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学者姓名：李晓理
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摘要 ：
There has recently been increasing attention paid to sulphur dioxide (SO2) pollution owing to its hazardous effect on both human health and atmospheric environment. To handle this problem, the wet flue gas desulphurization (FGD) system has found wide applications in SO2 emitting industries. Accurate prediction of SO2 emissions in treated flue gas serves the purpose of providing timely operating guidance for the FGD system. However, the wet FGD process is characterized by highly nonlinear dynamics and non-stationarity, which poses significant difficulties and limitations for traditional modeling methods. To address above issues, in this article, an integrated model is proposed to perform SO2 emission forecasting for an FGD process. Our integrated model comprises a multiplicity of techniques, including complete ensemble empirical mode decomposition with adaptive noise stacking ensemble learning (SEL) and permutation-based entropy (PEN). The serves as decomposing SO2 emission signal, then the complexity of each decomposed sub-series is analyzed by PEN and ones with similar scores are combined, finally a stacking-based ensemble learning model which incorporates different types of member models are developed for modeling purposes. The proposed method was validated and evaluated by measurements of a real FGD system in a 600MW coal-fired unit, and experimental results illustrate the superiority of our method.
关键词 ：
Neural network Neural network CEEMDAN-forecasting CEEMDAN-forecasting Ensemble learning Ensemble learning Wet flue gas desulfurization Wet flue gas desulfurization Stacking Stacking
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| GB/T 7714 | Li, Xiaoli , Liu, Quanbo , Wang, Kang . A CEEMDAN-based Stacking Ensemble Learning Method for SO2 Emission Forecast in a Wet FGD Process [J]. | INFORMATION TECHNOLOGY AND CONTROL , 2024 , 53 (3) . |
| MLA | Li, Xiaoli 等. "A CEEMDAN-based Stacking Ensemble Learning Method for SO2 Emission Forecast in a Wet FGD Process" . | INFORMATION TECHNOLOGY AND CONTROL 53 . 3 (2024) . |
| APA | Li, Xiaoli , Liu, Quanbo , Wang, Kang . A CEEMDAN-based Stacking Ensemble Learning Method for SO2 Emission Forecast in a Wet FGD Process . | INFORMATION TECHNOLOGY AND CONTROL , 2024 , 53 (3) . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
This study focuses primarily on sulfur dioxide (SO2) emissions control problem in a wet flue gas desulfurization (WFGD) process, and our objective is to design an intelligent control system so that the outlet SO2 concentration satisfies the SO2 emission standard. In our approach, a multimodel control framework, which is made up of a linear robust controller and a neural controller, is integrated with the invasive weed optimization (IWO) algorithm in an elegant fashion and used for SO2 emissions control purposes. A case study is carried out based on operation data from a 600 MW coal-fired unit, and simulation results show that IWO-based automatic clustering can identify different operating modes in the WFGD process with high accuracy. Further, the established multimodel control system can remove SO2 emissions effectively. Experimental results show that SO2 emissions can be removed effectively with the proposed method, and this could provide engineering guidance to design a WFGD control system.
关键词 ：
Multiple models Multiple models Invasive weed optimization (IWO) Invasive weed optimization (IWO) Adaptive control Adaptive control Nonlinear control Nonlinear control Wet flue gas desulfurization (WFGD) Wet flue gas desulfurization (WFGD)
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| GB/T 7714 | Liu, Quanbo , Li, Xiaoli , Wang, Kang . Removal of Sulfur Dioxide in Flue Gas Using Invasive Weed Optimization-Based Control Method [J]. | JOURNAL OF ENVIRONMENTAL ENGINEERING , 2024 , 150 (3) . |
| MLA | Liu, Quanbo 等. "Removal of Sulfur Dioxide in Flue Gas Using Invasive Weed Optimization-Based Control Method" . | JOURNAL OF ENVIRONMENTAL ENGINEERING 150 . 3 (2024) . |
| APA | Liu, Quanbo , Li, Xiaoli , Wang, Kang . Removal of Sulfur Dioxide in Flue Gas Using Invasive Weed Optimization-Based Control Method . | JOURNAL OF ENVIRONMENTAL ENGINEERING , 2024 , 150 (3) . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
The smelting of non-ferrous metals produces substantial quantities of sulfur dioxide (SO2$$ {}_2 $$)-laden flue gas, which is seriously harmful to environment and humans. To improve the conversion ratio of SO2$$ {}_2 $$ and minimize environmental pollution, controlling converter inlet temperature during acid production has proven to be an efficient approach. However, unsteadiness of smelting procedure leads to frequent changes in the concentration of SO2$$ {}_2 $$, which affects the catalytic conversion of SO2$$ {}_2 $$ and the production of sulfuric acid. To regulate converter inlet temperature, a proposed method of multi-model predictive control is introduced. First, working conditions are divided and characterized according to the range of SO2$$ {}_2 $$ concentration. Then, the mathematical model is established for each working condition and the model predictive controller is designed. Finally, an effective switching mechanism is established to realize smooth switching under different working conditions and closed-loop control of the whole system. Through simulation validation, compared with traditional single-model predictive controllers and multi-model PID controllers, the proposed approach demonstrates improved transient performance and steady-state performance. Simulation outcomes clearly highlight the superiority of the proposed algorithm.
关键词 ：
acid production acid production multi-model predictive control multi-model predictive control flue gas flue gas converter inlet temperature converter inlet temperature switching control switching control
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| GB/T 7714 | Liu, Minghua , Li, Xiaoli , Wang, Kang et al. Multi-model predictive control of converter inlet temperature in the process of acid production with flue gas [J]. | INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING , 2024 , 38 (5) : 1725-1743 . |
| MLA | Liu, Minghua et al. "Multi-model predictive control of converter inlet temperature in the process of acid production with flue gas" . | INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING 38 . 5 (2024) : 1725-1743 . |
| APA | Liu, Minghua , Li, Xiaoli , Wang, Kang , Liu, Zhiqiang , Li, Guihai . Multi-model predictive control of converter inlet temperature in the process of acid production with flue gas . | INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING , 2024 , 38 (5) , 1725-1743 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
This paper develops a composite output consensus control protocol for a general linear multiagent system subject to mismatched disturbances, which incorporates active disturbance-rejection control and fully distributed adaptive consensus control. To estimate and then cancel out the effect of mismatched disturbances on the outputs of the agents, heterogeneous generalized equivalent-input-disturbance estimators are constructed in the inner loop. Then a fully distributed adaptive feedback controller is designed to achieve consensus control based on the states of the designed heterogeneous observers for the agents. The restriction on the disturbances is lowered, the requirement for the global information of the communication topology is removed, and the exchanging information among agents is only relative estimated states. Further, the output consensus performance is analyzed for the closed-loop multiagent system. Our results complement and improve the results of the existing literature. Lastly, the effectiveness and superiority of the developed method are demonstrated through a numerical simulation and a comparison with the distributed extended-state-observer-based method.
关键词 ：
consensus control consensus control Multiagent systems Multiagent systems undirected graph undirected graph heterogeneous disturbances heterogeneous disturbances disturbance estimation disturbance estimation distributed control distributed control adaptive control adaptive control
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| GB/T 7714 | Yu, Pan , Ding, Yifan , Liu, Kang-Zhi et al. Composite Output Consensus Control for General Linear Multiagent Systems With Heterogeneous Mismatched Disturbances [J]. | IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS , 2024 , 10 : 434-444 . |
| MLA | Yu, Pan et al. "Composite Output Consensus Control for General Linear Multiagent Systems With Heterogeneous Mismatched Disturbances" . | IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 10 (2024) : 434-444 . |
| APA | Yu, Pan , Ding, Yifan , Liu, Kang-Zhi , Li, Xiaoli . Composite Output Consensus Control for General Linear Multiagent Systems With Heterogeneous Mismatched Disturbances . | IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS , 2024 , 10 , 434-444 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Evolutionary algorithms have proven to be extremely effective at tackling multi-objective opti-mization problems (MOPs). However, when dealing with many-objective optimization problems (MaOPs), their performance frequently degrades, especially when the Pareto marking irregular shapes. The population pressure to choose the Pareto optimal front and address the generaliz-ability of different Pareto front shapes becomes more challenging as the number of objectives increases. We present a strength Pareto evolutionary algorithm based on adaptive reference points (SPEA/ARP) to address this problem. First, the reference points are updated using current and historical population information. The angles between the current demographic information and the predefined uniform reference points are used to select the active reference points, and the adaptive reference points are selected from the historical population information projected onto the reference plane. Second, the fitness function values are applied to classify the environmental selection criteria into two categories: 1) The angle distance scaling function using adaptive reference points is utilized to increase selection pressure, and the diversity of non-dominated solutions is balanced using the angle-based secondary selection technique. 2) Otherwise, the fitness function values are employed to choose the next generation of non-dominated solutions. Third, an aggregate fitness r-value generated by the angle distance scaling function is employed to construct matching pools that produce valid offsprings. Finally, extensive experiments are carried out to demonstrate SPEA/ARP performance by comparing it with six state-of-the-art many -objective evolutionary algorithms on 5-, 10-, 15-objective of 31 benchmark MaOPs. The experi-ments show that SPEA/ARP outperforms the compared algorithms.
关键词 ：
Irregular fronts Irregular fronts Strength Pareto evolutionary algorithm Strength Pareto evolutionary algorithm Matching pool Matching pool Angle distance scaling function Angle distance scaling function Adaptive reference points Adaptive reference points
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| GB/T 7714 | Li, Xin , Li, Xiaoli , Wang, Kang et al. A strength pareto evolutionary algorithm based on adaptive reference points for solving irregular fronts [J]. | INFORMATION SCIENCES , 2023 , 626 : 658-693 . |
| MLA | Li, Xin et al. "A strength pareto evolutionary algorithm based on adaptive reference points for solving irregular fronts" . | INFORMATION SCIENCES 626 (2023) : 658-693 . |
| APA | Li, Xin , Li, Xiaoli , Wang, Kang , Yang, Shengxiang . A strength pareto evolutionary algorithm based on adaptive reference points for solving irregular fronts . | INFORMATION SCIENCES , 2023 , 626 , 658-693 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
本发明公开了一种基于跟踪微分器的机械臂末端位置与力变阻抗协同控制方法，将模仿学习与自适应变阻抗控制相结合用于机械臂末端位置与力协同控制策略学习中，设计新的自适应率以应对在斜坡、曲面等复杂环境下的力跟踪问题，由此保证清洁任务的顺利完成。本发明采取末端位置与力协同学习策略，充分保证了机械臂在执行清洁任务时，末端运动轨迹、施力与示教信息特征的相似性；同时，在与不同环境接触时较高精度的期望力跟踪，确保了机器人在不同环境下利用学习到的清洁操作技能完成清洁任务的效率。本发明对于执行易碎物品的接触清洁任务具有重要意义。
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| GB/T 7714 | 刘春芳 , 李长峰 , 李晓理 et al. 一种基于跟踪微分器的机械臂末端位置与力变阻抗协同控制方法 : CN202310163371.0[P]. | 2023-02-24 . |
| MLA | 刘春芳 et al. "一种基于跟踪微分器的机械臂末端位置与力变阻抗协同控制方法" : CN202310163371.0. | 2023-02-24 . |
| APA | 刘春芳 , 李长峰 , 李晓理 , 余攀 . 一种基于跟踪微分器的机械臂末端位置与力变阻抗协同控制方法 : CN202310163371.0. | 2023-02-24 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
The widespread adoption of intelligent devices has led to the generation of vast amounts of Global Positioning System (GPS) trajectory data. One of the significant challenges in this domain is to accurately identify stopping points from GPS trajectory data. Traditional clustering methods have proven ineffective in accurately identifying non-stopping points caused by trailing or round trips. To address this issue, this paper proposes a novel density peak clustering algorithm based on coherence distance, incorporating temporal and entropy constraints, referred to as the two-step DPCC-TE. The proposed algorithm introduces a coherence index to integrate spatial and temporal features, and imposes temporal and entropy constraints on the clusters to mitigate local density increase caused by slow-moving points and back-and-forth movements. Moreover, to address the issue of interactions between subclusters after one-step clustering, a two-step clustering algorithm is proposed based on the DPCC-TE algorithm. Experimental results demonstrate that the proposed two-step clustering algorithm outperforms the DBSCAN-TE and one-step DPCC-TE methods, and achieves an accuracy of 95.49% in identifying stopping points.
关键词 ：
stopping point extraction stopping point extraction two-step clustering two-step clustering temporal constraint temporal constraint density peaks clustering density peaks clustering entropy constraint entropy constraint
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| GB/T 7714 | Wang, Kang , Pang, Liwei , Li, Xiaoli . Identification of Stopping Points in GPS Trajectories by Two-Step Clustering Based on DPCC with Temporal and Entropy Constraints [J]. | SENSORS , 2023 , 23 (7) . |
| MLA | Wang, Kang et al. "Identification of Stopping Points in GPS Trajectories by Two-Step Clustering Based on DPCC with Temporal and Entropy Constraints" . | SENSORS 23 . 7 (2023) . |
| APA | Wang, Kang , Pang, Liwei , Li, Xiaoli . Identification of Stopping Points in GPS Trajectories by Two-Step Clustering Based on DPCC with Temporal and Entropy Constraints . | SENSORS , 2023 , 23 (7) . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
The wet flue gas desulfurization (WFGD) process is a complex controlled system with nonlinear and time-varying characteristics. There are a variety of external disturbances in the desulfurization process, which can easily cause a jump in system parameters and even structure. In response to this problem, a model-free adaptive control scheme based on a switching mechanism is proposed, and the convergence analysis is given. In the scheme, firstly, the operating conditions of the WFGD system are divided into n kinds based on multiple external disturbances, and the mathematical models of the multi-input multi-output WFGD system under different operating conditions are established. Then the MFA controllers of the WFGD system under multiple operating conditions are established, respectively. Finally, a switching index function is designed to switch the controller of the WFGD system according to different operating conditions, so that the desulfurization system is always controlled by the most appropriate controller. In the simulation experiment, according to the actual data of the WFGD system of a 1000 MW coal-fired power station in Beijing, the modeling test and algorithm simulation of the WFGD system under three operating conditions are carried out. The simulation results show that this method can effectively reduce the transient error of the WFGD system output and improve the desulfurization effect.
关键词 ：
switching mechanism switching mechanism multiple operating conditions multiple operating conditions MIMO system MIMO system wet flue gas desulfurization wet flue gas desulfurization model-free adaptive control model-free adaptive control
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| GB/T 7714 | Liu, Jian , Li, Xiaoli , Wang, Kang . Model-free adaptive control based on switching mechanism for multi-input multi-output wet flue gas desulfurization system [J]. | INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING , 2022 , 36 (11) : 2795-2822 . |
| MLA | Liu, Jian et al. "Model-free adaptive control based on switching mechanism for multi-input multi-output wet flue gas desulfurization system" . | INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING 36 . 11 (2022) : 2795-2822 . |
| APA | Liu, Jian , Li, Xiaoli , Wang, Kang . Model-free adaptive control based on switching mechanism for multi-input multi-output wet flue gas desulfurization system . | INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING , 2022 , 36 (11) , 2795-2822 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
本发明公开了一种有向通信下具有异构动态不确定性的多智能体的一致跟踪控制方法和系统，所述智能体的模型差异由动态不确定性特性描述。基于输出信息设计分散式状态观测器，利用相邻多智能体间的相对输出信息构建分布式一致跟踪控制协议，使跟随者的输出能够渐进跟踪领航者的输出轨迹。本发明提供的技术方案带来的有益效果为：本发明将多智能体间的模型差异处理为动态不确定性，提出了一种适用于一般异构多智能体系统的一致跟踪控制算法；多智能体间的交换信息仅包含相邻跟随者的相对输出信息，降低了网络通讯负荷；通过充分利用智能体动态不确定性的频域特征，与现有方法相比，所提出的算法极大地降低了系统设计保守性。
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| GB/T 7714 | 余攀 , 丁意凡 , 李晓理 et al. 一种有向通信下异构不确定多智能体一致跟踪控制方法及系统 : CN202211217407.0[P]. | 2022-10-03 . |
| MLA | 余攀 et al. "一种有向通信下异构不确定多智能体一致跟踪控制方法及系统" : CN202211217407.0. | 2022-10-03 . |
| APA | 余攀 , 丁意凡 , 李晓理 , 刘春芳 , 王康 . 一种有向通信下异构不确定多智能体一致跟踪控制方法及系统 : CN202211217407.0. | 2022-10-03 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
本发明公开了一种基于孤立森林和加权随机森林的烟气制酸数据清洗及优化方法，该方法对烟气制酸脱硫过程进行分析，结合大量的生产监测数据，采用最大信息系数分析方法对风机出口O2浓度、风机出口烟气温度、一级动力波入口压力、炉内压力、风机入口流量、转化器入口温度等工艺变量进行相关性分析，获得影响SO2转化率和硫酸产量等指标的关键变量。然后，针对关键变量，对其原始数据的变化趋势进行分析，利用孤立森林算法对数据集中的异常值、离群值进行识别并剔除，得到缺失数据集。最后，采用加权随机森林算法对缺失数据集进行拟合预测，补偿其中的缺失数据，实现对烟气制酸过程数据的清洗及优化，从而达到提升脱硫效率和硫酸产量的目的。
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| GB/T 7714 | 李晓理 , 刘明华 , 赵金元 et al. 一种基于孤立森林和加权随机森林的烟气制酸数据清洗及优化方法 : CN202211492179.8[P]. | 2022-11-25 . |
| MLA | 李晓理 et al. "一种基于孤立森林和加权随机森林的烟气制酸数据清洗及优化方法" : CN202211492179.8. | 2022-11-25 . |
| APA | 李晓理 , 刘明华 , 赵金元 , 李桂海 , 刘正明 , 王康 . 一种基于孤立森林和加权随机森林的烟气制酸数据清洗及优化方法 : CN202211492179.8. | 2022-11-25 . |
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