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学者姓名：乔俊飞
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摘要 ：
This article addresses fault-tolerant event-triggered sampled-data (SD) fuzzy control for nonlinear delayed parabolic partial differential equation (PDE) systems under spatially point measurements (SPMs). First, a Takagi-Sugeno fuzzy delayed PDE model is presented to accurately describe the nonlinear delayed parabolic PDE systems. Second, a fault-tolerant event-triggered SD fuzzy control strategy under SPMs is designed to cope with Markov jump faults occurring in actuators, which can effectively reduce the unnecessary data transmission and be achieved by finite sensors and actuators. The membership functions of the proposed controller are determined by the measurement output and independent of the fuzzy delayed PDE plant model. Then, by constructing a Lyapunov functional, sufficient conditions that guarantee the stochastically exponential stability of closed-loop nonlinear delayed parabolic PDE systems are obtained based on linear matrix inequalities. Finally, two examples are given to illustrate the designed approach.
关键词 ：
spatially point measurements (SPMs) spatially point measurements (SPMs) Markov jump actuator failures Markov jump actuator failures Fault-tolerant event-triggered sampled-data (SD) fuzzy control Fault-tolerant event-triggered sampled-data (SD) fuzzy control nonlinear delayed parabolic partial differential equation (PDE) system nonlinear delayed parabolic partial differential equation (PDE) system
引用：
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| GB/T 7714 | Chen, Bo-Ming , Wang, Zi-Peng , Zhao, Feng-Liang et al. Fault-Tolerant Event-Triggered Sampled-Data Fuzzy Control for Nonlinear Delayed Parabolic PDE Systems [J]. | IEEE TRANSACTIONS ON FUZZY SYSTEMS , 2024 , 32 (11) : 6460-6471 . |
| MLA | Chen, Bo-Ming et al. "Fault-Tolerant Event-Triggered Sampled-Data Fuzzy Control for Nonlinear Delayed Parabolic PDE Systems" . | IEEE TRANSACTIONS ON FUZZY SYSTEMS 32 . 11 (2024) : 6460-6471 . |
| APA | Chen, Bo-Ming , Wang, Zi-Peng , Zhao, Feng-Liang , Qiao, Junfei , Wu, Huai-Ning , Huang, Tingwen . Fault-Tolerant Event-Triggered Sampled-Data Fuzzy Control for Nonlinear Delayed Parabolic PDE Systems . | IEEE TRANSACTIONS ON FUZZY SYSTEMS , 2024 , 32 (11) , 6460-6471 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Under intermittent noncollocated boundary measurement (BM), this article introduces a dynamic intermittent boundary output-feedback control for reaction-diffusion systems (RDSs). Since the system state is not fully available and intermittent noncollocated BM makes the intermittent BC design very difficult, an observer-based control technique is given to surmount this design difficulty. Initially, a PDE state observer under intermittent noncollocated BM is provided to estimate the RDS state. Then, the exponential stability of closed-loop RDSs is ensured by constructing an observer-based controller. Sufficient conditions of such dynamic controller are subsequently presented by linear matrix inequalities (LMIs) via employing a switching time-dependent LF and inequality techniques. Finally, two numerical examples are presented to demonstrate the effectiveness of the proposed design approach.
关键词 ：
reaction-diffusion systems (RDSs) reaction-diffusion systems (RDSs) Dynamic intermittent boundary control (DIBC) Dynamic intermittent boundary control (DIBC) intermittent noncollocated boundary measurement (BM) intermittent noncollocated boundary measurement (BM)
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| GB/T 7714 | Wang, Zi-Peng , Zhao, Feng-Liang , Qiao, Junfei et al. Dynamic Intermittent Boundary Control for Reaction-Diffusion Systems Under Intermittent Noncollocated Boundary Measurement [J]. | IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS , 2024 . |
| MLA | Wang, Zi-Peng et al. "Dynamic Intermittent Boundary Control for Reaction-Diffusion Systems Under Intermittent Noncollocated Boundary Measurement" . | IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS (2024) . |
| APA | Wang, Zi-Peng , Zhao, Feng-Liang , Qiao, Junfei , Wu, Huai-Ning , Huang, Tingwen . Dynamic Intermittent Boundary Control for Reaction-Diffusion Systems Under Intermittent Noncollocated Boundary Measurement . | IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS , 2024 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
A mixed fuzzy intermittent control method based on boundary control under boundary measurement and distributed control under spatial local averaged measurements (SLAMs) is introduced for nonlinear ordinary differential equations (ODE)-partial differential equations(PDE) coupled systems in this article. To accurately characterize the nonlinear ODE-PDE coupled systems, a Takagi-Sugeno fuzzy model is first employed. Then, based on the fuzzy model, the switched Lyapunov function is proposed to design the mixed fuzzy intermittent controller under boundary measurement and SLAMs. Sufficient conditions on stability for the closed-loop coupled system are obtained via a set of space dependent linear matrix inequalities. The simulation results ultimately confirm the effectiveness of the proposed design approach in controlling hypersonic rocket car.
关键词 ：
space- dependent linear matrix inequalities (SDLMIs) space- dependent linear matrix inequalities (SDLMIs) Hypersonic rocket car (HRC) Hypersonic rocket car (HRC) mixed fuzzy intermittent control mixed fuzzy intermittent control nonlinear ordinary differential equations- partial differential equations (ODE-PDE) coupled systems nonlinear ordinary differential equations- partial differential equations (ODE-PDE) coupled systems
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| GB/T 7714 | Wang, Zi-Peng , Su, Hua-Ran , Shi, Xi-Dong et al. Mixed Fuzzy Intermittent Control for Nonlinear ODE-PDE Coupled Systems [J]. | IEEE TRANSACTIONS ON FUZZY SYSTEMS , 2024 , 32 (12) : 6658-6670 . |
| MLA | Wang, Zi-Peng et al. "Mixed Fuzzy Intermittent Control for Nonlinear ODE-PDE Coupled Systems" . | IEEE TRANSACTIONS ON FUZZY SYSTEMS 32 . 12 (2024) : 6658-6670 . |
| APA | Wang, Zi-Peng , Su, Hua-Ran , Shi, Xi-Dong , Qiao, Junfei , Wu, Huai-Ning , Huang, Tingwen et al. Mixed Fuzzy Intermittent Control for Nonlinear ODE-PDE Coupled Systems . | IEEE TRANSACTIONS ON FUZZY SYSTEMS , 2024 , 32 (12) , 6658-6670 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Transfer learning can handle the domain adaptation of different feature spaces in nonlinear systems. Most existing studies only focus on common features between heterogeneous scenes rather than specific features that account for latent similarities between them. To deal with this problem, a filter transfer learning algorithm for nonlinear systems modeling with heterogeneous features is proposed. First, nonlinear mapping is constructed to learn the potential relationships between different feature attributes, including common features and specific features. Then, source knowledge from different domains can be obtained in the form of process parameters according to the mapping relationship. Second, a hierarchical filter framework is presented to reconstruct source knowledge in different transfer phases. In the pre-transfer phase, a knowledge filter is designed to increase the diversity of knowledge through selection, crossover, and mutation operations. In the post-transfer phase, a guided filter is established to achieve a coupling balance between source knowledge and target domain by using target samples as guidance information. Third, a dynamic parameter learning strategy is given to promote the learning performance of heterogeneous tasks. Finally, the effectiveness of innovations and the superiority of this proposed algorithm are verified by the experimental results in nonlinear systems with heterogeneous features.
关键词 ：
Nonlinear mapping Nonlinear mapping Transfer learning Transfer learning Heterogeneous features Heterogeneous features Hierarchical filter framework Hierarchical filter framework
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| GB/T 7714 | Han, Honggui , Li, Mengmeng , Wu, Xiaolong et al. Filter transfer learning algorithm for nonlinear systems modeling with heterogeneous features [J]. | EXPERT SYSTEMS WITH APPLICATIONS , 2024 , 260 . |
| MLA | Han, Honggui et al. "Filter transfer learning algorithm for nonlinear systems modeling with heterogeneous features" . | EXPERT SYSTEMS WITH APPLICATIONS 260 (2024) . |
| APA | Han, Honggui , Li, Mengmeng , Wu, Xiaolong , Yang, Hongyan , Qiao, Junfei . Filter transfer learning algorithm for nonlinear systems modeling with heterogeneous features . | EXPERT SYSTEMS WITH APPLICATIONS , 2024 , 260 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
This paper introduces a fuzzy intermittent control issue for nonlinear PDE-ODE coupled system under spatially point measurements (SPMs), which can be represented by an ordinary differential equation (ODE) and a partial differential equation (PDE). Firstly, the nonlinear coupled system is aptly characterized by the Takagi-Sugeno (T-S) fuzzy PDE-ODE coupled model. Subsequently, based on T-S fuzzy model, a novel Lyapunov function (LF) is provided to design a fuzzy intermittent controller ensuring exponential stability of the closed-loop coupled system. The stabilization conditions are presented by means of a group of space-dependent linear matrix inequalities (SDLMIs). Finally, simulation results are given to illustrate the effectiveness of the proposed design method in the control of a hypersonic rocket car (HRC).
关键词 ：
Fuzzy intermittent controller Fuzzy intermittent controller Nonlinear PDE-ODE coupled systems Nonlinear PDE-ODE coupled systems Spatially point measurements (SPMs) Spatially point measurements (SPMs) Hypersonic rocket car (HRC) Hypersonic rocket car (HRC)
引用：
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| GB/T 7714 | Shi, Xi-Dong , Wang, Zi-Peng , Qiao, Junfei et al. Fuzzy Intermittent Control for Nonlinear PDE-ODE Coupled Systems [J]. | INTERNATIONAL JOURNAL OF FUZZY SYSTEMS , 2024 , 26 (8) : 2585-2601 . |
| MLA | Shi, Xi-Dong et al. "Fuzzy Intermittent Control for Nonlinear PDE-ODE Coupled Systems" . | INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 26 . 8 (2024) : 2585-2601 . |
| APA | Shi, Xi-Dong , Wang, Zi-Peng , Qiao, Junfei , Wu, Huai-Ning , Zhang, Xiao-Wei , Yan, Xue-Hua . Fuzzy Intermittent Control for Nonlinear PDE-ODE Coupled Systems . | INTERNATIONAL JOURNAL OF FUZZY SYSTEMS , 2024 , 26 (8) , 2585-2601 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Broad learning system based on neural network (BLS-NN) has poor efficiency for small data modeling with various dimensions. Tree-based BLS (TBLS) is designed for small data modeling by introducing nondifferentiable modules and an ensemble strategy to the traditional broad learning system (BLS). TBLS replaces the neurons of BLS with the tree modules to map the input data. Moreover, we present three new TBLS variant methods and their incremental learning implementations, which are motivated by deep, broad, and ensemble learning. Their major distinction is reflected in the incremental learning strategies based on: 1) mean square error (mse); 2) pseudo-inverse; and 3) pseudo-inverse theory and stack representation. Therefore, this study further explores the domain of BLS based on the nondifferentiable modules. The simulations are compared with some state-of-the-art (SOTA) BLS-NN and tree methods under high-, medium-, and low-dimensional benchmark datasets. Results show that the proposed method outperforms the BLS-NN, and the modeling accuracy is remarkably improved with the small training data of the proposed TBLS.
关键词 ：
BLS-based on neural network (BLS-NN) BLS-based on neural network (BLS-NN) small data modeling small data modeling broad learning system (BLS) broad learning system (BLS) tree BLS (TBLS) tree BLS (TBLS)
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| GB/T 7714 | Xia, Heng , Tang, Jian , Yu, Wen et al. Tree Broad Learning System for Small Data Modeling [J]. | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS , 2024 , 35 (7) : 8909-8923 . |
| MLA | Xia, Heng et al. "Tree Broad Learning System for Small Data Modeling" . | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 35 . 7 (2024) : 8909-8923 . |
| APA | Xia, Heng , Tang, Jian , Yu, Wen , Qiao, Junfei . Tree Broad Learning System for Small Data Modeling . | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS , 2024 , 35 (7) , 8909-8923 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Owing to high complexity and time-variant operation, as well as increasingly requirements for water quality, stability, and reliability, the wastewater treatment process (WWTP) is regarded as an adaptive control problem. In this study, a data-driven adaptive control with deep learning (DRAC-DL) is developed to improve the operational performance of the WWTP. First, a feedback controller is designed to construct the closed-loop control scheme. Second, an adaptive deep belief network (ADBN), based on the data-driven self-incremental learning strategy, is proposed to approximate the ideal control law. Third, the stability of the DRAC-DL scheme is analyzed in detail. The main advantage of DRAC-DL lies in its improved robustness and efficiency, which benefit from the Lyapunov-based closed-loop strategy and the efficient ADBN controller. Finally, the feasibility and applicability of DRAC-DL are verified by two parts: 1) simulation on the nonlinear system and 2) application to the WWTP on the benchmark simulation model No.1. The experimental results show the applicability and effectiveness, among which DRAC-DL reduces the output fluctuation (variance) by no less than 82% and realizes the better stability and robustness.
关键词 ：
stability analysis stability analysis wastewater treatment process (WWTP) wastewater treatment process (WWTP) Adaptive control Adaptive control adaptive deep belief network (ADBN) adaptive deep belief network (ADBN)
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| GB/T 7714 | Wang, Gongming , Zhao, Yidi , Liu, Caixia et al. Data-Driven Robust Adaptive Control With Deep Learning for Wastewater Treatment Process [J]. | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS , 2024 , 20 (1) : 149-157 . |
| MLA | Wang, Gongming et al. "Data-Driven Robust Adaptive Control With Deep Learning for Wastewater Treatment Process" . | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 20 . 1 (2024) : 149-157 . |
| APA | Wang, Gongming , Zhao, Yidi , Liu, Caixia , Qiao, Junfei . Data-Driven Robust Adaptive Control With Deep Learning for Wastewater Treatment Process . | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS , 2024 , 20 (1) , 149-157 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
The real-time detection technique and comprehensive characterization of dioxin (DXN) emission concentration during the municipal solid waste incineration process persist as unresolved challenges. Prevailing research predominantly relies on data-driven models, often overlooking the potential benefits derived from fusing combustion mechanism knowledge. To confront this issue, we propose a hybrid modeling strategy that fuses a simulator-based mechanism model with an enhanced regression decision tree-based data model. This approach aims to predict DXN emission concentrations while accommodating diverse time-scaled measurement requirements. Based on virtual mechanism data obtained via numerical simulation models coupling FLIC and Aspen Plus, we constructed a white-box surrogate model utilizing a multiple-input multiple-output linear regression decision tree (LRDT). To establish a relationship with DXN emission concentration, we employed a semisupervised transfer learning mapping model. It was then fused with a novel ensemble LRDT model based on real historical data by using a constrained incremental random weight neural network. The efficacy of this modeling strategy was validated through an industrial application case study conducted in Beijing.
关键词 ：
Numerical models Numerical models municipal solid waste incineration (MSWI) municipal solid waste incineration (MSWI) MIMO communication MIMO communication Data models Data models Mathematical models Mathematical models multidemand modeling multidemand modeling Predictive models Predictive models Solid modeling Solid modeling Combustion Combustion linear regression decision tree (LRDT) linear regression decision tree (LRDT) mechanism-driven (MD) and data-driven (DD) mechanism-driven (MD) and data-driven (DD) numerical simulation numerical simulation Dioxin (DXN) Dioxin (DXN) semisupervised transfer learning semisupervised transfer learning
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| GB/T 7714 | Xia, Heng , Tang, Jian , Yu, Wen et al. Hybrid Simulator-Based Mechanism and Data-Driven for Multidemand Dioxin Emissions Intelligent Prediction in the MSWI Process [J]. | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS , 2024 . |
| MLA | Xia, Heng et al. "Hybrid Simulator-Based Mechanism and Data-Driven for Multidemand Dioxin Emissions Intelligent Prediction in the MSWI Process" . | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2024) . |
| APA | Xia, Heng , Tang, Jian , Yu, Wen , Qiao, Junfei . Hybrid Simulator-Based Mechanism and Data-Driven for Multidemand Dioxin Emissions Intelligent Prediction in the MSWI Process . | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS , 2024 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Dioxin (DXN) is a persistent organic pollutant produced from municipal solid waste incineration (MSWI) processes. It is a crucial environmental indicator to minimize emission concentration by using optimization control, but it is difficult to monitor in real time. Aiming at online soft-sensing of DXN emission, a novel fuzzy tree broad learning system (FTBLS) is proposed, which includes offline training and online measurement. In the offline training part, weighted k-means is presented to construct a typical sample pool for reduced learning costs of offline and online phases. Moreover, the novel FTBLS, which contains a feature mapping layer, enhance layer, and increment layer, by replacing the fuzzy decision tree with neurons applied to construct the offline model. In the online measurement part, recursive principal component analysis is used to monitor the time-varying characteristic of the MSWI process. To measure DXN emission, offline FTBLS is reused for normal samples; for drift samples, fast incremental learning is used for online updates. A DXN data from the actual MSWI process is employed to prove the usefulness of FTBLS, where the RMSE of training and testing data are 0.0099 and 0.0216, respectively. This result shows that FTBLS can effectively realize DXN online prediction.
关键词 ：
Decision trees Decision trees municipal solid waste incineration (MSWI) municipal solid waste incineration (MSWI) online soft-sensing online soft-sensing time-varying time-varying Training Training Pollution measurement Pollution measurement Dioxin (DXN) Dioxin (DXN) Frequency modulation Frequency modulation Principal component analysis Principal component analysis fuzzy tree broad learning system (FTBLS) fuzzy tree broad learning system (FTBLS) Data models Data models Monitoring Monitoring
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| GB/T 7714 | Xia, Heng , Tang, Jian , Yu, Wen et al. Online Measurement of Dioxin Emission in Solid Waste Incineration Using Fuzzy Broad Learning [J]. | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS , 2024 , 20 (1) : 358-368 . |
| MLA | Xia, Heng et al. "Online Measurement of Dioxin Emission in Solid Waste Incineration Using Fuzzy Broad Learning" . | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 20 . 1 (2024) : 358-368 . |
| APA | Xia, Heng , Tang, Jian , Yu, Wen , Qiao, Junfei . Online Measurement of Dioxin Emission in Solid Waste Incineration Using Fuzzy Broad Learning . | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS , 2024 , 20 (1) , 358-368 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
The rise of edge intelligence is driving a shift in the focus of complexity computing to the edge. Due to network and communication constraints, traditional edge computing resource scheduling solutions for industrial Internet of Thing (IIoT) usually face many challenges. For example, delayed decision release, unreasonable policy scheduling and under-utilization of resources. These problems hinder the further construction and advancement of intelligent IIoT. In order to solve these problems, this paper proposes an edge computing resource scheduling scheme based on collective learning. The process of model training is formulated as a Markovian decision process (MDP). The scheme enables edge nodes to exchange learning experiences of resource scheduling schemes, through a shared ledger on the blockchain, including parameters for initial model training. The updated policy scheduling scheme is then obtained through a collective deep reinforcement learning (CDRL) algorithm. Also, to reduce the transmission burden of the underlying industrial devices, we benefit ambient backscatter communication (AmBC) to improve the power utilization of battery. Simulation results display our proposed scheme can reduce energy consumption significantly, while decreased approximately 12.6% compare to A3C algorithm.
关键词 ：
collective deep reinforcement learning (CDRL) collective deep reinforcement learning (CDRL) blockchain blockchain ambient backscatter communication (AmBC) ambient backscatter communication (AmBC) industrial Internet of Things (IIoT) industrial Internet of Things (IIoT) Mobile edge computing (MEC) Mobile edge computing (MEC)
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| GB/T 7714 | Huang, Yudian , Li, Meng , Yu, F. Richard et al. Resources Scheduling for Ambient Backscatter Communication-Based Intelligent IIoT: A Collective Deep Reinforcement Learning Method [J]. | IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING , 2024 , 10 (2) : 634-648 . |
| MLA | Huang, Yudian et al. "Resources Scheduling for Ambient Backscatter Communication-Based Intelligent IIoT: A Collective Deep Reinforcement Learning Method" . | IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 10 . 2 (2024) : 634-648 . |
| APA | Huang, Yudian , Li, Meng , Yu, F. Richard , Si, Pengbo , Zhang, Haijun , Qiao, Junfei . Resources Scheduling for Ambient Backscatter Communication-Based Intelligent IIoT: A Collective Deep Reinforcement Learning Method . | IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING , 2024 , 10 (2) , 634-648 . |
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