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A fuzzy compensation-Koopman model predictive control design for pressure regulation in proten exchange membrane electrolyzer

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Chinese Journal of Chemical Engineering 2024年第12期76卷 251-263页

作者： Haokun Xiong Lei Xie Cheng Hu Hongye Su State Key Laboratory of Industrial Control Technology Zhejiang UniversityHangzhou 310027China

Proton exchange membrane(PEM)electrolyzer have attracted increasing attention from the industrial and researchers in recent years due to its excellent hydrogen production *** accurate models to predict their performance is crucial for promoting and accelerating the design and optimization of electrolysis *** work developed a Koopman model predictive control(MPC)method incorporating fuzzy compensation for regulating the anode and cathode pressures in a PEM electrolyzer.A PEM electrolyzer is then built to study pressure control and provide experimental data for the identification of the Koopman linear *** identified linear predictors are used to design the Koopman *** addition,the developed fuzzy compensator can effectively solve the Koopman MPC model mismatch *** effectiveness of the proposed method is verified through the hydrogen production process in PEM simulation.

关键词： Hydrogen production PEM electrolyzer Nonlinear control Model predictive control Koopman operator Fuzzy logic system

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From Static and Dynamic Perspectives:A Survey on Historical Data Benchmarks of control Performance Monitoring

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 300-316页

作者： Pengyu Song Jie Wang Chunhui Zhao Biao Huang the State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University IEEE the Department of Chemical and Materials Engineering University of Alberta

In recent decades, control performance monitoring(CPM) has experienced remarkable progress in research and industrial applications. While CPM research has been investigated using various benchmarks, the historical data benchmark(HIS) has garnered the most attention due to its practicality and effectiveness. However, existing CPM reviews usually focus on the theoretical benchmark, and there is a lack of an in-depth review that thoroughly explores HIS-based methods. In this article, a comprehensive overview of HIS-based CPM is provided. First, we provide a novel static-dynamic perspective on data-level manifestations of control performance underlying typical controller capacities including regulation and servo: static and dynamic properties. The static property portrays time-independent variability in system output, and the dynamic property describes temporal behavior driven by closed-loop feedback. Accordingly,existing HIS-based CPM approaches and their intrinsic motivations are classified and analyzed from these two ***, two mainstream solutions for CPM methods are summarized, including static analysis and dynamic analysis,which match data-driven techniques with actual controlling behavior. Furthermore, this paper also points out various opportunities and challenges faced in CPM for modern industry and provides promising directions in the context of artificial intelligence for inspiring future research.

关键词： control performance monitoring (CPM) data-driven method historical data benchmark (HIS) industrial process performance index static and dynamic analysis

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Toward Universal controller: Performance-Aware Self-Optimizing Reinforcement Learning for Discrete-Time Systems With Uncontrollable Factors

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3249-3260页

作者： Zhang, Jianfeng Zhang, Haoran Zhao, Chunhui Zhejiang University State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou310027 China

The industrial system usually contains not only controllable variables (CVs) but also uncontrollable variables (unCVs), e.g., weather conditions and friction. These unCVs have a direct impact on system control performance. Despite the success of current deep reinforcement learning (DRL) control algorithms, most of them neglect the impact of unCVs, which can cause the deterioration of control performance and instability of the system. To perceive and eliminate the impact of unCVs, a performance-aware self-optimizing universal controller (PASOUC) is designed in this article. The PASOUC aims at integrating the representation of unCVs and controller design to perceive and eliminate the impact of unCVs under different conditions, which goes beyond most existing control methods. Technically, a historical trajectory-inspired control performance perceptron is developed to perceive the impact of unCVs on system control performance under different conditions. Subsequently, a new performance-aware reward is designed to integrate the representation of unCVs and controller design while training the DRL controller. In addition, the domain randomization (DR) training strategy is employed to learn a universal control policy, which can access the approximate optimal trajectory under nonideal conditions. In this way, the impact of unCVs can be eliminated. To handle the low efficiency of the DR training, the policy improvement-policy proximal optimization (PI-PPO) is proposed to enhance the convergence speed of the DR training by performing explicit policy improvement. Finally, illustrative examples are presented to demonstrate the superiority of the proposed method. © 2013 IEEE.

关键词： Deep reinforcement learning

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A blast furnace fault monitoring algorithm with low false alarm rate:Ensemble of greedy dynamic principal component analysis-Gaussian mixture model

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Chinese Journal of Chemical Engineering 2023年第5期57卷 151-161页

作者： Xiongzhuo Zhu Dali Gao Chong Yang Chunjie Yang State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou 310027China

The large blast furnace is essential equipment in the process of iron and steel manufacturing. Due to the complex operation process and frequent fluctuations of variables, conventional monitoring methods often bring false alarms. To address the above problem, an ensemble of greedy dynamic principal component analysis-Gaussian mixture model(EGDPCA-GMM) is proposed in this paper. First, PCA-GMM is introduced to deal with the collinearity and the non-Gaussian distribution of blast furnace ***, in order to explain the dynamics of data, the greedy algorithm is used to determine the extended variables and their corresponding time lags, so as to avoid introducing unnecessary noise. Then the bagging ensemble is adopted to cooperate with greedy extension to eliminate the randomness brought by the greedy algorithm and further reduce the false alarm rate(FAR) of monitoring results. Finally, the algorithm is applied to the blast furnace of a large iron and steel group in South China to verify *** with the basic algorithms, the proposed method achieves lowest FAR, while keeping missed alarm rate(MAR) remain stable.

关键词： Chemical processes Principal component analysis Gaussian mixture model Process monitoring Ensemble Process control

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An Accurate and Interpretable Framework for Trustworthy Process Monitoring

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第5期5卷 2241-2252页

作者： Wang, Hao Wang, Zhiyu Niu, Yunlong Liu, Zhaoran Li, Haozhe Liao, Yilin Huang, Yuxin Liu, Xinggao Zhejiang University State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou310027 China

Trustworthy process monitoring seeks to build an accurate and interpretable monitoring framework, which is critical for ensuring the safety of energy conversion plant (ECP) that operates under extreme working conditions, such as high pressure and temperature. Contemporary self-attentive models, however, fall short in this domain for two main reasons. First, they rely on stepwise correlations that fail to involve physically meaningful semantics in ECP logs, resulting in suboptimal accuracy and interpretability. Second, attention matrices are frequently cluttered with spurious correlations that obscure physically meaningful ones, further impeding effective interpretation. To overcome these issues, we propose AttentionMixer, a framework aimed at improving both accuracy and interpretability of the existing methods, and establish a trustworthy ECP monitoring framework. Specifically, to tackle the first issue, we employ a spatial adaptive message passing block to capture variatewise correlations. This block is coupled with a temporal adaptive message passing block through a mixing operator, yielding a multifaceted representation of ECP logs accounting for both the stepwise and variatewise correlations. Concurrently, to tackle the second issue, we employ a sparse message passing regularizer to filter out spurious correlations. We validate the efficacy of AttentionMixer using two real-world datasets from the radiation monitoring network for Chinese nuclear power plants. © 2020 IEEE.

关键词： Accident prevention

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Pyramid Dynamic Bayesian Networks for key Performance Indicator Prediction in Long Time-Delay industrial Processes

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第2期5卷 661-671页

作者： Zeng, Lingquan Ge, Zhiqiang Zhejiang University State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou310027 China

Building dynamic Bayesian networks (DBNs) for time-delay industrial processes has always been tough, since the structure learning of the DBN is a NP-hard problem. In this article, a pyramid DBN (PDBN) framework is proposed to speed up modeling and improve feature learning for industrial processes with large time delays. In the PDBN framework, a sequence of small-sized DBNs are established, each of which is a progressively simpler representation of the previous layer, and the feature information learned by the DBN sequence corresponds to the layers in the pyramid. With information fusion of the feature pyramid and a further feature filtering with hill climbing based on Bayesian information criterion, we can restore the feature information of the time-delay industrial processes. Regression models are built based on the features learned by the PDBN framework for further key performance indicator estimation. Advantages of the method have been effectively validated on two actual industrial cases. With multilevel feature learning and filtering, both modeling speed and prediction accuracy have been greatly improved. The proposed method outperforms other similar works on identifying important features in industrial processes and existing DBN-based soft sensors. © 2023 IEEE.

关键词： Hidden Markov models

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Augmented industrial Data-Driven Modeling Under the Curse of Dimensionality

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IEEE/CAA Journal of Automatica Sinica 2023年第6期10卷 1445-1461页

作者： Xiaoyu Jiang Xiangyin Kong Zhiqiang Ge the State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou 310027China the Peng Cheng Laboratory Shenzhen 518000China

The curse of dimensionality refers to the problem o increased sparsity and computational complexity when dealing with high-dimensional *** recent years,the types and vari ables of industrial data have increased significantly,making data driven models more challenging to *** address this prob lem,data augmentation technology has been introduced as an effective tool to solve the sparsity problem of high-dimensiona industrial *** paper systematically explores and discusses the necessity,feasibility,and effectiveness of augmented indus trial data-driven modeling in the context of the curse of dimen sionality and virtual big ***,the process of data augmen tation modeling is analyzed,and the concept of data boosting augmentation is *** data boosting augmentation involves designing the reliability weight and actual-virtual weigh functions,and developing a double weighted partial least squares model to optimize the three stages of data generation,data fusion and *** approach significantly improves the inter pretability,effectiveness,and practicality of data augmentation in the industrial ***,the proposed method is verified using practical examples of fault diagnosis systems and virtua measurement systems in the *** results demonstrate the effectiveness of the proposed approach in improving the accu racy and robustness of data-driven models,making them more suitable for real-world industrial applications.

关键词： Index Terms—Curse of dimensionality data augmentation data-driven modeling industrial processes machine learning

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Reduced-Order Observer-Based Resilient control for MASs with Time-Varying Delay Against DoS Attacks

IEEE Transactions on Industrial Cyber-Physical Systems

引用

IEEE Transactions on industrial Cyber-Physical Systems 2024年 2卷 51-59页

作者： Qiu, Xiaojie Meng, Wenchao Xu, Jinming Yang, Qinmin Zhejiang University State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou310027 China

This paper concentrates on the resilient control issue in multi-agent systems (MASs) with the time-varying delay subjected to denial of service (DoS) attacks. To eliminate the impact of DoS attacks, a novel resilient control framework is constructed for MASs, where a variable following the Bernoulli distribution process is utilized to denote the behaviors of the attacker. Besides, we design a novel reduced-order (RO) observer with internal time-varying delay to estimate the unknown output of the system and compensate for lost data in the channels. Different from the general observers, the designed observer can ensure both the reduced-order observation errors and the full-order ones can converge to zero. Furthermore, an RO observer-based distributed protocol is devised such that the consensus errors of the closed-loop systems asymptotically converge to zero. Simulation comparisons are provided to validate the efficiency and superiority of our approach. © 2023 IEEE.

关键词： Multi agent systems

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Spherical robot: A novel robot for exploration in harsh unknown environments

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IET Cyber-Systems and Robotics 2023年第4期5卷 1-15页

作者： Wei Ren You Wang Haoxiang Liu Song Jin Yixu Wang Yifan Liu Ziang Zhang Tao Hu Guang Li State Key Laboratory of Industrial Control Technology Institute of Cyber Systems and ControlZhejiang UniversityHangzhouChina

The authors propose a complete software and hardware framework for a novel spherical robot to cope with exploration in harsh and unknown *** proposed robot is driven by a heavy pendulum covered by a fully enclosed spherical shell,which is strongly protected,amphibious,anti-overturn and has a *** for location and perception,planning and motion control are comprehensively *** the one hand,the authors fully consider the kinematic model of a spherical robot,propose a positioning algorithm that fuses data from inertial measurement units,motor encoder and Global Navigation Satellite System,improve global path planning algorithm based on Hybrid A*and design an instruction planning controller based on model predictive control(MPC).On the other hand,the dynamic model is built,linear MPC and robust servo linear quadratic regulator algorithm is improved,and a speed controller and a direction controller are *** addition,based on the pose and motion charac-teristics of a spherical robot,a visual obstacle perception algorithm and an electronic image stabilisation algorithm are ***,the authors build physical systems to verify the effectiveness of the above algorithms through experiments.

关键词： mobile robots robot control robot perception robot planning

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Unmanned aerial vehicle orthogonal laser localization by Gaussian mixture model‐based map representation

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IET Cyber-Systems and Robotics 2023年第3期5卷 37-44页

作者： Zeyu Wan Changjian Jiang Yu Zhang State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhouChina

Localization is a core problem in mobile robot *** localization and mapping(SLAM)costs much for an unmanned aerial vehicle(UAV).This research aims to design an orthogonal laser scan device for localization and to save computation *** on disturbance analysis,residual influences on sensor state are quantitative,and they are related to uncertainty and *** research applied the residual selection method to a *** feature point detection utilises multi‐scale and Gaussian model fitting techniques to guarantee true *** map is represented by Gaussian Mixture Models(GMM)with lower memory *** orthogonal laser scan device is composed and placed on a UAV for real‐time three‐dimensional localization,whose er-rors are at the centimeter level.

关键词： indoor positioning mobile robots unmanned aerial vehicle

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