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H ∞ Asynchronous Control for Discrete-Time Semi-Markov Jump Systems

IFAC-PapersOnLine 2022年第3期55卷 143-148页

作者： Liqiang Jin Yanyan Yin Yanqing Liu Kok Lay Teo Fei Liu National Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Institute of Automation Jiangnan University Wuxi 214122 China School of Electrical Engineering Computing and Mathematical Sciences Curtin University Perth Western Australia 6102 Australia School of Mathematical Sciences Sunway University Kuala Lumpur 47500 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.

关键词： Asynchronous control semi-Markov jump systems hidden semi-Markov model semi-Markov kernel (SMK) σ-error mean square stable

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Geo-NI: Geometry-aware Neural Interpolation for Light Field Rendering

arXiv

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arXiv 2022年

作者： Wu, Gaochang Zhou, Yuemei Liu, Yebin Fang, Lu Chai, Tianyou The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China The Institute of Industrial Artificial Intelligence Northeastern University Shenyang110819 China Department of Automation Tsinghua University Beijing100084 China The Department of Electronic Engineering Tsinghua University Beijing100084 China The Beijing National Research Center for Information Science and Technology Beijing100084 China

In this paper, we present a Geometry-aware Neural Interpolation (Geo-NI) framework for light field rendering. Previous learning-based approaches either rely on the capability of neural networks to perform direct interpolation, which we dubbed Neural Interpolation (NI), or explore scene geometry for novel view synthesis, also known as Depth Image-Based Rendering (DIBR). Instead, we incorporate the ideas behind these two kinds of approaches by launching the NI with a novel DIBR pipeline. Specifically, the proposed Geo-NI first performs NI using input light field sheared by a set of depth hypotheses. Then the DIBR is implemented by assigning the sheared light fields with a novel reconstruction cost volume according to the reconstruction quality under different depth hypotheses. The reconstruction cost is interpreted as a blending weight to render the final output light field by blending the reconstructed light fields along the dimension of depth hypothesis. By combining the superiorities of NI and DIBR, the proposed Geo-NI is able to render views with large disparity with the help of scene geometry while also reconstruct non-Lambertian effect when depth is prone to be ambiguous. Extensive experiments on various datasets demonstrate the superior performance of the proposed geometry-aware light field rendering framework. © 2022, CC BY-NC-ND.

关键词： Geometry

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Multivariate Time Delay Estimation Based on Dynamic Characteristic Analytics

Multivariate Time Delay Estimation Based on Dynamic Characte...

引用

第三十九届中国控制会议

作者： Xu Chen Chunhui Zhao State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University State Key Laboratory of Process Automation in Mining & Metallurgy Beijing Key Laboratory of Process Automation in Mining &Metallurgy

Time delay widely exists among industrial process variables, which may lead to invalid description of systems and reduce the model accuracy for soft sensor, process monitoring etc. Therefore, it is crucial to estimate time delay for improving model accuracy. In traditional research, time delay estimation（TDE） algorithm for two variables is fully investigated, but little research pays attention to multivariate. The method of pairwise comparison may not only cause high calculation complexity but also cut off the correlation between multivariate. In this work, a new TDE algorithm is proposed to estimate time delay between multivariate, which extracts dynamic latent variable to represent the process using improved dynamic inner PCA algorithm（DiPCA）. Dynamic latent variable extracts the autocorrelation and cross-correlation between process variables. Take it as a standard and analyze the time delay, so that the influence of multivariate variables could be comprehensively considered. Since there may be non-linear variables, the significant factor（SF） is used for variable selection, and the multivariate variables are divided into multiple linear subgroups, which makes the analysis result more reasonable. The effectiveness is illustrated with two numerical examples and the Tennessee Eastman process.

关键词： Time delay estimation process dynamics dynamic latent variable variable selection multivariate

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SOS-based secure consensus control for stochastic nonlinear MASs against multiple DoS attacks

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Applied Mathematics and Computation 2025年 507卷

作者： Zhang, Zhen Che, Wei-Wei School of Automation Shandong Key Laboratory of Industrial Control Technology Qingdao University Shandong Qingdao 266071 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Liaoning Shenyang 110819 China College of Information Science and Engineering Northeastern University Liaoning Shenyang 110819 China

This paper investigates the fully distributed fuzzy secure consensus control problem for stochastic nonlinear multi-agent systems (MASs). A polynomial fuzzy model is applied to reconstruct the system dynamics for a class of nonlinear MASs described by the Itoˆ differential equation. The asynchronous denial-of-service (DoS) attack is considered that occurs independently across communication channels between the agents. Then, a novel fuzzy secure control protocol is developed to achieve the consensus task and resist the effect of the DoS attack. Meanwhile, the control protocol is fully distributed without relying on the global information of the Laplace matrix, and its adaptive parameter is membership-function-dependent, which is more flexible. Then, a non-parallel distributed compensation method is used to deal with the mismatch problem of the membership function between the fuzzy control protocol and the fuzzy system. In addition, several mean-square bounded convergence criteria in the form of the sum-of-squares for the secure tracking error are obtained by constructing the piecewise Lyapunov functions. Finally, the feasibility of the theoretical results is verified by the simulation example and the comparative analysis. © 2025 Elsevier Inc.

关键词： Denial-of-service attack Fully distributed fuzzy secure control Polynomial fuzzy model Stochastic nonlinear multi-agent system Sum-of-squares

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Submodularity-based sensor scheduling for state estimation among multiple linear dynamical systems

Submodularity-based sensor scheduling for state estimation a...

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Jiangsu Annual Conference on automation (JACA 2022)

作者： Y. Ni C. Yang X. Liu X. Luan F. Liu The Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) School of Internet of Things Engineering Jiangnan University Wuxi People's Republic of China The Key Laboratory of Smart Manufacturing in Energy Chemical Process (Ministry of Education) Department of Automation East China University of Science and Technology Shanghai People's Republic of China Department of Radiology Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital/Harvard Medical School Boston USA

In this paper we focus on the sensor scheduling for dynamic state estimation among multiple processes. Multiple sensors transmit their measurements to the remote estimator via a shared wireless channel. Only a subset of sensors are allowed to transmit at each time. The objective of the scheduler is to maximize a linear combination of the terminal time estimation error covariances in a log-determinant sense. We develop conditions under which the objective is monotone and submodular. A submodularity-based greedy scheduling algorithm is proposed, and its performance bound is provided. Numerical examples are presented to illustrate the theoretical findings.

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Corrosive Pseudomonas Aeruginosa Detection by Measuring Pyocyanin with a Lab-on-Fiber Optical Surface Plasmon Resonance Biosensor in Aquatic Environments

SSRN

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SSRN 2023年

作者： Zheng, Wanlu Ju, Chunxue Liu, Pan Li, Zhong Liu, Jidong Zhang, Yanan Zhao, Yong Gu, Tingyue Wang, Fuhui Xu, Dake Fan, Yongqiang College of Information Science and Engineering Northeastern University Shenyang110819 China Shenyang National Laboratory for Materials Science Northeastern University Shenyang110819 China College of Life and Health Sciences Northeastern University Shenyang110819 China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao066004 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Department of Chemical and Biomolecular Engineering Institute for Corrosion and Multiphase Technology Ohio University AthensOH45701 United States Northeastern University United States

Oceanic facilities and equipment corrosion present considerable economic and safety concerns, predominantly due to microbial corrosion. Early detection of corrosive microbes is pivotal for effective monitoring and prevention. Yet, traditional detection methods often lack specificity, require extensive processing time, and yield inaccurate results. Hence, the need for an efficient real-time corrosive microbe monitoring technology is evident. Pseudomonas aeruginosa, a widely distributed microorganism in aquatic environments, utilizes its production of quinone-like compounds, specifically pyocyanin (PYO), to corrode metals. Here, we report a novel fiber optic surface plasmon resonance (SPR) sensor modified by the C-terminal of BrlR protein (BrlR-C), which is a specific receptor of PYO molecule, to detect P. aeruginosa in aquatic environments. The results showed that the sensor had a good ability to recognize PYO in the concentration range of 0-1 μg/mL, and showed excellent sensing performance in real-time monitoring the growth status of P. aeruginosa. With a strong selectivity of PYO, the sensor could clearly detect P. aeruginosa against other bacteria in seawater environment, and exhibited excellent anti-interference ability against variations in pH, temperature and pressure and other interfering substances. This study provides a useful tool for monitoring corrosive P. aeruginosa biofilm in aquatic environments, which is a first of its kind example that serves as a laboratory model for the application of fiber optic technology in real-world scenarios to monitoring biofilms in microbial corrosion and biofouling. © 2023, The Authors. All rights reserved.

关键词： Optical fibers

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Communication Compression for Distributed Nonconvex Optimization

arXiv

引用

arXiv 2022年

作者： Yi, Xinlei Zhang, Shengjun Yang, Tao Chai, Tianyou Johansson, Karl H. Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Digital Futures Stockholm100 44 Sweden Department of Electrical Engineering University of North Texas DentonTX76203 United States State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China

This paper considers distributed nonconvex optimization with the cost functions being distributed over agents. Noting that information compression is a key tool to reduce the heavy communication load for distributed algorithms as agents iteratively communicate with neighbors, we propose three distributed primal–dual algorithms with compressed communication. The first two algorithms are applicable to a general class of compressors with bounded relative compression error and the third algorithm is suitable for two general classes of compressors with bounded absolute compression error. We show that the proposed distributed algorithms with compressed communication have comparable convergence properties as state-of-the-art algorithms with exact communication. Specifically, we show that they can find first-order stationary points with sublinear convergence rate O(1/T) when each local cost function is smooth, where T is the total number of iterations, and find global optima with linear convergence rate under an additional condition that the global cost function satisfies the Polyak–Lojasiewicz condition. Numerical simulations are provided to illustrate the effectiveness of the theoretical results. Copyright © 2022, The Authors. All rights reserved.

关键词： Iterative methods

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

arXiv

引用

arXiv 2022年

作者： Xu, Lei Yi, Xinlei Sun, Jiayue Shi, Yang Johansson, Karl H. Yang, Tao State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology and Digital Futures Stockholm10044 Sweden Department of Mechanical Engineering University of Victoria VictoriaBCV8W 2Y2 Canada

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 them 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 and that larger quantization level leads to faster convergence speed. 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. Copyright © 2022, The Authors. All rights reserved.

关键词： Optimization algorithms

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Corrosive Pseudomonas Aeruginosa Detection by Measuring Pyocyanin with a Lab-on-Fiber Optical Surface Plasmon Resonance Biosensor in Aquatic Environments

SSRN

引用

SSRN 2023年

作者： Zheng, Wanlu Ju, Chunxue Liu, Pan Li, Zhong Liu, Jidong Zhang, Yanan Zhao, Yong Gu, Tingyue Wang, Fuhui Xu, Dake Fan, Yongqiang China Northeastern University China College of Information Science and Engineering Northeastern University Shenyang110819 China Shenyang National Laboratory for Materials Science Northeastern University Shenyang110819 China College of Life and Health Sciences Northeastern University Shenyang110819 China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao066004 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Department of Chemical and Biomolecular Engineering Institute for Corrosion and Multiphase Technology Ohio University AthensOH45701 United States

关键词： Optical fibers

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Adaptive identifier–critic–actor neural optimal control of stochastic nonlinear systems with elastic state constraints

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Chaos, Solitons & Fractals 2025年 199卷

作者： Penghao Chen Hamid Reza Karimi Xiaoli Luan Fei Liu Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Institute of Automation Jiangnan University Wuxi 214122 China Department of Mechanical Engineering Politecnico di Milano Milan 20156 Italy

This article discusses the adaptive identifier–critic–actor neural optimal control for stochastic nonstrict-feedback nonlinear systems with elastic state constraints. Reinforcement learning is used to achieve optimal control, which is designed based on the identifier–critic–actor structure of neural network approximation. In this framework, the identifier, critic and actor are used to estimate unknown dynamics, evaluate system performance and execute control actions, respectively. This control scheme designs the actual control from all virtual controls and dynamic surface controls as the optimal solution to the corresponding subsystems. The update law is derived through the negative gradient of a simple positive function, which is generated by the partial derivative of the Hamilton–Jacobi-Bellman (HJB) equation. At the same time, this design can also alleviate the requirement for continuous excitation conditions in current optimal control methods. A key innovation lies in formulating an elastic constraint function with flexible capabilities, thus providing a unified framework capable of flexibly addressing custom time constraints without changing the control structure. Stability analysis shows that all signals are semi-globally uniformly ultimately bounded in probability.

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