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16th IFAC Symposium on Large Scale Complex Systems: Theory and Applications LSS 2022

作者： Jin, Liqiang Yin, Yanyan Liu, Yanqing Teo, Kok Lay Liu, Fei National Key Laboratory of Advanced Process Control for Light Industry Ministry of Education Institute of Automation Jiangnan University Wuxi214122 China School of Electrical Engineering Computing and Mathematical Sciences Curtin University PerthWA6102 Australia School of Mathematical Sciences Sunway University Kuala Lumpur47500 Malaysia Tianjin University of Finance and Economics Tianjin China

This paper investigates the problem of H∞asynchronous control for discrete-time semi-Markov jump systems. In practice, it is difficult to ensure that controller modes and system modes are updated synchronously. Therefore, a hidden semi-Markov model is introduced to characterize such an asynchronous phenomenon between system modes and controller modes. Meanwhile, the switching signal of the controller is generated by a given conditional probability related to the system mode. Based on the mode-dependent and elapsed-time-dependent Lyapunov function, together with the semi-Markov kernel (SMK) method, sufficient conditions constructed by linear matrix inequalities (LMIs) are derived to ensure the existence of an asynchronous controller such that the resulting system is σ-error mean square stable and satisfies an H∞performance index. Finally, a numerical example is given to illustrate the effectiveness and feasibility of the method proposed. © 2022 Elsevier B.V.. All rights reserved.

关键词： controllers

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Learning-Based Switched Reliable control of Cyber-Physical Systems With Intermittent Communication Faults

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IEEE/CAA Journal of Automatica Sinica 2020年第3期7卷 711-724页

作者： Xin Huang Jiuxiang Dong College of Information Science and Engineering Northeastern UniversityShenyang 110819China State Key Laboratory of Synthetical Automation of Process Industries Northeastern UniversityShenyang 110819China Key Laboratory of Vibration and Control of Aero-Propulsion System Ministry of Education Northeastern UniversityShenyang 110819China IEEE

This study deals with reliable control problems in data-driven cyber-physical systems(CPSs) with intermittent communication faults, where the faults may be caused by bad or broken communication devices and/or cyber attackers. To solve them, a watermark-based anomaly detector is proposed, where the faults are divided to be either detectable or ***, the fault's intermittent characteristic is described by the average dwell-time(ADT)-like concept, and then the reliable control issues, under the undetectable faults to the detector, are converted into stabilization issues of switched systems. Furthermore,based on the identifier-critic-structure learning algorithm, a datadriven switched controller with a prescribed-performance-based switching law is proposed, and by the ADT approach, a tolerated fault set is given. Additionally, it is shown that the presented switching laws can improve the system performance degradation in asynchronous intervals, where the degradation is caused by the fault-maker-triggered switching rule, which is unknown for CPS operators. Finally, an illustrative example validates the proposed method.

关键词： Adaptive dynamic programming(ADP) communication fault cyber-physical systems(CPSs) data-driven control reliable control

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Queue estimation for isolated signalized intersections in intelligent vehicle-infrastructure cooperation systems

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Science China(Information Sciences) 2021年第4期64卷 226-228页

作者： Yunpeng WANG Ge GUO Wei YUE School of Control Science and Engineering Dalian University of Technology State Key Laboratory of Synthetical Automation for Process Industries Northeastern University School of Control Engineering Northeastern University at Qinhuangdao School of Marine Electrical Engineering Dalian Maritime University

Dear editor,Traffic jams have become an important issue in urban networks, particularly for arterial roads [1]. The need for more efficient traffic control techniques has become critical. One good solution is the so-called intelligent vehicleinfrastructure cooperation systems (i-VICS), which assumes autonomous vehicles in the traffic flow (i.e., a mixed traffic flow). The aim of i-VICS is to achieve cooperative vehicle intersection control, i.e., to enable cooperation between vehicles and traffic signals, for safe and efficient intersection operations.

关键词： Information Systems and Communication Service

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Repetitive T-S Fuzzy Model-Based Iterative Learning control Law Design

Repetitive T-S Fuzzy Model-Based Iterative Learning Control ...

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IEEE Conference on Decision and control

作者： Robert Maniarski Wojciech Paszke Hongfeng Tao Eric Rogers Institute of Automation Electronic and Electrical Engineering University of Zielona Góra ul. Szafrana 2 Zielona Góra Poland Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Jiangnan University Wuxi China School of Electronics and Computer Science University of Southampton Southampton UK

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper develops an iterative learning control law for a class of nonlinear systems. The approach used to represent the nonlinear system dynamics is a Takagi-Sugeno fuzzy repetitive process that considers the two directions of information propagation. Then, the control action investigated is a state feedback control law combined with a PD-type feed-forward learning control law. Consequently, linear matrix inequality techniques can be used for control design. Furthermore, this approach allows the design of control action to satisfy the requirements on both the error convergence and the transient dynamics. Finally, an example demonstrates the properties of the new design.

关键词： State feedback control design Takagi-Sugeno model Nonlinear dynamical systems Linear matrix inequalities Transient analysis Iterative learning control Convergence

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A New Robust Adaptive Neural Network Backstepping control for Single Machine Infinite Power System With TCSC

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IEEE/CAA Journal of Automatica Sinica 2020年第1期7卷 48-56页

作者： Yanhong Luo Shengnan Zhao Dongsheng Yang Huaguang Zhang A new robust adaptive neural network backstepping control for single machine infinite power system with TCSC Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110004 School of Information Science and Engineering Northeastern UniversityShenyang 110004China

For a single machine infinite power system with thyristor controlled series compensation(TCSC) device, which is affected by system model uncertainties, nonlinear time-delays and external unknown disturbances, we present a robust adaptive backstepping control scheme based on the radial basis function neural network(RBFNN). The RBFNN is introduced to approximate the complex nonlinear function involving uncertainties and external unknown disturbances, and meanwhile a new robust term is constructed to further estimate the system residual error,which removes the requirement of knowing the upper bound of the disturbances and uncertainty terms. The stability analysis of the power system is presented based on the Lyapunov function,which can guarantee the uniform ultimate boundedness(UUB) of all parameters and states of the whole closed-loop system. A comparison is made between the RBFNN-based robust adaptive control and the general backstepping control in the simulation part to verify the effectiveness of the proposed control scheme.

关键词： Backstepping control radial basis function neural network(RBFNN) robust adaptive control thyristor controlled series compensation(TCSC) uniform ultimate boundedness(UUB)

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Sparse Gaussian Feature Learning for Nonlinear Industrial Fault Diagnosis

Sparse Gaussian Feature Learning for Nonlinear Industrial Fa...

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第34届中国控制与决策会议

作者： Jianfeng Zhang Chunhui Zhao Jinliang Ding Youxian Sun Xianling Li State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang University State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Science and Technology on Thermal Energy and Power Laboratory Wuhan 2nd Ship Design and Research Institute

ISBN: (纸本)9781665478977

With the increasing complexity of modern industrial processes,the high-dimensional industrial data exhibits strong nonlinearity,bringing considerable challenges to the fault diagnosis of *** efficiently extract deep meaningful features that are crucial for fault diagnosis,a sparse Gaussian feature extractor(SGFE) is designed to learn a nonlinear mapping that projects the raw data into the feature space with the fault label *** feature space is described by the one-hot encoding of the fault category label as an orthogonal *** this way,the sparse Gaussian features related to fault categories can be gradually learned from the raw data by *** the feature space,the sparse Gaussian(SG) loss function is designed to constrain the distribution of features to multiple sparse multivariate Gaussian *** sparse Gaussian features are linearly separable in the feature space,which can improve the accuracy of the downstream fault classification *** effectiveness of the proposed SGFE is verified by the Tennessee-Eastman(TE) benchmark process.

关键词： Nonlinear fault diagnosis sparse Gaussian feature learning Gaussian feature extractor

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Data-Driven Distributionally Robust Mixed-Integer control through Lifted control Policy

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IEEE Transactions on Automatic control 2025年

作者： Ma, Xutao Ning, Chao Du, Wenli Shi, Yang Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education Shanghai200237 China University of Victoria Department of Mechanical Engineering VictoriaBCV8W 3P6 Canada

This paper investigates the finite-horizon distributionally robust mixed-integer control (DRMIC) of uncertain linear systems. However, deriving an optimal causal feedback control policy to this DRMIC problem is computationally formidable for most ambiguity sets. To address the computational challenge, we propose a novel distributionally robust lifted control policy (DR-LCP) method to derive a high-quality approximate solution to this DRMIC problem for a rich class of Wasserstein metric-based ambiguity sets, including the Wasserstein ambiguity set and its variants. In theory, we analyze the asymptotic performance and establish a tight non-asymptotic bound of the proposed method. In numerical experiments, the proposed DR-LCP method empirically demonstrates superior performance compared with existing methods in the literature. ©1963-2012 IEEE.

关键词： Mixed-integer linear programming

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Input-output finite-time sliding mode fault-tolerant control for Markovian jump systems with incomplete transition rates

Input-output finite-time sliding mode fault-tolerant control...

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第34届中国控制与决策会议

作者： Meng Zhao Haijuan Zhao Zhiru Cao Yugang Niu The Key Laboratory of Smart Manufacturing in Energy Chemical Process (East China University of Science and Technology) Ministry of Education School of Information and Control Engineering Qingdao University of Technology School of Mechatronic Engineering and Automation Shanghai University

This paper deals with the input-output finite-time sliding mode control(SMC) problem of a class of uncertain Markovian jump systems with incomplete transition *** is assumed that the actuator failure may occur.A modedependent sliding surface is *** synthesizing a fault-tolerant SMC scheme,the specified sliding surface can be reached in finite ***,the input-output finite-time stability over both the reaching and sliding motion stages is ensured simultaneously by establishing some sufficient ***,the developed fault-tolerant SMC approach is verified by a numerical example.

关键词： Markovian jump systems input-output finite-time stability sliding mode control faults-tolerant control

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An improved self-organizing maps neural network model for plantar pressure image classification 9

An improved self-organizing maps neural network model for pl...

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9th IEEE Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2020

作者： Li, Zairan Wang, Dan Tianjin University Tianjin Key Laboratory of Process Measurement and Control School of Electrical Engineering and Automation China Wenzhou Polytechnic Wenzhou China

A Self-organizing map neural network combining with classification output layers network was proposed for classifying plantar pressure dataset, which was acquired from foot 3D scan system after features extraction. The single SOM with 10, 15, 20, 30 hidden layers was compared and validated. Subsequently, the classification modeling with SOM combining multiple hidden neural networks was proposed, and results showed that the proposed model performs high effectiveness in plantar pressure dataset and has potential applications on comfort design through last customization. © 2020 IEEE.

关键词： Self organizing maps

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Quantized Distributed Nonconvex Optimization Algorithms with Linear Convergence under the Polyak–Lojasiewicz Condition

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IEEE Transactions on Automatic control 2025年

作者： Xu, Lei Yi, Xinlei Sun, Jiayue Shi, Yang Johansson, Karl H. Yang, Tao Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China University of Victoria Department of Mechanical Engineering VictoriaBCV8W 2Y2 Canada Tongji University Shanghai Institute of Intelligent Science and Technology Shanghai201804 China KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm100 44 Sweden

This paper considers distributed optimization for minimizing the average of local nonconvex cost functions, by using local information exchange over undirected communication networks. To reduce the required communication capacity, we introduce an encoder–decoder scheme. By integrating it with distributed gradient tracking and proportional integral algorithms, respectively, we then propose two quantized distributed nonconvex optimization algorithms. Assuming the global cost function satisfies the Polyak–Lojasiewicz condition, which does not require the global cost function to be convex and the global minimizer is not necessarily unique, we show that our proposed algorithms linearly converge to a global optimal point. Moreover, we show that a low data rate is sufficient to guarantee linear convergence when the algorithm parameters are properly chosen. The theoretical results are illustrated by numerical examples. © 1963-2012 IEEE.

关键词： Cost functions

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