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Zhengqiang Zhang - IEEE Xplore Author Profile

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Author details

Zhengqiang Zhang

Publications

83

Citations

1,731

Publications by Year

20112025

Co-Authors:

Fuad E. AlsaadiXiqiang CaoZiran ChenMou ChenXinran Chen

Show All Co-Authors (93)

Zhengqiang Zhang

Affiliation

School of Engineering

Qufu Normal University

Rizhao, China

Publication Topics

Adaptive Control,
Nonlinear Systems,
Tracking Error,
Optimal Control,
Time Delay,
Class Of Nonlinear Systems,
Class Of Systems,
Control Design,
Time-delay Systems,
Closed-loop System,
Positive Constant,
Adaptive Law

Biography

Zhengqiang Zhang received the B.S. and M.S. degrees from Qufu Normal University, Qufu, China, in 2000 and 2003, respectively, and the Ph.D. degree from the Nanjing University of Science and Technology, Nanjing, China, in 2011.,He is currently a Professor with the School of Engineering, Qufu Normal University. His current research interests include adaptive control, control of nonlinear systems, fault-tolerant control, and time-delay systems.(Based on document published on 8 January 2020).

Publications

83

Citations

1,731

Publications by Year

20112025

Co-Authors:

Fuad E. Alsaadi
Xiqiang Cao
Ziran Chen
Mou Chen
Xinran Chen

Show All Co-Authors (93)

Author's Published Works

Search History

Showing 1-25 of 138 results

Conferences (85)

Journals (52)

Early Access Articles (1)

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Jun Zhao(137)
Wei Wang(121)
Zhongyang Han(29)
Linqing Wang(23)
Ying Liu(20)
School of Control Science and Engineering, Dalian University of Technology, Dalian, China(58)
School of Control Sciences and Engineering, Dalian University of Technology, Dalian, China(17)
Key Laboratory of Intelligent Control and Optimization for Industrial Equipment, Ministry of Education, Dalian University of Technology, Dalian, China(12)
Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada(10)
Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland(8)
IEEE Transactions on Industrial Electronics(9)
2024 14th Asian Control Conference (ASCC)(8)
IEEE Transactions on Instrumentation and Measurement(8)
IEEE Transactions on Cybernetics(7)
2022 China Automation Congress (CAC)(6)
IEEE(138)
Dalian, China(11)
Shenyang, China(7)
Beijing, China(6)
Chongqing, China(6)
Xiamen, China(6)
Steel Industry(57)
Mean Absolute Percentage Error(42)
Blast Furnace(39)
Echo State Network(32)
Time Series(30)

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Results

Dynamic Modeling of Multioperating Conditions for a Three-Converter Gas Holder System in Steel Industry by Using Time-Varying Dynamic Bayesian Networks

Long Chen;Yaliang Yan;Jun Zhao;Wei Wang

IEEE Transactions on Instrumentation and Measurement

Year: 2025 | Volume: 74 | Journal Article |

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The multiple joint Linz-Donawitz converter gas (LDG) holder systems are usually employed to alleviate the LDG fluctuation in steel enterprises. A dynamic modeling method based on time-varying dynamic Bayesian network (TVDBN) is proposed for a three-converter gas holder system (TCGHS). Considering the connectivity, the difference in pressure, and the interlocking rules between different gas holders...Show More

Dynamic Modeling of Multioperating Conditions for a Three-Converter Gas Holder System in Steel Industry by Using Time-Varying Dynamic Bayesian Networks

Long Chen;Yaliang Yan;Jun Zhao;Wei Wang

IEEE Transactions on Instrumentation and Measurement

Year: 2025 | Volume: 74 | Journal Article |

An Uncertainty Involved Two-Stage Energy Optimization Framework Based on Deep Reinforcement Learning for Steel Enterprise

Zhiyuan Wang;Zhongyang Han;Jun Zhao;Wei Wang

IEEE Transactions on Industrial Electronics

Year: 2025 | Early Access Article |

The optimization of energy systems within the steel industry faces challenges due to the complexity of nonconvex models as well as supply and demand uncertainty. To address these issues, a novel two-stage data-driven deep reinforcement learning (DRL) framework is proposed in this study. The first stage is focused on offline learning, where probability distribution of energy media is incorporated i...Show More

An Uncertainty Involved Two-Stage Energy Optimization Framework Based on Deep Reinforcement Learning for Steel Enterprise

Zhiyuan Wang;Zhongyang Han;Jun Zhao;Wei Wang

IEEE Transactions on Industrial Electronics

Year: 2025 | Early Access Article |

A Multi-Condition Sequential Network Ensemble for Industrial Energy Storage Prediction Considering the Condition Switching Characteristics

Tianyu Wang;Fan Zhou;Yangjie Wu;Jun Zhao;Wei Wang

IEEE/CAA Journal of Automatica Sinica

Year: 2025 | Volume: 12, Issue: 2 | Journal Article |

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As a crucial storage and buffering apparatus for balancing the production and consumption of byproduct gases in industrial processes, accurate prediction of gas tank levels is essential for optimizing energy system scheduling. Considering that the continuous switching of the pressure and valve status (mechanism knowledge) would bring about multiple working conditions of the equipment, a multi-cond...Show More

A Multi-Condition Sequential Network Ensemble for Industrial Energy Storage Prediction Considering the Condition Switching Characteristics

Tianyu Wang;Fan Zhou;Yangjie Wu;Jun Zhao;Wei Wang

IEEE/CAA Journal of Automatica Sinica

Year: 2025 | Volume: 12, Issue: 2 | Journal Article |

Active Disturbance Rejection Control of Dense Medium Separation Process Based on Improved PI-GPC

Xiaoyao Zhao;Fan Zhou;Jun Zhao;Wei Wang

2024 China Automation Congress (CAC)

Year: 2024 | Conference Paper |

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Aiming at the complex field environment of dense medium separation process and the large amount of matrix inversion calculation in the traditional PI-GPC solution process, an adaptive stepped generalized predictive control method with proportional-integral structure (PI-ASGPC-LESO) based on active disturbance rejection strategy is proposed. Considering the large amount of calculation when PI-GPC s...Show More

Active Disturbance Rejection Control of Dense Medium Separation Process Based on Improved PI-GPC

Xiaoyao Zhao;Fan Zhou;Jun Zhao;Wei Wang

2024 China Automation Congress (CAC)

Year: 2024 | Conference Paper |

Reinforcement Learning with Model Predictive Control for Coordinated Operation of Power Plant

Chuang Wang;Qinggan Wei;Yang Liu;Jun Zhao;Wei Wang

2024 China Automation Congress (CAC)

Year: 2024 | Conference Paper |

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To address the challenges of rapid and accurate regulation of power generation load due to significant model nonlinearity, strong inertia, and substantial variable coupling in the load control process of boiler and turbine units in coal-fired power plants, this paper proposes a control policy that combines model predictive control with reinforcement learning based on a multi-operating condition mo...Show More

Reinforcement Learning with Model Predictive Control for Coordinated Operation of Power Plant

Chuang Wang;Qinggan Wei;Yang Liu;Jun Zhao;Wei Wang

2024 China Automation Congress (CAC)

Year: 2024 | Conference Paper |

Data-Driven Economic Predictive Control for Coordinated Control System of Coal-fired Units

Qiangqiang Zhao;Xinyu Zhang;Zhongyang Han;Jun Zhao;Wei Wang

2024 China Automation Congress (CAC)

Year: 2024 | Conference Paper |

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Coordinated control of coal-fired units is a key system to ensure the stability and economic operation of power supply. While the nonlinearity, strong coupling, and large inertia of coal-fired units bring about challenge for the model establishment resulting in unsatisfied control performance due to insufficient model accuracy. To address this issue, a novel data-driven economic predictive control...Show More

Data-Driven Economic Predictive Control for Coordinated Control System of Coal-fired Units

Qiangqiang Zhao;Xinyu Zhang;Zhongyang Han;Jun Zhao;Wei Wang

2024 China Automation Congress (CAC)

Year: 2024 | Conference Paper |

A Scheduling Method for Blast Furnace Gas Systems Based on Conditional Generative Adversarial Networks and Process Rules

Xiaoxue Wang;Feng Jin;Jun Zhao;Wei Wang;Long Chen

2024 China Automation Congress (CAC)

Year: 2024 | Conference Paper |

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Blast furnace gas (BFG), a vital byproduct energy resource in the ironmaking process, can be efficiently managed through proper scheduling to minimize emissions and augment productivity. A novel scheduling method for BFG systems based on an improved Conditional Generative Adversarial Network (CGAN) and process rules is proposed. Based on the Wasserstein generative adversarial network with gradient...Show More

A Scheduling Method for Blast Furnace Gas Systems Based on Conditional Generative Adversarial Networks and Process Rules

Xiaoxue Wang;Feng Jin;Jun Zhao;Wei Wang;Long Chen

2024 China Automation Congress (CAC)

Year: 2024 | Conference Paper |

A Control Method for Coal Slime Water Thickening Process in Coal Preparation Plants Based on BP- TSFNN

Shangluo Fu;Fan Zhou;Jun Zhao;Wei Wang

2024 China Automation Congress (CAC)

Year: 2024 | Conference Paper |

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Aiming at the complex mechanism of coal slime water thickening process in coal preparation plant, which has the characteristics of multivariable and strong nonlinearity leading to the difficulty of realizing the intelligent optimization control, a model predictive control method based on back propagation neural network ( BPNN) and Takagi-Sugeno fuzzy neural network (TSFNN) cascade (BP-TSFNN-MPC) i...Show More

A Control Method for Coal Slime Water Thickening Process in Coal Preparation Plants Based on BP- TSFNN

Shangluo Fu;Fan Zhou;Jun Zhao;Wei Wang

2024 China Automation Congress (CAC)

Year: 2024 | Conference Paper |

A Dual Autoregressive HSMM for Prediction of Consumption Gas Flow of Hot Blast Stoves in Steel Industry

Long Chen;Guanglin Cong;Jun Zhao;Wei Wang

IEEE Sensors Journal

Year: 2024 | Volume: 24, Issue: 24 | Journal Article |

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The consumption flow of blast furnace gas (BFG) by the hot blast stoves typically exhibits significant uncertain fluctuations due to the intermittent blasting, posing considerable impacts on the gas pipeline networks. Making its prediction accurate is of great importance in the scheduling of BFG systems. Therefore, viewing the uncertain stoves changing as a stochastic process, a dual autoregressiv...Show More

A Dual Autoregressive HSMM for Prediction of Consumption Gas Flow of Hot Blast Stoves in Steel Industry

Long Chen;Guanglin Cong;Jun Zhao;Wei Wang

IEEE Sensors Journal

Year: 2024 | Volume: 24, Issue: 24 | Journal Article |

An Improved Transformer U-Net-Based Reconstruction Method for Unsupervised Anomaly Detection

Jialong Tang;Zheng Lv;Ying Liu;Jun Zhao

2024 14th Asian Control Conference (ASCC)

Year: 2024 | Conference Paper |

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In this paper, we propose a new anomaly detection method U-NET-VIT (U-ViT). We embed a block composed of Vision Transformer (ViT) into U-Net structure and use it as an image reconstructor and image discriminator. In order to make ViT more suitable for the task of anomaly detection, we have made changes to ViT: we have adopted window-based multi-head Self-attention (W-MSA), which can improve the sp...Show More

An Improved Transformer U-Net-Based Reconstruction Method for Unsupervised Anomaly Detection

Jialong Tang;Zheng Lv;Ying Liu;Jun Zhao

2024 14th Asian Control Conference (ASCC)

Year: 2024 | Conference Paper |

Multi-Feature Fusion Prediction of Steelmaking by-Product Gas Based on Hierarchical Attention Actor-Critic Network

Tianhao Dong;Tianyu Wang;Jun Zhao;Wei Wang

2024 14th Asian Control Conference (ASCC)

Year: 2024 | Conference Paper |

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The effective prediction of energy from by-products in the steel industry is a prerequisite for optimizing industrial scheduling. Linz- Donawitz converter gas (LDG) is a significant secondary energy source. Due to the high production output, significant fluctuations, and diverse user groups of the LDG system, accurately predicting LDG generation flow presents considerable challenges. To address th...Show More

Multi-Feature Fusion Prediction of Steelmaking by-Product Gas Based on Hierarchical Attention Actor-Critic Network

Tianhao Dong;Tianyu Wang;Jun Zhao;Wei Wang

2024 14th Asian Control Conference (ASCC)

Year: 2024 | Conference Paper |

Active Fault Diagnosis for Gas-Steam Combined Cycle Power Plant Considering Variable Working Conditions

Yang Han;Demin Xu;Fan Zhou;Ying Liu;Jun Zhao;Wei Wang

2024 14th Asian Control Conference (ASCC)

Year: 2024 | Conference Paper |

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The gas-steam combined cycle power plant (CCPP) is one of the important energy conversion equipment in iron and steel energy systems., which can utilize the by-product gas to supply power in industrial parks. Due to the complexity of the environment in which the equipment is located., the components of the equipment., including actuators., compressors and combustion chambers., are prone to fault. ...Show More

Active Fault Diagnosis for Gas-Steam Combined Cycle Power Plant Considering Variable Working Conditions

Yang Han;Demin Xu;Fan Zhou;Ying Liu;Jun Zhao;Wei Wang

2024 14th Asian Control Conference (ASCC)

Year: 2024 | Conference Paper |

Distributed Physics-Informed Neural Networks for Temperature Field Reconstruction in the Regenerator of Hot Blast Stove Considering Parametric Spatial-Temporal Dynamics

Jiurui Han;Wange Li;Chao Wang;Jun Zhao;Wei Wang

2024 14th Asian Control Conference (ASCC)

Year: 2024 | Conference Paper |

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Hot blast stove as an important heating equipment in the iron and steel production process, reconstructing its regenerator temperature field is of great significance to provide guidance of structural parameter design. In this paper, a distributed physics-informed neural network (DPINN) method considering spatial-temporal dynamics of physical parameters is proposed to reconstruct the two-dimensiona...Show More

Distributed Physics-Informed Neural Networks for Temperature Field Reconstruction in the Regenerator of Hot Blast Stove Considering Parametric Spatial-Temporal Dynamics

Jiurui Han;Wange Li;Chao Wang;Jun Zhao;Wei Wang

2024 14th Asian Control Conference (ASCC)

Year: 2024 | Conference Paper |

Operational Optimization Based on Model Adaptation for Compressed Air System

Xiangze Wang;Yang Liu;Lulu Zhong;Jun Zhao;Wei Wang

2024 14th Asian Control Conference (ASCC)

Year: 2024 | Conference Paper |

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With the energy problem becoming more and more prominent, high energy consumption of the compressed air system (CAS) is widely concerned. Therefore, it is of great significance to reduce its energy waste and power consumption for energy saving and emission reduction. In this study, an online optimization method for CAS with the identification model and output modifier is proposed. Firstly, a new o...Show More

Operational Optimization Based on Model Adaptation for Compressed Air System

Xiangze Wang;Yang Liu;Lulu Zhong;Jun Zhao;Wei Wang

2024 14th Asian Control Conference (ASCC)

Year: 2024 | Conference Paper |

Event-Triggered Prediction of Blast Furnace Gas Generation Based on a Trend Self-Adaption Scheme

Guanglin Cong;Long Chen;Jun Zhao;Feng Jin;Wei Wang

2024 14th Asian Control Conference (ASCC)

Year: 2024 | Conference Paper |

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The prediction of blast furnace gas (BFG) generation in ironmaking processes is crucial for the BFG scheduling work. Due to frequent switching of working conditions of the ironmaking process, the generation of BFG under each working condition fluctuates greatly. It is difficult to predict accurately during the transition of working conditions. In order to address these problems, this paper propose...Show More

Event-Triggered Prediction of Blast Furnace Gas Generation Based on a Trend Self-Adaption Scheme

Guanglin Cong;Long Chen;Jun Zhao;Feng Jin;Wei Wang

2024 14th Asian Control Conference (ASCC)

Year: 2024 | Conference Paper |

Gas Holder Levels Prediction with Multi-Working Conditions Identified by Change Point Detection

Zihe Yang;Long Chen;Jun Zhao;Wei Wang

2024 14th Asian Control Conference (ASCC)

Year: 2024 | Conference Paper |

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The operation of gas holders in steel industry presents complex characteristics due to various factors such as ambient temperature and piston tilt, leading to multiple working conditions. To address this, the paper proposes a gas holder levels prediction method based on time series change point detection, where the real-time data are identified to find the shifts in working conditions and thereby ...Show More

Gas Holder Levels Prediction with Multi-Working Conditions Identified by Change Point Detection

Zihe Yang;Long Chen;Jun Zhao;Wei Wang

2024 14th Asian Control Conference (ASCC)

Year: 2024 | Conference Paper |

Interval Predictive Controller with Disturbance Observer for Gas Turbine

Han Zhang;Qinggan Wei;Yang Liu;Jun Zhao;Wei Wang

2024 14th Asian Control Conference (ASCC)

Year: 2024 | Conference Paper |

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Gas turbines, crucial to the Combined Cycle Gas Turbine (CCGT), provide exceptional load-following capabilities and low emissions, which are key for enhancing the power system's capacity for deep peaking and rapid frequency regu-lation. Such advancements are vital for integrating renewable energy sources and optimizing national energy structures. As the power market increasingly focuses on stabili...Show More

Interval Predictive Controller with Disturbance Observer for Gas Turbine

Han Zhang;Qinggan Wei;Yang Liu;Jun Zhao;Wei Wang

2024 14th Asian Control Conference (ASCC)

Year: 2024 | Conference Paper |

Unbalanced few-shot classification based on ME-MAML framework

Zherun Liao;Zheng Lv;Qiao Sun;Jun Zhao

2024 43rd Chinese Control Conference (CCC)

Year: 2024 | Conference Paper |

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Aiming at the few-shot problem of lack of sufficient samples and uneven samples, which is common in medical image classification tasks, this paper proposes a meta-learning framework ME-MAML (Medical ModelAgnostic Meta-Learning) for few-shot medical image classification tasks. The framework aims to train a set of parameters with good generalization performance for the multi-class medical image clas...Show More

Unbalanced few-shot classification based on ME-MAML framework

Zherun Liao;Zheng Lv;Qiao Sun;Jun Zhao

2024 43rd Chinese Control Conference (CCC)

Year: 2024 | Conference Paper |

An Improved-knowledge-distillation Based Method for Working Condition Recognition of Hot Rolling Heating Furnace in Steel Industry

Xiang Long Meng;Feng Jin;Jun Zhao;Wei Wang

2024 International Joint Conference on Neural Networks (IJCNN)

Year: 2024 | Conference Paper |

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Hot rolling is a crucial production process in the steel industry, with the heating furnace serving as the primary unit of energy consumption. Accurately recognizing the conditions of the gas combustion inside the furnace is profoundly meaningful to support the optimal scheduling of the energy resources. In this paper, a recognition method for working conditions inside the heating furnace based on...Show More

An Improved-knowledge-distillation Based Method for Working Condition Recognition of Hot Rolling Heating Furnace in Steel Industry

Xiang Long Meng;Feng Jin;Jun Zhao;Wei Wang

2024 International Joint Conference on Neural Networks (IJCNN)

Year: 2024 | Conference Paper |

Ingredient Planning for Copper Industry: A Deep Reinforcement Learning-Based $\epsilon$-Constrained Multi-Objective Optimization Framework

Xuerui Zhang;Zhongyang Han;Zhiyuan Wang;Jun Zhao;Wei Wang

2024 IEEE Congress on Evolutionary Computation (CEC)

Year: 2024 | Conference Paper |

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Effective ingredient plan plays a crucial role in the copper industry as it directly impacts both economic interest and resource utilization of the enterprise. Aims at minimizing both production cost and loss simultaneously, a multi-objective ingredient optimization model considering practical constraints is established in this study. In order to effectively address the model, a deep reinforcement...Show More

Ingredient Planning for Copper Industry: A Deep Reinforcement Learning-Based $\epsilon$-Constrained Multi-Objective Optimization Framework

Xuerui Zhang;Zhongyang Han;Zhiyuan Wang;Jun Zhao;Wei Wang

2024 IEEE Congress on Evolutionary Computation (CEC)

Year: 2024 | Conference Paper |

Reinforcement Learning based parameter optimization of cascade control loop in the Coke Dry Quenching System

Xiyu Quan;Yang Liu;Jie Jin;Jun Zhao;Wei Wang

2024 36th Chinese Control and Decision Conference (CCDC)

Year: 2024 | Conference Paper |

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Waste heat boiler is an important part of dry quenching power generation, and its steam drum level cascade three impulse system is characterized by strong perturbation, non-linearity, strong coupling and multiple working conditions. In this paper, a reinforcement learning based parameter optimization of cascade control loop in the coke dry quenching system is proposed for such series control probl...Show More

Reinforcement Learning based parameter optimization of cascade control loop in the Coke Dry Quenching System

Xiyu Quan;Yang Liu;Jie Jin;Jun Zhao;Wei Wang

2024 36th Chinese Control and Decision Conference (CCDC)

Year: 2024 | Conference Paper |

Multi-condition modeling and optimal control of boiler based on improved FCM clustering algorithm

Heping Xia;Xinyu Zhang;Zhongyang Han;Jun Zhao;Wei Wang

2024 36th Chinese Control and Decision Conference (CCDC)

Year: 2024 | Conference Paper |

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Boiler is the main equipment of coal-fired power plant, which has complex and variable characteristics of multiple operating conditions due to different loads in daily production. The overall operational efficiency of the plant is directly affected by its control level, especially for the accommodation of the different operating conditions. In this paper, the improved fuzzy C-mean clustering algor...Show More

Multi-condition modeling and optimal control of boiler based on improved FCM clustering algorithm

Heping Xia;Xinyu Zhang;Zhongyang Han;Jun Zhao;Wei Wang

2024 36th Chinese Control and Decision Conference (CCDC)

Year: 2024 | Conference Paper |

Interval Type-2 Fuzzy Deep Reinforcement Learning-Based Operational Optimization of Industrial Aerodynamic System

Lulu Zhong;Yang Liu;Linqing Wang;Jun Zhao;Wei Wang

IEEE Transactions on Instrumentation and Measurement

Year: 2024 | Volume: 73 | Journal Article |

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Industrial aerodynamic system (IAS) provides the high-pressure air for consumer users, which is the primary source of power in the industrial park. Thus, their accurate measurement balance state and optimal scheduling are of paramount significance for reducing operational costs and enhancing operational efficiency. In order to solve this problem, an approach known as interval type-2 (IT2) fuzzy de...Show More

Interval Type-2 Fuzzy Deep Reinforcement Learning-Based Operational Optimization of Industrial Aerodynamic System

Lulu Zhong;Yang Liu;Linqing Wang;Jun Zhao;Wei Wang

IEEE Transactions on Instrumentation and Measurement

Year: 2024 | Volume: 73 | Journal Article |

A Multi-Stage Differential-Multifactorial Evolutionary Algorithm for Ingredient Optimization in the Copper Industry

Xuerui Zhang;Zhongyang Han;Jun Zhao

IEEE/CAA Journal of Automatica Sinica

Year: 2024 | Volume: 11, Issue: 10 | Journal Article |

Cited by: Papers (1)

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Ingredient optimization plays a pivotal role in the copper industry, for which it is closely related to the concentrate utilization rate, stability of furnace conditions, and the quality of copper production. To acquire a practical ingredient plan, which should exhibit long duration time with sufficient utilization and feeding stability for real applications, an ingredient plan optimization model ...Show More

A Multi-Stage Differential-Multifactorial Evolutionary Algorithm for Ingredient Optimization in the Copper Industry

Xuerui Zhang;Zhongyang Han;Jun Zhao

IEEE/CAA Journal of Automatica Sinica

Year: 2024 | Volume: 11, Issue: 10 | Journal Article |

Graph-Frequency Domain Kalman Filtering for Industrial Pipe Networks Subject to Measurement Outliers

Lei Su;Zhongyang Han;Jun Zhao;Wei Wang

IEEE Transactions on Industrial Informatics

Year: 2024 | Volume: 20, Issue: 5 | Journal Article |

Cited by: Papers (1)

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This article is concerned with the outlier-resistant state estimation problem for industrial pipe networks (PNs). PN signals, e.g., pressure and temperature, are modeled as low-pass time-varying graph signals, and an outlier-resistant graph-frequency domain (GFD) filter is proposed. This article extends the innovation saturation (IS) mechanism from the node domain to the GFD. A GFD IS function is ...Show More

Graph-Frequency Domain Kalman Filtering for Industrial Pipe Networks Subject to Measurement Outliers

Lei Su;Zhongyang Han;Jun Zhao;Wei Wang

IEEE Transactions on Industrial Informatics

Year: 2024 | Volume: 20, Issue: 5 | Journal Article |

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