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第36届中国控制会议

作者： Jing Xu Yugang Niu the Key Laboratory of Advanced Control and Optimization for Chemical Process （East China University of Science and Technology） Ministry of Education

ISBN: (纸本)9781538629185

This paper presents a robust control scheme for the tracking control of singularly perturbed uncertain systems. The design problem is divided into two parts: tracking the reference trajectory and enhancing the robustness of the dynamic *** using the time-scale techniques, dynamic inversion approach and linear parameter-varying methods, a set of singularly perturbed state feedback controllers are employed to enhance the tracking performance and robustness of a dynamic system against parameter uncertainties, actuator and sensor unmodelled dynamics, and external disturbances. The longitudinal control of an F16 aircraft model is included to show the merits and and effectiveness of the proposed design scheme.

关键词： Self-scheduled Tracking control Singularly perturbed systems Robust control

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Convergence Analysis for an Identifier-Based Adaptive Dynamic Programming Algorithm 34

Convergence Analysis for an Identifier-Based Adaptive Dynami...

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第三十四届中国控制会议

作者： SONG Jun NIU Yugang ZOU Yuanyuan Key Laboratory of Advanced Control and Optimization for Chemical Process (East China University of Science and Technology) Ministry of Education

ISBN: (纸本)9781467374439

This paper investigates the uniformly ultimate boundedness(UUB) of an identifier-based adaptive dynamic programming(ADP) algorithm proposed in [7]. It is demonstrated that the estimation errors of weights in both critic and action networks are UUB during iteration learning. Moreover, a selection method on learning rates is also given.

关键词： Adaptive Dynamic Programming(ADP),Markov Jump Systems(MJSs),Uniformly Ultimately Boundedness(UUB)

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On-Line Batch process Monitoring Using Multiway Kernel Partial Least Squares

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Journal of Donghua University(English Edition) 2011年第6期28卷 585-590页

作者：胡益马贺贺侍洪波 Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of EducationEast China University of Science and Technology

An approach for batch processes monitoring and fault detection based on multiway kernel partial least squares(MKPLS) was *** is known that conventional batch process monitoring methods,such as multiway partial least squares(MPLS),are not suitable due to their intrinsic linearity when the variations are *** address this issue,kernel partial least squares(KPLS) was used to capture the nonlinear relationship between the latent structures and predictive *** addition,KPLS requires only linear algebra and does not involve any nonlinear *** this paper,the application of KPLS was extended to on-line monitoring of batch *** proposed batch monitoring method was applied to a simulation benchmark of fed-batch penicillin fermentation *** the results demonstrate the superior monitoring performance of MKPLS in comparison to MPLS monitoring.

关键词： process monitoring fault detection kernel partial least squares(KPLS) nonlinear process multiway kernel partial least squares(MKPLS)

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Exponential dissipative control of stochastic systems under wireless communication networks 35

Exponential dissipative control of stochastic systems under ...

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第35届中国控制会议

作者： SONG Jun NIU Yugang ZOU Yuanyuan Key Laboratory of Advanced Control and Optimization for Chemical Process(East China University of Science and Technology) Ministry of Education

ISBN: (纸本)9781509009107

In this work,the problem of dissipative control is investigated for stochastic systems under the unreliable wireless network,in which there may exist stochastic fading ***,a modified Rice fading model with disturbance-dependent noise is *** then,a state-feedback controller with fading measurements is designed and the some sufficient conditions are derived such that the closed-loop system is finite-time stochastic bounded with a prescribed exponentially dissipative ***,numerical simulation results are provided.

关键词： Stochastic systems fading channel dissipative control

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Locust Behaved Particle Swarm optimization Technique

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Journal of Donghua University(English Edition) 2014年第2期31卷 190-196页

作者：钟伟民谢雪勤梁毅罗娜张娟钱锋 Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of EducationEast China University of Science and Technology

The collective behavior of certain animals and insects has the characteristic of self-organization. The simple interactions among individuals can produce complex adaptive patterns at the level of the group. Recently,new scientific investigation pointed out that desert locusts show extreme phenotypic plasticity in transforming between the lonely phase and the swarming gregarious phase depending on the population density,which is controlled by a serotonin called 5-hydroxytryptamine( 5HT). In this paper,based on the mechanism of the locusts' collective behavior,a new particle swarm optimization technique called LBPSO is studied. The number of swarms is selfadaptively adjusted by the acquired outstanding particles coming from behind the previous global best solution. The swarm sizes are related to the corresponding serotonin 5HT,which is determined by the optimization parameters such as global best and iteration number. And each swarm adopts one of three rules below according to its density, generalized social evolution strategy, generalized cognition evolution strategy and the independent moving strategy. A comparative study of LBPSO,social particle swarm optimization( SPSO), improved SPSO and the standard particle swarm optimization( StdPSO) on their abilities of tracking optima is carried out. And the results under four static benchmark functions and a dynamic function generator moving peaks benchmark( MPB)show that LBPSO outperforms the other three functions in both static and dynamic landscapes due to the introduced locusts' collective behavior.

关键词： evolutionary algorithm particle swarm optimization(PSO) locust collective behavior

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Coupling pinch analysis and rigorous process simulation for hydrogen networks with light hydrocarbon recovery

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Chinese Journal of chemical Engineering 2021年第12期34卷 141-148页

作者： Minbo Yang Xiao Feng Liang Zhao Shaanxi Key Laboratory of Energy Chemical Process Intensification School of Chemical Engineering and TechnologyXi’an Jiaotong UniversityXi'an 710049China Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and TechnologyMinistry of EducationShanghai 200237China

In refineries,some hydrogen-rich streams contain considerable light hydrocarbons that are important raw materials for the chemical *** hydrogen networks with light hydrocarbon recovery can enhance the reuse of both hydrogen and light *** work proposes an automated method for targeting hydrogen networks with light hydrocarbon recovery.A pinch-based algebraic method is improved to determine the minimum fresh hydrogen consumption and hydrogen sources fed into the light hydrocarbon recovery unit *** process simulation is conducted to determine the mass and energy balances of the light hydrocarbon recovery *** targeting procedures are developed through combination of the improved pinch method and rigorous process *** hybrid method is realized by coupling the Matlab and Aspen HYSYS platforms.A refinery hydrogen network is analyzed to illustrate application of the proposed *** integration of hydrogen network with light hydrocarbon recovery further reduces fresh hydrogen requirement by463.0 m^(3)·h^(-1) and recovers liquefied petroleum gas and gasoline of 1711.5 kg·h^(-1) and 643 kg·h^(-1),respectively.A payback period of 9.2 months indicates that investment in light hydrocarbon recovery is economically attractive.

关键词： Hydrogen Light hydrocarbon recovery Pinch technology Simulation Systems engineering

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Novel Ensemble Modeling Method for Enhancing Subset Diversity Using Clustering Indicator Vector Based on Stacked Autoencoder

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Computer Modeling in Engineering & Sciences 2019年第10期121卷 123-144页

作者： Yanzhen Wang Xuefeng Yan Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and TechnologyShanghai200237China

A single model cannot satisfy the high-precision prediction requirements given the high nonlinearity between *** contrast,ensemble models can effectively solve this *** key factors for improving the accuracy of ensemble models are namely the high accuracy of a submodel,the diversity between subsample sets and the optimal ensemble *** study presents an improved ensemble modeling method to improve the prediction precision and generalization capability of the *** proposed method first uses a bagging algorithm to generate multiple subsample ***,an indicator vector is defined to describe these subsample ***,subsample sets are selected on the basis of the results of agglomerative nesting clustering on indicator vectors to maximize the diversity between ***,these subsample sets are placed in a stacked autoencoder for ***,XGBoost algorithm,rather than the traditional simple average ensemble method,is imported to ensemble the model during *** machine learning public datasets and atmospheric column dry point dataset from a practical industrial process show that our proposed method demonstrates high precision and improved prediction ability.

关键词： Ensemble model deep learning bagging stacked autoencoder XGBoost

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OSF-JS: Orthonormal Slow Feature-Based Quality-Related Data Decomposition Network With Jensen–Shannon Divergence for chemical processes Incipient Fault Detection

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Wang, Jiayu Yao, Le Xiong, Weili Cui, Xiaohui Yu, Wei Young, Brent Ministry of Education Key Laboratory of Advanced Process Control for Light Industry Wuxi214122 China Jiangnan University School of Internet of Things Engineering Wuxi214122 China Hangzhou Normal University School of Mathematics Hangzhou311121 China Jiangnan University Science Center for Future Foods Wuxi214122 China Wuhan University School of Cyber Science and Engineering Hubei Wuhan430072 China University of Auckland Department of Chemical and Materials Engineering Auckland1010 New Zealand

In chemical processes, there are both nonlinearity and slow dynamics among process variables. Meanwhile, at the beginning of faults, slight variation may not prompt the significant parametric changes. Traditional fault detection methods, based on mean and variation shifts, often fail to achieve satisfactory performance. To improve the detection of incipient quality-related faults, a novel orthonormal slow feature-based quality-related data decomposition network with Jensen-Shannon divergence (OSF-JS) fault detection method is proposed. The orthonormal subspace analysis (OSA) is embedded with Siamese to extract orthonormal slow features (OSFs) from decomposed subspaces. Enhancing the detection of incipient faults, JS divergence combined with a sliding window algorithm is introduced to construct statistics. The effectiveness of OSF-JS is validated for a wastewater treatment process and a real-world chemical process. Results show that the proposed OSF-JS method not only improves the fault detection rate (FDR) and reduces false alarm rate (FAR) but also identifies whether faults occur in the quality-related component. © 1963-2012 IEEE.

关键词： Fault detection Feature extraction chemical processes Monitoring Kernel Principal component analysis Windows Wastewater treatment Industries Decoding

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Fixed-Time Anti-Disturbance Sliding Mode control Based on a Novel Variable Time Headway Policy for Vehicle Platoon

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IET Intelligent Transport Systems 2025年第1期19卷

作者： Xin Yang Cheng-Lin Liu Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Institute of Automation Jiangnan University Wuxi China

This paper primarily studies the control problem of disturbed vehicle platoon using fixed-time control methods. With the proposed variable time headway policy, vehicles can maintain the desired distance to the preceding vehicle during constant speed cruising in advance based on their acceleration. Additionally, a fixed-time disturbance observer is introduced to estimate the disturbances. The introduction of adaptive law and the hyperbolic tangent function, which replaces the sign function, aims to mitigate the chattering issue of the controller. Subsequently, a fixed-time anti-disturbance control strategy is proposed. To address the transient issues caused by non-zero initial spacing errors, another improved approach is suggested. The effectiveness and superiority of the proposed solutions are verified through numerical simulations.

关键词： distributed control Vehicle dynamics and control

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A Fusion Kalman Filter and UFIR Estimator Using the Influence Function Method

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 709-718页

作者： Wei Xue Xiaoli Luan Shunyi Zhao Fei Liu IEEE the Key Laboratory of Advanced Process Control for Light Industry(Ministry of Education) Institute of AutomationJiangnan University

In this paper,the Kalman filter(KF)and the unbiased finite impulse response(UFIR)filter are fused in the discrete-time state-space to improve robustness against *** avoid the problem where fusion filters may give up some advantages of UFIR filters by fusing based on noise statistics,we attempt to find a way to fuse without using noise *** fusion filtering algorithm is derived using the influence function that provides a quantified measure for disturbances on the resulting filtering outputs and is termed as an influence finite impulse response(IFIR)*** main advantage of the proposed method is that the noise statistics of process noise and measurement noise are no longer required in the fusion process,showing that a critical feature of the UFIR filter is *** numerical example and a practice-oriented case are given to illustrate the effectiveness of the proposed *** is shown that the IFIR filter has adaptive performance and can automatically switch from the Kalman estimate to the UFIR estimates according to operating ***,the proposed method can reduce the effects of optimal horizon length on the UFIR estimate and can give the state estimates of best accuracy among all the compared methods.

关键词： Fusion filter influence function Kalman filter(KF) robustness unbiased finite impulse response(FIR)

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