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Data Generation Feedback Relearning control for Unmodeled Nonlinear systems

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2024年第1期8卷 389-400页

作者： Zhang, Yong Mu, Chaoxu Zhao, Dongbin Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China

Reinforcement learning (RL) algorithms require continuous interaction with the controlled plant to optimize the objective function and control policy. However, the security risk and poor real-time performance limit the application of RL algorithms. In this article, a data generation feedback relearning (DGFR) control algorithm is developed to avoid these limitations. The DGFR control algorithm improves the control performance by interacting with a proposed delayed neural network-based data generation model. Considering the time-delay characteristics of industrial production, the data generation model is different from the identifier of the system model, but it is used to generate the target data required by the DGFR control algorithm. The interaction between the algorithm and the data generation model avoids the risk of trial-and-error and improves real-time performance. Finally, the superiority of the proposed control algorithm is verified by giving comparative experiments in conjunction with a power grid system and a permanent magnet synchronous motor system.

关键词： Data models Real-time systems Heuristic algorithms Mathematical models Adaptation models Approximation algorithms Cost function Data generation model feedback relearning control delayed neural network reinforcement learning unmodeled nonlinear system

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Evaluating the moderated-mediation effects of switchingcosts in the link between social capital and NPD performance

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Journal of management Science and Engineering 2024年第2期9卷 161-176页

作者： Yi Li Nelson Oly Ndubisi Gang Li Jinpeng Xu School of Economics&Management Xidian UniversityXi'anChina College of Business&Economics Qatar UniversityDohaQatar School of Management Xi'an Jiao Tong UniversityXi'anChina The State Key Lab for Manufacturing Systems Engineering Xi'anChina The Key Laboratory of the Ministry of Education for Process Control and Efficiency Projects Xi'anChina

In this study,we explore the causes and performance outcomes of switching costs in the context of new product development(NPD)from both the supplier and customer perspectives,and discuss the role that switching costs play as moderators and mediators in the relationship between social capital and NPD *** on data from 214 Chinese manufacturers,we employ the structural equation model to test our conceptual model and *** results indicate that relationship quality and customer involvement positively and negatively affect switching costs,respectively,and that switching costs negatively affect NPD *** costs mediate and moderate the relationship between social capital and NPD ***,switching costs significantly and positively moderate the relationship between relationship quality and NPD *** the other hand,switching costs insignificantly and negatively moderate the relationship between customer involvement and NPD *** theoretical and managerial implications of the findings are discussed.

关键词： Social capital New product development(NPD) Performance Switching costs Moderated mediation analysis China

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The engineering of circular causality for specialization and design of complex systems:cad2CAS and casCAD2

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Frontiers of Information Technology & Electronic Engineering 2024年第2期25卷 323-332页

作者： Xiaolong LIANG Rui QIN Juanjuan LI Fei-Yue WANG Faculty of Innovation Engineering Macao University of Science and TechnologyMacao999078China State Key Laboratory of Multimodal Artificial Intelligence Systems Chinese Academy of SciencesBeijing100190China The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing100190China

The emergence of decentralized autonomous organizations and operations(DAOs),equipped with innovative mechanisms,enables new possibilities for transforming traditional social collaborative *** are the underlying drive... 详细信息

关键词： complexity. circular equipped

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Detection of Stealthy False Data Injection Attacks Against Cyber-Physical systems:A Stochastic Coding Scheme

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Journal of systems Science & complexity 2022年第5期35卷 1668-1684页

作者： GUO Haibin PANG Zhonghua SUN Jian LI Jun State Key Lab of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China Beijing Institute of Technology Chongging Innovation Center Chongqing 401120China Key Laboratory of Fieldbus Technology and Automation of Beijing North China University of TechnologyBeijing 100144China China Industrial Control Systems Cyber Emergency Response Team Beijing 100040China

This paper,from the view of a defender,addresses the security problem of cyber-physical systems(CPSs)subject to stealthy false data injection(FDI)attacks that cannot be detected by a residual-based anomaly detector without other defensive *** detect such a class of FDI attacks,a stochastic coding scheme,which codes the sensor measurement with a Gaussian stochastic signal at the sensor side,is proposed to assist an anomaly detector to expose the FDI *** order to ensure the system performance in the normal operational context,a decoder is adopted to decode the coded sensor measurement when received at the controller *** this detection scheme,the residual under the attack can be significantly different from that in the normal situation,and thus trigger an *** design condition of the coding signal covariance is derived to meet the constraints of false alarm rate and attack detection *** minimize the trace of the coding signal covariance,the design problem of the coding signal is converted into a constraint non-convex optimization problem,and an estimation-optimization iteration algorithm is presented to obtain a numerical solution of the coding signal covariance.A numerical example is given to verify the effectiveness of the proposed scheme.

关键词： Attack detection cyber-physical systems(CPSs) stealthy FDI attacks stochastic coding

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Multi-View Feature Selection for PolSAR Image Classification via l2,1 Sparsity Regularization and Manifold Regularization

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2021年 30卷 8607-8618页

作者： Huang, Xiayuan Nie, Xiangli Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Feature is a crucial element of polarimetric synthetic aperture radar (PolSAR) image classification. Multiple types of Features, such as polarimetric features (PF) generated from the PolSAR data and various polarimetric target decompositions, texture features (TF) of the Pauli color-coded PolSAR images are used as features for PolSAR image classification. The obtained PF and TF often form the high-dimensional data, which leads to high computational complexity. Moreover, some features are irrelative and do nothing to improve the classification performance. Therefore, it is fairly indispensable to select a subset of useful features for PolSAR image classification. This paper proposes a multi-view feature selection method for PolSAR image classification. Firstly, two types of features, PF and TF are generated separately. Then the optimization model is built to pursue the feature selection matrices. Specifically, in order to maintain the consistency of different types of features, we search for the common representation of multiple types of features in the optimization problem. The l(2,1) norm sparsity regularization is imposed on the feature selection matrices to achieve feature selection. In addition, the manifold regularization on the common representation is utilized to preserve the structure information of the data. The effectiveness of the proposed method is evaluated on three real PolSAR data sets. Experimental results demonstrate the superiority of the proposed method.

关键词： Multi-view feature selection PolSAR image classification l(2,1) norm manifold regularization

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Parallel control for Nonzero-Sum Games With Completely Unknown Nonlinear Dynamics via Reinforcement Learning

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2025年第4期55卷 2884-2896页

作者： Lu, Jingwei Wei, Qinglai Wang, Fei-Yue Tsinghua Univ Dept Ind Engn Beijing 100084 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Macau Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Fac Innovat Engn Macau Peoples R China

This article utilizes parallel control to investigate the problem of continuous-time (CT) nonzero-sum games (NZSGs) for completely unknown nonlinear systems via reinforcement learning (RL), and a parallel control-based NZSG (PNZSG) method is developed without reconstructing unknown dynamics or employing off-policy integral RL (IRL). First, novel dynamic control policies (DCPs) are developed for NZSGs by introducing controls into feedback, and an augmented system with augmented performance indices is constructed to derive the DCPs. Then, we theoretically analyze the effect of the DCPs on the control stability and performance indices, and the optimality of PNZSG is proven to be equivalent to the optimality of the original NZSGs. Subsequently, an IRL technique is employed to achieve the developed PNZSG method, and we show that no prior knowledge of the dynamics of NZSGs is needed to deploy the developed PNZSG method because of the augmented system and performance indices. Finally, numerical examples, including cooperative adaptive cruise control (CACC) of a vehicular platoon, demonstrate the correctness of the developed PNZSG method. The associated code is available at: https://***/lujingweihh/Adaptive-dynamic-programming-algorithms/tree/main/model_free_nonzero_sum_games.

关键词： Adaptive dynamic programming cooperative adaptive cruise control (CACC) cooperative adaptive cruise control (CACC) nonzero-sum games (NZSGs) nonzero-sum games (NZSGs) parallel control parallel control reinforcement learning (RL) reinforcement learning (RL) unknown nonlinear dynamics unknown nonlinear dynamics unknown nonlinear dynamics unknown nonlinear dynamics unknown nonlinear dynamics

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MSFANet: A Light Weight Object Detector Based on Context Aggregation and Attention Mechanism for Autonomous Mining Truck

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第3期8卷 2285-2295页

作者： Song, Ruiqi Ai, Yunfeng Tian, Bin Chen, Long Zhu, Fenghua Yao, Fei Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Waytous Inc Qingdao 266109 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China North Automatic Control Technol Inst Taiyuan 030006 Peoples R China

Accurate and reliable object detection is a fundamental component of perception system for autonomous driving. Specially, in some circumstances like autonomous driving in surface mine, there is a fact that the particularity of scene brings tremendous challenges for object detection with a series of problems caused by the multi-scale and camouflaged objects. In this paper, a multi-scale feature fusion and attention based multi-branches framework was proposed to improve the performance of object detection for above problems called MSFANet. In the proposed MSFANet, a multi-scale feature fusion module, which was used to capture the rich context features for multi-scale high level feature maps, and a multi-scale attention module, which was used to enhance the feature saliency of objects with different scales, were designed. What's more, to improve the performance of multi-scale object detection, we build 4 different prediction branches for large, medium small and smaller scale objects respectively. At last, we built our own dataset for automatic driving in surface mine called SurMine and test the model at our own datasets and KITTI benchmark. It achieved 82.7 mAP(%) and 92.57 mAP(%) in 32 36 ms on a TITAN RTX, compared to 80.2 mAP(%) and 87.83 mAP(%) in 28 similar to 34 ms by YOLOv7 on SurMine and KITTI benchmarks.

关键词： Feature extraction Object detection Autonomous vehicles Task analysis Detectors Semantics Real-time systems feature fusion object detection surface mine

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Hierarchical Neighbors Embedding

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systems 2024年第6期35卷 7816-7829页

作者： Liu, Shenglan Yu, Yang Liu, Kaiyuan Wang, Feilong Wen, Wujun Qiao, Hong Dalian Univ Technol Fac Elect Informat & Elect Engn Sch Comp Sci & Technol Dalian 116024 Peoples R China Dalian Univ Technol Sch Innovat & Entrepreneurship Dalian 116024 Peoples R China Chinese Acad Sci Inst Automation State Key Lab Management & Control Complex Syst Beijing Peoples R China

Manifold learning now plays an important role in machine learning and many relevant applications. In spite of the superior performance of manifold learning techniques in dealing with nonlinear data distribution, their performance would drop when facing the problem of data sparsity. It is hard to obtain satisfactory embeddings when sparsely sampled high-dimensional data are mapped into the observation space. To address this issue, in this article, we propose hierarchical neighbors embedding (HNE), which enhances the local connections through hierarchical combination of neighbors. And three different HNE-based implementations are derived by further analyzing the topological connection and reconstruction performance. The experimental results on both the synthetic and real-world datasets illustrate that our HNE-based methods could obtain more faithful embeddings with better topological and geometrical properties. From the view of embedding quality, HNE develops the outstanding advantages in dealing with data of general distributions. Furthermore, comparing with other state-of-the-art manifold learning methods, HNE shows its superiority in dealing with sparsely sampled data and weak-connected manifolds.

关键词： Data sparsity hierarchical neighbors manifold learning topological and geometrical properties. the introduced to

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Deep Reinforcement Learning or Lyapunov Analysis?A Preliminary Comparative Study on Event-Triggered Optimal control

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1702-1704页

作者： Jingwei Lu Lefei Li Qinglai Wei Fei-Yue Wang IEEE the Department of Industrial Engineering Tsinghua UniversityBeijing 100084China The State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Dear Editor,This letter develops a novel method to implement event-triggered optimal control(ETOC) for discrete-time nonlinear systems using parallel control and deep reinforcement learning(DRL), referred to as Deep-ETOC. The developed Deep-ETOC method introduces the communication cost into the performance index through parallel control, so that the developed method enables control systems to learn ETOC policies directly without triggering conditions.

关键词： Deep letter enable

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Spatial Repetitive Impedance Learning control for Robot-Assisted Rehabilitation

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2023年第3期28卷 1280-1290页

作者： Yang, Jiantao Sun, Tairen Cheng, Long Hou, Zeng-Guang Univ Shanghai Sci & Technol Sch Hlth Sci & Engn Shanghai 200093 Peoples R China Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Techno Beijing 100190 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Joint Lab Intelligence Sci & Technol Macau 999078 Peoples R China

In robot-assisted rehabilitation and leg exoskeletons, humans and robots are required to collaboratively complete repetitive tasks with fixed periodic paths. In such applications, impedance learning control can provide variable impedance regulation for improving the performance of physical interactions;however, designing such control is highly challenging owing to the difficulty in modeling human time-varying dynamics. By exploiting the spatial periodicity characteristics of the desired trajectory and human impedance, we propose a novel spatial repetitive impedance learning control strategy to enhance interaction performance. First, a defined spatial operator serves as the mathematics foundation for constructing the robot dynamics in the spatial domain. Then, a spatial impedance learning controller is designed. In this article, time-varying impedance profiles are estimated using spatial full-saturation iterative learning laws, while robotic parameter uncertainties are estimated using the differential adaptation law with projection modification. We validate the uniform convergence of the tracking error through a Lyapunov-like analysis and demonstrate the control effectiveness using an illustrative example. Compared with related results on temporal repetitive learning control, the proposed control approach can enable human-robot system to complete a repetitive task with unspecified speeds according to the users' strengths and motion capacity.

关键词： Adaptive control impedance learning iterative learning control rehabilitation robot repetitive learning control robot control spatial periodicity

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