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Tracking performance limitations of MIMO discrete-time networked control systems with multiple constraints

Science China(Information Sciences) 2023年第8期66卷 267-268页

作者： Xiaowei JIANG Bin ZHANG Xiangyong CHEN Huaicheng YAN School of Automation China University of Geosciences Key Laboratory of Industrial Internet of Things & Networked Control Ministry of Education Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation and Electrical Engineering Linyi University Key Laboratory of Smart Manufacturing in Energy Chemical Process of the Ministry of Education

Dear editor,Networked control systems(NCSs) have attracted widespread attention in some fields because they offer the advantages of reduced cabling, greater resource sharing,and ease of installation and maintenance [1]. However, in an unreliable network environment, network constraints can significantly limit the performance of NCSs.

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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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A Novel Trajectory Planning Method Based on Trust Region Policy Optimization

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on intelligent Vehicles 2024年 1-15页

作者： Ye, Bao-Lin Zhang, Jiajie Li, Lingxi Wu, Weimin Jiaxing Key Laboratory of Smart Transportations the School of Information Science and Engineering Jiaxing University China Elmore Family School of Electrical and Computer Engineering Purdue University Indianapolis USA State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

Trajectory planning method is a research hotspot in autonomous driving. Existing reinforcement learning-based trajectory planning methods suffer from unstable performance due to the strong randomness of network weight parameter updates during the training process. Therefore, this paper proposes a novel trajectory planning method based on deep reinforcement learning trust region policy optimization (TRPO). Firstly, in order to enhance the robustness of the trajectory planning method based on deep reinforcement learning TRPO, a TRPO-LSTM based decision model was proposed. More specifically, a long short term memory (LSTM) based state feature extraction network was designed and embeded into a TRPO-based decision model to enhance the ability of TRPO to extract information from the environmental state space. Secondly, in order to make the planned trajectory adaptive to the dynamic changes of traffic environment, we presented a novel TRPO-LSTM trajectory fitting algorithm. To the best of our knowledge, this is the first work aiming at applying the TRPO-LSTM based decision model in the trajectory fitting process to search the optimal longitudinal trajectory speed. Finally, the proposed trajectory planning method was implemented and simulated on the CARLA simulator. The experimental results show that, compared with existing trajectory planning methods based on deep reinforcement learning algorithms, our proposed method achieves a cumulative reward improvement of over 28.9% in the scenario of four lane highway, and has better robustness. Meanwhile, the proposed method can achieve a lower collision rate of 0.93% while improving the average speed and comfort of vehicle driving. IEEE

关键词： Heuristic algorithms

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Explainable Application Intent for Zero-Touch Networking: An Incorporation of Hypergraph and Transformer

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IEEE Transactions on Communications 2025年

作者： Wu, Bing Zou, Sai Liwang, Minghui Ni, Wei Wang, Xianbin Guizhou University College of Big Data and Information Engineering China National Key Laboratory of Autonomous Intelligent Unmanned Systems Department of Control Science and Engineering China Tongji University Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai China Data61 CSIRO Australia Department of Electrical and Computer Engineering Western University Canada

The autonomous interpretation of application intent (APPI) represents the primary step towards achieving closed-loop autonomy in zero-touch networking (ZTN) and also a prerequisite for intent-based networking (IBN). However, understanding APPIs and invoking the corresponding network resources require network professionals with extensive technical expertise to customize network service requests (NSRs), which presents significant challenges for the large-scale deployment of ZTN. This paper investigates an interesting problem of autonomous interpretation of APPIs for ZTN, where a novel mechanism integrating hypergraph and transformer with completeness assurance (HyperTrans-CA) is proposed. In particular, we first involve the Bayesian theory to model APPIs interpretability as maximizing the correct transition probability, where hypergraph is used to describe the complex relationship between application characteristics (e.g., scenario function, and performance) and NSRs, including network devices, virtual network functions (VNFs), and resources. Then, the hypergraph is integrated into the encoder, decoder, and attention mechanisms of Transformer, and a completeness assurance mechanism is designed to improve the prediction accuracy. The convergence of HyperTrans-CA and the corresponding convergence speed of the hypergraph-boosted Transformer in the graph search process are also analyzed. Comprehensive simulations and empirical measurements regarding industrial internet demonstrate that HyperTrans-CA can effectively explain/understand APPIs. Compared to the state-of-the-art Transformer and ChatGPT3.5 models, HyperTrans-CA improves the prediction accuracy of APPIs mapped to VNFs by 23% and 46%, respectively, while raising the prediction accuracy of VNF locations by 8.6 and 17.3 times. © 2025 IEEE.

关键词： Bayesian networks

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Condition Monitoring with Incomplete Data: An Integrated Variational Autoencoder and Distance Metric Framework

Condition Monitoring with Incomplete Data: An Integrated Var...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Maryam Ahang Mostafa Abbasi Todd Charter Homayoun Najjaran Department of Electrical and Computer Engineering University of Victoria Victoria Canada Advanced Control and Intelligent Systems Laboratory (ACIS) University of Victoria Victoria Canada Department of Mechanical Engineering University of Victoria Victoria Canada

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Condition monitoring of industrial systems is crucial for ensuring safety and maintenance planning, yet notable challenges arise in real-world settings due to the limited or non-existent availability of fault samples. This paper introduces an innovative solution to this problem by proposing a new method for fault detection and condition monitoring for unseen data. Adopting an approach inspired by zero-shot learning, our method can identify faults and assign a relative health index to various operational conditions. Typically, we have plenty of data on normal operations, some data on compromised conditions, and very few (if any) samples of severe faults. We use a variational autoencoder to capture the probabilistic distribution of previously seen and new unseen conditions. The health status is determined by comparing each sample’s deviation from a normal operation reference distribution in the latent space. Faults are detected by establishing a threshold for the health indexes, allowing the model to identify severe, unseen faults with high accuracy, even amidst noise. We validate our approach using the run-to-failure IMS-bearing dataset and compare it with other methods. The health indexes generated by our model closely match the established descriptive model of bearing wear, attesting to the robustness and reliability of our method. These findings highlight the potential of our methodology in augmenting fault detection capabilities within industrial domains, thereby contributing to heightened safety protocols and optimized maintenance practices.

关键词： Condition monitoring Fault diagnosis Protocols Fault detection Zero shot learning Probabilistic logic Robustness Safety Maintenance Indexes

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Trajectory Tracking of Tractor-Trailer Wheeled Mobile Robots via Dynamic Feedback Linearization in Forward and Backward Motion 10

Trajectory Tracking of Tractor-Trailer Wheeled Mobile Robots...

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10th International Conference on control, Instrumentation and Automation, ICCIA 2024

作者： Olyai, Mohammad Alipour, Khalil Tarvirdizadeh, Bahram Sorouri, Majid Ghamari, Mohammad Laboratory Department of Mechatronics Eng School of Intelligent Systems Engineering College of Interdisciplinary Science and Technology Tehran Iran Maynooth University Maynooth Department of Electronic Engineering Co. Kildare Ireland Islamic Azad University Science and Research Branch Department of Computer and Mechatronics Engineering Tehran Iran California Polytechnic State University Electrical Engineering Department San Luis ObispoCA93407 United States

ISBN: (纸本)9798331509972

One type of wheeled mobile robot widely used in public transportation and for carrying high payloads is the tractor-Trailer wheeled robots (TTWRs). This study considers a differentially-driven tractor under pure rolling conditions, which is subject to nonholonomic constraints. controlling a TTWR in both backward and forward motion is challenging due to inherent instability. To address this issue and achieve trajectory tracking control for these systems, this paper employs dynamic feedback linearization (DFL) to overcome the limitations of static feedback linearization (SFL). The system's response will be examined under various trajectories and initial conditions for both forward and backward motion. © 2024 IEEE.

关键词： Tractors (truck)

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A Swing-Up and Stable control Strategy for Pendubot Based on Nonlinear Model Predictive Method

A Swing-Up and Stable Control Strategy for Pendubot Based on...

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2023 China Automation Congress, CAC 2023

作者： Wang, Lejun Liu, Yiqin Zhang, Ruihuan Huang, Zixin Cai, Zhen School of Automation School of Industrial Internet Chongqing University of Posts and Telecommunications Chongqing400065 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 Hubei Key Laboratory of Digital Textile Equipment Wuhan Textile University Wuhan430200 China School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan430205 China School of Information Engineering Wuhan City Polytechnic Wuhan430064 China

ISBN: (纸本)9798350303759

The control objective of Pendubot is to stabilise the system in a vertically upward position by swinging up from a vertically downward position. Facing various physical constraints in practical applications, this paper proposes a swing-up stabil-isation control strategy based on a nonlinear model prediction method to accomplish the above control objective. On the basis of the system dynamics model, the forward Euler method is used to construct a nonlinear prediction model of the system. According to the control objectives and physical constraints of the system, the performance index function is designed, and then the control inputs of each sampling cycle are obtained through rolling optimisation and feedback correction, so that the system state gradually converges to the target trajectory, thus achieving the motion control objective. Based on the simulation results, it can be seen that the strategy proposed in this paper is efficient and robust under external disturbances. © 2023 IEEE.

关键词： Model predictive control

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Accelerating Distributed Nash Equilibrium Seeking

arXiv

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arXiv 2023年

作者： Tatarenko, Tatiana Nedić, Angelia The Department of Control Theory and Intelligent Systems TU Darmstadt Germany School of Electrical Computer and Energy Engineering Arizona State University United States

This work proposes a novel distributed approach for computing a Nash equilibrium in convex games with restricted strongly monotone pseudo-gradients. By leveraging the idea of the centralized operator extrapolation method presented in [4] to solve variational inequalities, we develop the algorithm converging to Nash equilibria in games, where players have no access to the full information but are able to communicate with neighbors over some communication graph. The convergence rate is demonstrated to be geometric and improves the rates obtained by the previously presented procedures seeking Nash equilibria in the class of games under consideration. © 2023, CC BY.

关键词： Game theory

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Anomaly Detection for Stochastic Networked Cyber-Physical systems: A Statistical Approach

Anomaly Detection for Stochastic Networked Cyber-Physical Sy...

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International Conference on control and Automation (ICCA)

作者： Yamin Yan Minyue Fu Maria M. Seron School of Electrical and Electronic Engineering Nanyang Technological University Singapore Shenzhen Key Laboratory of Control Theory and Intelligent Systems and Centre for Control Science and Technology School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China School of Engineering The University of Newcastle Callaghan NSW Australia

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

In this paper, we investigate the anomaly detection problem within stochastic networked cyber-physical systems. We introduce an anomaly detection scheme centered on estimator design employing a statistical approach. Specifically, we extend the statistical method recently developed for single systems to networked control scenarios. Additionally, we analyse the detection of several representative anomalies through this statistical approach. We then apply our anomaly detection strategy to a power network system exposed to such anomalies. Our findings demonstrate that the proposed design effectively detects these anomalies, which simpler methods relying solely on residual assessment with a threshold fail to identify.

关键词： Analytical models Automation Statistical analysis Stochastic processes Cyber-physical systems control systems Anomaly detection

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Experimental and numerical study on the shrinkage-deformation of carrot slices during hot air drying

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International Journal of Agricultural and Biological engineering 2023年第1期16卷 260-272页

作者： Dalong Jiang Congcong Li Zifan Lin Yuntian Wu Hongjuan Pei Magdalena Zielinska Hongwei Xiao School of Computer and Control Engineering Yantai UniversityYantai 264005ShandongChina Hebei Agricultural University Baoding 071001HebeiChina Hebei Key Laboratory of Agricultural Big Data Baoding 071001HebeiChina Department of Electrical and Electronic Engineering University of Western AustraliaPerth 6000Australia BeiGene Guangzhou Biologics Manufacturing Co. LtdGuangzhou 510555China Neuroscience and Intelligent Media Institute Communication University of ChinaBeijing 100024China Department of Systems Engineering University of Warmia and MazuryOlsztyn 004889Poland College of Engineering China Agricultural UniversityBeijing 100083China

In order to further understand the mechanism of material volume change in the drying process,numerical simulations(considering or neglecting shrinkage)of heat and mass transfer during convective drying of carrot slices under constant and controlled temperature and relative humidity were carried *** results were validated with experimental *** results of the simulation show that the Quadratic model fitted well to the moisture ratio and the material temperature data trend with average relative errors of 5.9%and 8.1%,***,the results of the simulation considering shrinkage show that the moisture and temperature distributions during drying are closer to the experimental data than the results of the simulation disregarding *** material moisture content was significantly related to the shrinkage of dried *** and relative humidity significantly affected the volume shrinkage of carrot *** volume shrinkage increased with the rising of the constant temperature and the decline of relative *** model can be used to provide more information on the dynamics of heat and mass transfer during drying and can also be adapted to other products and dryers devices.

关键词： carrot drying numerical simulation heat and mass transfer shrinkage

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