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学者姓名：李晓理
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Abstract ：
In this article, a global output feedback control scheme is developed for a class of uncertain nonlinear systems subject to input quantization and unknown output function. By employing a time-varying gain and a time-invariant gain, we address the challenges posed by quantization errors and nonlinear functions with an unknown linear growth rate. Additionally, we determine an allowable measurement sensitivity error by solving a straightforward inequality. We demonstrate that the proposed scheme ensures global asymptotic stability for the system and guarantees that all signals of the closed-loop system remain bounded. Finally, we validate the proposed approach through a mathematical example and an experiment conducted on the QUBE-Servo 2 equipped with an inertial disc module.
Keyword ：
Global asymptotic stability Global asymptotic stability unknown output function unknown output function Quantization (signal) Quantization (signal) Nonlinear systems Nonlinear systems Information science Information science quantization quantization Sensitivity Sensitivity networked control systems (NCSs) networked control systems (NCSs) Hysteresis Hysteresis Lyapunov methods Lyapunov methods Output feedback Output feedback Measurement uncertainty Measurement uncertainty Symmetric matrices Symmetric matrices Asymptotic stability Asymptotic stability
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| GB/T 7714 | Yu, Xiaowei , Li, Xiaoli . Global Asymptotic Stabilization Control for Uncertain Nonlinear Systems With Input Quantization and Unknown Output Function [J]. | IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS , 2024 . |
| MLA | Yu, Xiaowei 等. "Global Asymptotic Stabilization Control for Uncertain Nonlinear Systems With Input Quantization and Unknown Output Function" . | IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS (2024) . |
| APA | Yu, Xiaowei , Li, Xiaoli . Global Asymptotic Stabilization Control for Uncertain Nonlinear Systems With Input Quantization and Unknown Output Function . | IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS , 2024 . |
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Abstract ：
The process of smelting non-ferrous metals results in significant emissions of flue gas that contains sulfur dioxide (SO 2$$ {}_2 $$), which is very harmful to the environment. Through precise control of converter inlet temperature, it is feasible to enhance the conversion ratio of SO 2$$ {}_2 $$ and simultaneously mitigate environmental pollution by generating acid from flue gas. Because of the high degree of uncertainty in smelting process, converter inlet temperature is challenging to regulate and controller frequently needs updating. To improve control performance and decrease controller update times, an event-triggered neural network model predictive control (ETNMPC) strategy is proposed. First, long short-term memory (LSTM) prediction model and model predictive controller are developed. Second, it is decided whether to update the existing controller by designing an event-triggered mechanism. Finally, using real data from a copper facility in Jiangxi Province, the temperature control experiment of converter inlet is carried out. Simulation results demonstrate that the proposed ETNMPC outperforms conventional time-triggered method in terms of control performance, greatly lowers the times of controller updates, and significantly lowers computation costs and communication burden. Key findings: (1) LSTM neural network is used to establish the predictive model of converter inlet temperature and model predictive controller is designed. (2) Two different event-triggered mechanisms with fixed threshold are designed. (3) Event-triggered neural network predictive control can effectively reduce the number of controller triggers, save computing resources, and improve system performance. image
Keyword ：
neural network predictive control neural network predictive control event-triggered mechanism event-triggered mechanism tracking control tracking control flue gas acid production flue gas acid production
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| GB/T 7714 | Liu, Minghua , Li, Xiaoli , Wang, Kang . Neural network predictive control of converter inlet temperature based on event-triggered mechanism in flue gas acid production [J]. | OPTIMAL CONTROL APPLICATIONS & METHODS , 2024 , 45 (4) : 1815-1831 . |
| MLA | Liu, Minghua 等. "Neural network predictive control of converter inlet temperature based on event-triggered mechanism in flue gas acid production" . | OPTIMAL CONTROL APPLICATIONS & METHODS 45 . 4 (2024) : 1815-1831 . |
| APA | Liu, Minghua , Li, Xiaoli , Wang, Kang . Neural network predictive control of converter inlet temperature based on event-triggered mechanism in flue gas acid production . | OPTIMAL CONTROL APPLICATIONS & METHODS , 2024 , 45 (4) , 1815-1831 . |
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Abstract ：
Efficient flotation beneficiation heavily relies on accurate flotation condition recognition based on monitored froth video. However, the recognition accuracy is hindered by limitations of extracting temporal features from froth videos and establishing correlations between complex multi-modal high-order data. To address the difficulties of inadequate temporal feature extraction, inaccurate online condition detection, and inefficient flotation process operation, this paper proposes a novel flotation condition recognition method named the multi-modal temporal hypergraph neural network (MTHGNN) to extract and fuse multi-modal temporal features. To extract abundant dynamic texture features from froth images, the MTHGNN employs an enhanced version of the local binary pattern algorithm from three orthogonal planes (LBP-TOP) and incorporates additional features from the three-dimensional space as supplements. Furthermore, a novel multi-view temporal feature aggregation network (MVResNet) is introduced to extract temporal aggregation features from the froth image sequence. By constructing a temporal multi-modal hypergraph neural network, we encode complex high-order temporal features, establish robust associations between data structures, and flexibly model the features of froth image sequence, thus enabling accurate flotation condition identification through the fusion of multi-modal temporal features. The experimental results validate the effectiveness of the proposed method for flotation condition recognition, providing a foundation for optimizing flotation operations.
Keyword ：
MVResNet MVResNet froth image sequence froth image sequence temporal HGNN temporal HGNN multi-modal fusion multi-modal fusion flotation condition identification flotation condition identification
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| GB/T 7714 | Fan, Zunguan , Feng, Yifan , Wang, Kang et al. Multi-Modal Temporal Hypergraph Neural Network for Flotation Condition Recognition [J]. | ENTROPY , 2024 , 26 (3) . |
| MLA | Fan, Zunguan et al. "Multi-Modal Temporal Hypergraph Neural Network for Flotation Condition Recognition" . | ENTROPY 26 . 3 (2024) . |
| APA | Fan, Zunguan , Feng, Yifan , Wang, Kang , Li, Xiaoli . Multi-Modal Temporal Hypergraph Neural Network for Flotation Condition Recognition . | ENTROPY , 2024 , 26 (3) . |
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Abstract ：
Sulfur dioxide (SO2) can cause detrimental impacts on the ecosystem. It is well known that coal-fired power plants play a dominant role in SO2 emissions, and consequently industrial flue gas desulfurization (IFGD) systems are widely used in coal-fired power plants. To remove SO2 effectively such that ultra-low emission standard can be satisfied, IFGD modeling has become urgently necessary. IFGD is a chemical process with long-term dependencies between time steps, and it typically exhibits strong non-linear behavior. Furthermore, the process is rendered non-stationary due to frequent changes in boiler loads. The above-mentioned properties make IFGD process modeling a truly formidable problem, since the chosen model should have the capability of learning long-term dependencies, non-linear dynamics and non-stationary processes simultaneously. Previous research in this area fails to take all the above points into account at a time, and this calls for a novel modeling approach so that satisfactory modeling performance can be achieved. In this work, a novel bivariate empirical mode decomposition (BEMD)-based temporal convolutional network (TCN) approach is proposed. In our approach, BEMD is employed to generate relatively stationary processes, while TCN, which possesses long-term memory ability and uses dilated causal convolutions, serves to model each subprocess. Our method was validated using the operating data from the desulfurization system of a coal-fired power station in China. Simulation results show that our approach yields desirable performance, which demonstrates its effectiveness in the IFGD dynamic modeling problem.
Keyword ：
bivariate empirical mode decomposition bivariate empirical mode decomposition system modeling system modeling flue gas desulfurization flue gas desulfurization temporal convolutional network temporal convolutional network
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| GB/T 7714 | Liu, Quanbo , Li, Xiaoli , Wang, Kang . Dynamic Modeling of Flue Gas Desulfurization Process via Bivariate EMD-Based Temporal Convolutional Network [J]. | APPLIED SCIENCES-BASEL , 2023 , 13 (13) . |
| MLA | Liu, Quanbo et al. "Dynamic Modeling of Flue Gas Desulfurization Process via Bivariate EMD-Based Temporal Convolutional Network" . | APPLIED SCIENCES-BASEL 13 . 13 (2023) . |
| APA | Liu, Quanbo , Li, Xiaoli , Wang, Kang . Dynamic Modeling of Flue Gas Desulfurization Process via Bivariate EMD-Based Temporal Convolutional Network . | APPLIED SCIENCES-BASEL , 2023 , 13 (13) . |
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Abstract ：
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.
Keyword ：
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 . |
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Abstract ：
Due to the complex process of flue gas acid production and numerous processes, the controlled objects in the whole process have the characteristics of time-variation, randomness, nonlinearity and large lag. For SO2 fan, because the traditional PID control adopts the parameters set in advance, it is difficult to play an effective role in the complex flue gas acid production system, therefore, a fuzzy PID control method is proposed. The two-dimensional structure fuzzy controller model with two inputs and three outputs can dynamically adjust the PID controller parameters. By establishing a transfer function model for factory temperature data, we compare the fuzzy PID control with the traditional PID controller. The results demonstrate that the fuzzy PID controller possesses strong anti-interference ability, has a shorter adjustment time, and exhibits a smaller overshoot volume, which enables more effective control of the fan outlet temperature.
Keyword ：
Fuzzy PID Control Fuzzy PID Control PID Control PID Control Outlet Temperature of Fan Outlet Temperature of Fan
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| GB/T 7714 | Wang, Zihao , Li, Xiaoli , Wang, Kang et al. Fan flue gas temperature control system based on fuzzy PID control [J]. | 2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS , 2023 : 394-399 . |
| MLA | Wang, Zihao et al. "Fan flue gas temperature control system based on fuzzy PID control" . | 2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS (2023) : 394-399 . |
| APA | Wang, Zihao , Li, Xiaoli , Wang, Kang , Li, Yang . Fan flue gas temperature control system based on fuzzy PID control . | 2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS , 2023 , 394-399 . |
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Abstract ：
The heating, ventilation, and air conditioning(HVAC) system consumes a large amount of energy in buildings. Accurate modeling of the HVAC refrigeration room system is crucial for building temperature control and optimization of energy consumption. In this paper, Levenberg Marquardt (LM) algorithm and particle swarm optimization (PSO) algorithm are used to establish a nonlinear autoregressive neural network model (PSO-NARX) for the modeling of water chillers in HVAC systems. NARX is a model used to describe nonlinear discrete systems. At the same time, using particle swarm optimization algorithm can improve the accuracy of the prediction model. The experimental results show that the PSO-NARX model can effectively model and predict the chiller model, and its performance is better compared to traditional DNN neural networks.
Keyword ：
Data-driven Data-driven Modeling Modeling chiller system chiller system NARX NARX PSO Algorithm PSO Algorithm
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| GB/T 7714 | Song, Zilong , Li, Xiaoli , Wang, Kang et al. Chiller System Modeling using PSO Optimization based NARX approach [J]. | 2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS , 2023 : 616-621 . |
| MLA | Song, Zilong et al. "Chiller System Modeling using PSO Optimization based NARX approach" . | 2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS (2023) : 616-621 . |
| APA | Song, Zilong , Li, Xiaoli , Wang, Kang , Li, Yang . Chiller System Modeling using PSO Optimization based NARX approach . | 2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS , 2023 , 616-621 . |
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Abstract ：
The sulfur dioxide (SO2) concentration of flue gas in metal smelting industry is high and difficult to recycle. The commonly used SO2 desulfurization treatment is acid production from flue gas, however, it is difficult to directly measure the conversion rate of SO2. In this paper, we present a soft sensor method based on Long Short-Term Memory (LSTM) which integrates the attention mechanism for predicting SO2 conversion rate to tackle such problem. Considering that the change of SO2 conversion rate is affected by many external factors, the attention mechanism is used to quickly and accurately predict the change results by considering the system data of several past moments. The proposed attention mechanism uses LSTM units to encode the hidden state of the look back time data, obtains different attention weights, and then decodes and predicts SO2 conversion rate according to the hidden state. The experimental results indicate that LSTM model with attention mechanism has lower training cost compared with LSTM model. The training accuracy and soft sensor accuracy are also improved owing to the attention mechanism. It is instructive for SO2 conversion rate soft sensor in acid production from flue gas.
Keyword ：
soft sensor soft sensor Long Short-Term Memory neural network Long Short-Term Memory neural network attention mechanism attention mechanism Acid production from flue gas Acid production from flue gas
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| GB/T 7714 | Zhang, Yongzheng , Li, Xiaoli , Wang, Kang et al. A LSTM Model with Attention Mechanism for Soft Sensor of SO2 Conversion Rate in Flue Gas Acid Production Process [J]. | 2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS , 2023 : 27-32 . |
| MLA | Zhang, Yongzheng et al. "A LSTM Model with Attention Mechanism for Soft Sensor of SO2 Conversion Rate in Flue Gas Acid Production Process" . | 2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS (2023) : 27-32 . |
| APA | Zhang, Yongzheng , Li, Xiaoli , Wang, Kang , Li, Yang . A LSTM Model with Attention Mechanism for Soft Sensor of SO2 Conversion Rate in Flue Gas Acid Production Process . | 2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS , 2023 , 27-32 . |
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Abstract ：
This paper proposes a data-driven, multi-objective optimization control method based on policy iteration for flotation process, addressing the limitations of existing control methods that rely on the system model and cannot satisfy multiple performance indices simultaneously. Firstly, The ordinary linear quadratic regulator algorithm is improved with process data, enabling the algorithm to obtain optimal feedback gain without relying on the internal dynamics model of the system. Then, This method is further extended to the situation where multiple objectives exist, minimizing the deviation of the final additive amount while meeting control requirements for both concentrate and tailings grade. Finally, the convergence and effectiveness of the proposed method are verified by simulation experiments.
Keyword ：
Multi-objective optimization Multi-objective optimization Optimal control Optimal control Flotation process Flotation process Data-driven Data-driven
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| GB/T 7714 | Xu, Hang , Wang, Kang , Li, Xiaoli . Multi-objective Optimization Control of Flotation Process Based on Policy Iteration [J]. | 2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS , 2023 : 417-422 . |
| MLA | Xu, Hang et al. "Multi-objective Optimization Control of Flotation Process Based on Policy Iteration" . | 2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS (2023) : 417-422 . |
| APA | Xu, Hang , Wang, Kang , Li, Xiaoli . Multi-objective Optimization Control of Flotation Process Based on Policy Iteration . | 2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS , 2023 , 417-422 . |
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Abstract ：
The quality of flotation conditions directly affects the flotation efficiency. Aiming at the problems of difficult online detection, strong subjective arbitrariness, and low recognition efficiency of various flotation conditions in actual flotation work, a flotation condition recognition method based on hypergraph neural network (HGNN) and dynamic feature of forth images is proposed in this paper. Firstly, an improved local binary mode (LBP-TOP) algorithm is introduced to extract the dynamic features of forth sequence containing time information, and then features such as kurtosis and skewness are extracted as supplements to integrate the dynamic features of forth with the supplementary features. By utilizing the aforementioned characteristics and constructing a hypergraph, we have developed an HGNN model that facilitates high-order complex data correlation encoding, thus accomplishing accurate identification of flotation conditions. Finally, simulation shows the effectiveness of the proposed method.
Keyword ：
Forth flotation Forth flotation LBP-TOP LBP-TOP Hypergraph neural network Hypergraph neural network Condition identification Condition identification
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| GB/T 7714 | Fan, Zunguan , Wang, Kang , Li, Xiaoli . Flotation Condition Recognition Based on HGNN and Forth Image Dynamic Feature [J]. | 2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS , 2023 : 423-428 . |
| MLA | Fan, Zunguan et al. "Flotation Condition Recognition Based on HGNN and Forth Image Dynamic Feature" . | 2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS (2023) : 423-428 . |
| APA | Fan, Zunguan , Wang, Kang , Li, Xiaoli . Flotation Condition Recognition Based on HGNN and Forth Image Dynamic Feature . | 2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS , 2023 , 423-428 . |
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