwest University) Ministry of Education Southwest University Chongqing 400715 China Frontier Institute of Science and Technology Xi'an Jiaotong University Shanxi 710049 China School of Physical Science and Technology Southwest Jiaotong University Sichuan Chengdu 610031 China College of Physics Chongqing University Chongqing 400044 China

Harvesting energy from the nature such as ocean, wind, solar and so on offers one of most promising clean power for self-sustained system. There has significantly been demonstrated on the variety of applications from nano/micro-electronic device to TW energy supply through special material engineering and elaborate structure design. Here we show that a simple device made from pristine soils without any chemical processing can generate continuous electricity power. The devices can generate a sustained voltage of around 0.5 volts with a power density around 0.35μW/cm after the soils immersing in water. Connecting several devices in series or parallel can linearly scale up the voltage and current to power electronics such as memristor and liquid crystal display. Gradient distribution of water and ions originated from self-maintained hydrolyzation in soils yields streaming potential and ionic current. Our results demonstrate a novel continuous energy-harvesting approach that is less restriction in material, structure, or environment conditions than other sustainable technologies. © 2025 Elsevier Ltd

关键词： Hydrolysis process Memristor Self-power system Water-soil generators

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Stochastic Resonance of Asymmetric Bistable System Driven by Binary Signals under α Stable Noise 35

Stochastic Resonance of Asymmetric Bistable System Driven by...

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第35届中国控制会议

作者： JIAO Shang-bin KOU Jie LIU Ding ZHANG Qing National & Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing

ISBN: (纸本)9781509009107

In this paper the asymmetric bistable system excited by binary aperiodic signals is taken as a model and the average symbol error rate is regarded as an index to study stochastic resonance(SR) ***,the SR driven by binary signals under α stable noise is ***,the interplay between the α stable noise parameters α,β,and the system parameters a,b,r on the resonant output effect is *** results show that weak binary signals detection can be realized by adjusting the system parameters a,b and *** optimal values solved by these parameters can make the system produce the best SR *** a certain a or b,there is an optimal value under different α or β.For the parameter r,there is an optimal value under different α,and there are several optimal values under different β.Moreover,when α or β is given different values,the evolution laws in asymmetric bistable SR system excited by binary signals are *** results lay a foundation for realizing the adaptive parameter adjustment in asymmetric bistable SR system with α stable noise.

关键词： α stable noise asymmetric bistable system stochastic resonance symbol error rate

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