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Distributed formation control for a multi-agent system with dynamic and static obstacle avoidances

Chinese Physics B 2014年第7期23卷 337-342页

作者：曹建福凌志浩袁宜峰高冲 Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology

Formation control and obstacle avoidance for multi-agent systems have attracted more and more attention. In this paper, the problems of formation control and obstacle avoidance are investigated by means of a consensus algorithm. A novel distributed control model is proposed for the multi-agent system to form the anticipated formation as well as achieve obstacle avoidance. Based on the consensus algorithm, a distributed control function consisting of three terms （formation control term, velocity matching term, and obstacle avoidance term） is presented. By establishing a novel formation control matrix, a formation control term is constructed such that the agents can converge to consensus and reach the anticipated formation. A new obstacle avoidance function is developed by using the modified potential field approach to make sure that obstacle avoidance can be achieved whether the obstacle is in a dynamic state or a stationary state. A velocity matching term is also put forward to guarantee that the velocities of all agents converge to the same value. Furthermore, stability of the control model is proven. Simulation results are provided to demonstrate the effectiveness of the proposed control.

关键词： multi-agent system formation control obstacle avoidance consensus theory

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Fault diagnosis and process monitoring using a statistical pattern framework based on a self-organizing map

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Journal of Central South University 2015年第2期22卷 601-609页

作者：宋羽姜庆超颜学峰 Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education(East China University of Science and Technology)

A multivariate method for fault diagnosis and process monitoring is proposed. This technique is based on a statistical pattern(SP) framework integrated with a self-organizing map(SOM). An SP-based SOM is used as a classifier to distinguish various states on the output map, which can visually monitor abnormal states. A case study of the Tennessee Eastman(TE) process is presented to demonstrate the fault diagnosis and process monitoring performance of the proposed method. Results show that the SP-based SOM method is a visual tool for real-time monitoring and fault diagnosis that can be used in complex chemical *** with other SOM-based methods, the proposed method can more efficiently monitor and diagnose faults.

关键词： statistic pattern framework self-organizing map fault diagnosis process monitoring

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Robustness of gross primary production estimation from long-term reconstructed solar-induced chlorophyll fluorescence varies with greenness on a global scale

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Journal of Applied Remote Sensing 2025年第1期19卷

作者： Fu, Lijiang Xia, Qian Tang, Hao Chen, Junqing Tan, Jinglu Guo, Ya Jiangnan University Ministry of Education Key Laboratory of Advanced Process Control for Light Industry Wuxi China Changshu Institute of Technology School of Electrical Engineering and Automation Changshu China University of Missouri Department of Chemical and Biomedical Engineering ColumbiaMO United States

Long-term reconstructed solar-induced chlorophyll fluorescence (SIF) derived from raw gridded SIF has been used for the estimation of gross primary production (GPP), but the robustness of the spatial relationship may vary from location to location. We examined the often-used linear relationship between GPP and SIF in terms of R2 values for varied locations globally using three GPP datasets (FLUXCOM, VPM, PML) and three long-term reconstructed monthly SIF datasets (CSIF, SIF005, and RTSIF). The results show that the R2 value is a concave function of vegetation greenness level (NDVI) on an annual or seasonal basis. The average R2 is over 0.8 in areas where the annual average NDVI is in the range of 0.4 to 0.6, whereas the R2 is much lower where the annual average NDVI is less than 0.2 or greater than 0.8. Prediction of GPP or SIF by three methods from five major environmental variables revealed greater uncertainties in GPP and/or SIF at low or high greenness levels as an apparent cause of the low R2. The results offer useful insights into how global GPP may be effectively estimated from multi-satellite measured SIF. © 2025 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： Optical correlation

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Orthogonal nonnegative matrix factorization based local hidden Markov model for multimode process monitoring

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Chinese Journal of chemical Engineering 2016年第7期24卷 856-860页

作者： Fan Wang Honglin Zhu Shuai Tan Hongbo Shi Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology

Traditional data driven fault detection methods assume that the process operates in a single mode so that they cannot perform well in processes with multiple operating modes. To monitor multimode processes effectively,this paper proposes a novel process monitoring scheme based on orthogonal nonnegative matrix factorization(ONMF) and hidden Markov model(HMM). The new clustering technique ONMF is employed to separate data from different process modes. The multiple HMMs for various operating modes lead to higher modeling *** proposed approach does not presume the distribution of data in each mode because the process uncertainty and dynamics can be well interpreted through the hidden Markov estimation. The HMM-based monitoring indication named negative log likelihood probability is utilized for fault detection. In order to assess the proposed monitoring strategy, a numerical example and the Tennessee Eastman process are used. The results demonstrate that this method provides efficient fault detection performance.

关键词： Multimode processFault detectionHidden Markov modelOrthogonal nonnegative matrix factorization

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New techniques and strategies in drug discovery(2020–2024 update)

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Chinese chemical Letters 2025年第3期36卷 104-112页

作者： Qijie Gong Jian Song Yihui Song Kai Tang Panpan Yang Xiao Wang Min Zhao Liang Ouyang Li Rao Bin Yu Peng Zhan Saiyang Zhang Xiaojin Zhang Jiangsu Key Laboratory of Drug Design and Optimization and Department of ChemistryChina Pharmaceutical UniversityNanjing 211198China School of Basic Medical Sciences Zhengzhou UniversityZhengzhou 450001China School of Pharmaceutical Sciences&Key Laboratory of Advanced Drug Preparation Technologies Zhengzhou UniversityZhengzhou 450001China Department of Medicinal Chemistry Key Laboratory of Chemical Biology(Ministry of Education)School of Pharmaceutical SciencesShandong UniversityJi’nan 250012China State Key Laboratory of Biotherapy and Cancer Center National Clinical Research Center for GeriatricsWest China Hospitaland Collaborative Innovation Center of BiotherapySichuan UniversityChengdu 610041China National Key Laboratory of Green Pesticide Key Laboratory of Pesticide&Chemical Biology of Ministry of EducationHubei International Scientific and Technological Cooperation Base of Pesticide and Green SynthesisCollege of ChemistryCentral China Normal UniversityWuhan 430079China College of Chemistry Pingyuan LaboratoryZhengzhou UniversityZhengzhou 450001China

In the realm of drug discovery,recent advancements have paved the way for innovative approaches and *** comprehensive review encapsulates six distinct yet interrelated mini-reviews,each shedding light on novel strategies in drug development.(a)The resurgence of covalent drugs is highlighted,focusing on the targeted covalent inhibitors(TCIs)and their role in enhancing selectivity and affinity.(b)The potential of the quantum mechanics-based computational aid drug design(CADD)tool,Cov_DOX,is introduced for predicting protein-covalent ligand binding structures and affinities.(c)The scaffolding function of proteins is proposed as a new avenue for drug design,with a focus on modulating protein-protein interactions through small molecules and proteolysis targeting chimeras(PROTACs).(d)The concept of pro-PROTACs is explored as a promising strategy for cancer therapy,combining the principles of prodrugs and PROTACs to enhance specificity and reduce toxicity.(e)The design of prodrugs through carbon-carbon bond cleavage is discussed,offering a new perspective for the activation of drugs with limited modifiable functional groups.(f)The targeting of programmed cell death pathways in cancer therapies with small molecules is reviewed,emphasizing the induction of autophagy-dependent cell death,ferroptosis,and *** insights collectively contribute to a deeper understanding of the dynamic landscape of drug discovery.

关键词： Drug discovery Covalent inhibitors Computational drug design Protein scaffolding Pro-PROTACs Programmed cell death C-C cleavage

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Scatter Search Based Particle Swarm optimization Algorithm for Earliness/Tardiness Flowshop Scheduling with Uncertainty

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International Journal of Automation and computing 2016年第3期13卷 285-295页

作者： Jia-Can Geng Zhe Cui Xing-Sheng Gu Key Laboratory of Advanced Control and Optimization for Chemical Process Ministry of EducationEast China University of Science and Technology

Considering the imprecise nature of the data in real-world problems, the earliness/tardiness （E/T） fiowshop scheduling problem with uncertain processing time and distinct due windows is concerned in this paper. A fuzzy scheduling model is established and then transformed into a deterministic one by employing the method of maximizing the membership function of middle value. Moreover, an effective scatter search based particle swarm optimization （SSPSO） algorithm is proposed to minimize the sum of total earliness and tardiness penalties. The proposed SSPSO algorithm incorporates the scatter search （SS） algorithm into the frame of particle swarm optimization （PSO） algorithm and gives full play to their characteristics of fast convergence and high diversity. Besides, a differential evolution （DE） scheme is used to generate solutions in the SS. In addition, the dynamic update strategy and critical conditions are adopted to improve the performance of SSPSO. The simulation results indicate the superiority of SSPSO in terms of effectiveness and efficiency.

关键词： Earliness/tardiness (E/T) scheduling fuzzy modeling scatter search (SS) particle swarm optimization (PSO).

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A new improved Alopex-based evolutionary algorithm and its application to parameter estimation

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Journal of Central South University 2013年第1期20卷 123-133页

作者：桑志祥李绍军董跃华 Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education (East China University of Science and Technology)

In this work, focusing on the demerit of AEA (Alopex-based evolutionary algorithm) algorithm, an improved AEA algorithm (AEA-C) which was fused AEA with clonal selection algorithm was proposed. Considering the irrationality of the method that generated candidate solutions at each iteration of AEA, clonal selection algorithm could be applied to improve the method. The performance of the proposed new algorithm was studied by using 22 benchmark functions and was compared with original AEA given the same conditions. The experimental results show that the AEA-C clearly outperforms the original AEA for almost all the 22 benchmark functions with 10, 30, 50 dimensions in success rates, solution quality and stability. Furthermore, AEA-C was applied to estimate 6 kinetics parameters of the fermentation dynamics models. The standard deviation of the objective function calculated by the AEA-C is 41.46 and is far less than that of other literatures' results, and the fitting curves obtained by AEA-C are more in line with the actual fermentation process curves.

关键词： Alopex evolutionary algorithm Alopex-based evolutionary algorithm clone selection parameter estimation

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Adaptive neural control for pure-feedback nonlinear time-delay systems with unknown dead-zone: a Lyapunov-Razumikhin method

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控制理论与应用（英文版） 2013年第1期11卷 18-26页

作者： Zhaoxu YU Jianxu LUO Ji LIU Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology

This paper addresses the problem of adaptive neural control for a class of uncertain pure-feedback nonlinear systems with multiple unknown state time-varying delays and unknown dead-zone. Based on a novel combination of the Razumikhin functional method, the backstepping technique and the neural network parameterization, an adaptive neural control scheme is developed for such systems. All closed-loop signals are shown to be semiglobally uniformly ultimately bounded, and the tracking error remains in a small neighborhood of the origin. Finally, a simulation example is given to demonstrate the effectiveness of the proposed control schemes.

关键词： Pure-feedback nonlinear systems Adaptive neural control Razumikhin functional Time-delay Deadzone

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chemical process dynamic optimization based on hybrid differential evolution algorithm integrated with Alopex

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Journal of Central South University 2013年第4期20卷 950-959页

作者：范勤勤吕照民颜学峰郭美锦 Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education (East China University of Science and Technology)

To solve dynamic optimization problem of chemical process (CPDOP), a hybrid differential evolution algorithm, which is integrated with Alopex and named as Alopex-DE, was proposed. In Alopex-DE, each original individual has its own symbiotic individual, which consists of control parameters. Differential evolution operator is applied for the original individuals to search the global optimization solution. Alopex algorithm is used to co-evolve the symbiotic individuals during the original individual evolution and enhance the fitness of the original individuals. Thus, control parameters are self-adaptively adjusted by Alopex to obtain the real-time optimum values for the original population. To illustrate the whole performance of Alopex-DE, several varietal DEs were applied to optimize 13 benchmark functions. The results show that the whole performance of Alopex-DE is the best. Further, Alopex-DE was applied to solve 4 typical CPDOPs, and the effect of the discrete time degree on the optimization solution was analyzed. The satisfactory result is obtained.

关键词： evolutionary computation dynamic optimization differential evolution algorithm Alopex algorithm self-adaptivity

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Multimodal process monitoring based on transition-constrained Gaussian mixture model

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Chinese Journal of chemical Engineering 2020年第12期28卷 3070-3078页

作者： Shutian Chen Qingchao Jiang Xuefeng Yan Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and TechnologyShanghai 200237China

Reliable process monitoring is important for ensuring process safety and product quality.A production process is generally characterized bymultiple operation modes,and monitoring thesemultimodal processes is *** multimodal monitoring methods rely on the assumption that the modes are independent of each other,which may not be appropriate for practical *** study proposes a transition-constrained Gaussian mixture model method for efficient multimodal process *** technique can reduce falsely and frequently occurring mode transitions by considering the time series information in the mode identification of historical and online *** process enables the identified modes to reflect the stability of actual working conditions,improve mode identification accuracy,and enhance monitoring reliability in cases of mode *** studies on a numerical simulation example and simulation of the penicillin fermentation process are provided to verify the effectiveness of the proposed approach inmultimodal process monitoring with mode overlap.

关键词： Multimodal process monitoring Gaussian mixture model State transition matrix Process control Process systems Systems engineering

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