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第34届中国控制与决策会议

作者： Di Lu Fuli Wang Shu Wang Kang Li Tao Liang College of Information Science and Engineering Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Northeastern University

ISBN: (纸本)9781665478977

The overflow identification of flotation process is important to ensure the flotation quality and improve the comprehensive economic efficiency of the ***,direct monitoring of flotation froth overflow is difficult due to the strong nonlinearity,information redundancy and uncertainty of industrial *** solve the above problems,a two-branch convolutional neural network model(MSI-two-branch CNN) based on multi-source information is *** merging multiple network branches,the features of industrial processes can be extracted *** addition,the one-dimensional time series are transformed using Gram angular domain(GAF) to generate two-dimensional images,which not only can introduce process variables into the overflow recognition model,but also can improve the recognition efficiency by using the correlation between ***,the method was applied to the gold flotation *** experimental results show that MSI-two-branch CNN can perform overflow recognition with high accuracy and efficiency in a practical industrial context.

关键词： Convolutional neural network Data fusion Gramian angular fields Flotation industrial process Feature extraction Image analysis

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Performance Verification of Smith Predictor Control Using IMC Scheme via Chemical Reaction Networks and DNA Strand Displacement Reaction

Performance Verification of Smith Predictor Control Using IM...

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Smart World Congress (SWC), IEEE

作者： Jingwang Yao Hui Lv Yijun Xiao Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China

Biochemical process control is often achieved through feedback control strategies for theoretical analysis and optimization of biochemical reaction processes. Here, the design improves the robustness of the biochemical process by taking advantage of the Smith prediction structure and internal mode control within the chemical reaction network theory framework to intervene against the time delays and perturbations present in the biochemical process. First, an IMC representation of Smith prediction is constructed using the presence of parameter rectification of the control method, and then an IMC-MSP control scheme is proposed. A first-order time-lag controlled object is established in the construction of the IMC-MSP control scheme. Besides, CRNs and kinetic representations are established based on the control structure to facilitate the evaluation of the control system performance. In this paper, CRNs representations of parabolic functions as signal inputs are defined for the first time and optimized for stepping signals to facilitate the design and study of biomolecular circuits. Notably, for the CRNs-based IMC-MSP control scheme, on the one hand, numerical simulation experiments are designed to verify the effectiveness of the whole scheme from multiple perspectives of time delays, external perturbations, and filter tuning parameters; on the other hand, using DNA strand displacement is reflected to verify the feasibility of the whole scheme.

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Event-triggered output regulation bumpless transfer control for discrete-time switched systems under DoS

Event-triggered output regulation bumpless transfer control ...

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第35届中国控制与决策会议

作者： Hanqing Qu Siyuan Zhang Jun Zhao State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University) Ministry of Education College of Information Science and Engineering Northeastern University

This paper studies the output regulation bumpless transfer control for event-triggered discrete-time switched systems under denial-of-service(DoS) *** switched systems subject to DoS,both the sensor signal and switching signal may be *** satisfy the bumpless transfer(BT) performance,we cannot use the common input zeroing *** problem that the triggering error being attacked may not follow the triggering condition ***,the controller mode may be asynchronous with the plant *** with the above two problems,we design an event-triggered dynamic error feedback controller and a switching law to guarantee that the output regulation problem for switched systems is solvable,and the BT performance can be ***,corresponding sufficient conditions are proposed in the form of linear matrix inequalities(LMIs).Finally,a numerical example is given to illustrate the validity of our results.

关键词： Discrete-time switched systems output regulation bumpless transfer control event-triggering transmission DoS attacks

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A Motion Intent Recognition Method Based on Dynamic Feature Fusion and Multi-Stream TCN

A Motion Intent Recognition Method Based on Dynamic Feature ...

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International Conference on Industrial Artificial Intelligence (IAI)

作者： Jianfeng Chen Xing Li Fujie Wang Yuxuan Zhu School of Electrical Engineering & Intelligentization Dongguan University of Technology Dongguan Guangdong China The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang Liaoning China School of Information Science and Engineering Northeastern University Shenyang Liaoning China

ISBN: (数字)9798350356618

ISBN: (纸本)9798350356625

Clinical motor relearning theory emphasizes the active participation of patients, so the recognition of patient's motor imagery plays a key role in robot-assisted rehabilitation exercise training. We constructed an upper limb rehabilitation sEMG (Surface Electromyography) dataset by capturing 18 upper limb rehabilitation movements using a wireless sEMG acquisition system and proposed a deep learning model based on dynamic feature fusion with a multi-stream Temporal Convolutional Network (MS-TCN), which identified the patient's motor intention from the captured sEMG by end-to-end learning. On the one hand, the method can extract the temporal features of single-channel signals through the multi-stream TCN structure, and on the other hand, dynamic feature fusion can effectively utilize the primary-secondary relationship of multiple muscles in completing different motions, achieving high recognition accuracy and low response time. Through simulation analysis and experimental testing, the results show that this method can achieve higher classification accuracy than mainstream motor intention recognition algorithms on different subjects, and the effective use of this method will promote the recovery of patient's limb function and significantly improve the efficiency of rehabilitation training.

关键词： Training Wireless communication Accuracy Dynamics Motors Feature extraction Time factors Robots Testing Periodic structures

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Modeling of Rate-Dependent Butterfly Hysteresis in Dielectric Elastomer Actuators

Modeling of Rate-Dependent Butterfly Hysteresis in Dielectri...

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IEEE International Conference on Control Science and Systems engineering (CCSSE)

作者： Zenghong Duan Hongzhi Xu Xiuyu Zhang Zhi Li State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China School of Automation Engineering Northeast Electric Power University Jilin China

The rate-dependent butterfly hysteretic nonlinearity deteriorates the positioning performance of smart material-based actuators, such as dielectric elastomer actuators (DEAs). In comparison with modeling of the single loop hysteresis effect, the rate-dependent butterfly hysteresis is more complicated to represent. In this paper, a rate-dependent butterfly hysteresis structure is proposed for describing the rate-dependent butterfly hysteresis behavior in DEAs. The structure is composed of a rate-dependent butterfly operators and neural networks for weight prediction and rate-dependent hysteresis. In particular, the rate-dependent hysteresis operator is constructed by considering the rate change of the input to the model. Two neural networks are used for predicting the weights of operators and improving the prediction performance of the rate-dependent butterfly hysteresis model. Experiment results indicate that the developed model can effectively predict the rate-dependent hysteresis effect in DEAs.

关键词： Neural networks Elastomers Predictive models Control systems Behavioral sciences Modeling Hysteresis

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Robust Global Prescribed Performance Control of Unknown Strict-Feedback Systems

Robust Global Prescribed Performance Control of Unknown Stri...

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Chinese Control and Decision Conference, CCDC

作者： Wei Ding Jin-Xi Zhang Faculty of Electrical Engineering and Automation Changshu Institute of Technology Changshu China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China

ISBN: (纸本)9781665478977

This paper is concerned with the reference tracking control problem for strict-feedback systems with unknown nonlinear functions under mismatched disturbances. A tuning function-based prescribed performance control strategy is proposed to solve the problem. In the control design, the tuning function is employed to adjust the output tracking error and the intermediate tracking errors. In this way, the dependence of the performance specifications on the initial condition of the closed loop is relaxed. Then, a set of barrier functions are adopted to confine the resulting error signals, while the potential robustness of the barrier functions is thoroughly revealed. By doing so, it is guaranteed that all the real errors converge from any initial positions to the preassigned sets in the given time. The simulation results illustrate the effectiveness of the proposed control.

关键词： Robust control Uncertainty Control design Simulation Estimation Robustness Closed loop systems

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AIGC for Industrial Time Series: From Deep Generative Models to Large Generative Models

arXiv

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arXiv 2024年

作者： Ren, Lei Wang, Haiteng Li, Jinwang Tang, Yang Yang, Chunhua School of Automation Science and Electrical Engineering Beihang University Beijing100191 China The Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai200237 China The School of Automation Central South University Changsha410083 China

With the remarkable success of generative models like ChatGPT, Artificial Intelligence Generated Content (AIGC) is undergoing explosive development. Not limited to text and images, generative models can generate industrial time series data, addressing challenges such as the difficulty of data collection and data annotation. Due to their outstanding generation ability, they have been widely used in Internet of Things, metaverse, and cyber-physical-social systems to enhance the efficiency of industrial production. In this paper, we present a comprehensive overview of generative models for industrial time series from deep generative models (DGMs) to large generative models (LGMs). First, a DGM-based AIGC framework is proposed for industrial time series generation. Within this framework, we survey advanced industrial DGMs and present a multi-perspective categorization. Then, we systematically propose the roadmap to construct industrial LGMs from four aspects: large-scale industrial dataset, LGMs architecture for complex industrial characteristics, self-supervised training for industrial time series, and fine-tuning of industrial downstream tasks. Furthermore, we introduce an evaluation benchmark that systematically assesses fidelity, diversity, and utility. We include a case study on aircraft engine maintenance, demonstrating the application of DGMs in industrial predictive maintenance. Finally, we conclude the challenges and future directions to enable the development of generative models in industry. Copyright © 2024, The Authors. All rights reserved.

关键词： Predictive maintenance

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Constructive Safety Control

arXiv

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arXiv 2024年

作者： Wu, Si Liu, Tengfei Jiang, Zhong-Ping State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110004 China Department of Electrical and Computer Engineering New York University 360 Jay Street BrooklynNY11201 United States

This paper proposes a constructive approach to safety control of nonlinear cascade systems subject to multiple state constraints. New design ingredients include a unified characterization of safety and stability for systematic designs of safety controllers, and a novel technique of reshaping the feasible sets of quadratically constrained quadratic programming induced from safety control. The proposed method guarantees Lipschitz continuity of virtual control laws, enabling a stepwise constructive design. A refined nonlinear small-gain synthesis is employed to address the nonlinear uncertain interconnections between the resulting subsystems corresponding to different virtual control laws, and to guarantee the achievement of the safety control objective. When the safety constraints are removed, the proposed approach coincides with the standard constructive nonlinear control. The proposed safety-control algorithm is experimentally validated in a testbed involving a vertical takeoff and landing (VTOL) vehicle taking off in narrow spaces. Copyright © 2024, The Authors. All rights reserved.

关键词： Quadratic programming

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Singular Perturbation: When the Perturbation Parameter Becomes a State-Dependent Function

arXiv

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arXiv 2024年

作者： Liu, Tengfei Jiang, Zhong-Ping State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110004 China Department of Electrical and Computer Engineering New York University Five MetroTech Center BrooklynNY11201 United States

This paper presents a new systematic framework for nonlinear singularly perturbed systems in which state-dependent perturbation functions are used instead of constant perturbation coefficients. Under this framework, general results are obtained for the global robust stability and input-to-state stability of nonlinear singularly perturbed systems. Interestingly, the proposed methodology provides innovative solutions beyond traditional singular perturbation theory for emerging control problems arising from nonlinear integral control, feedback optimization, and formation-based source seeking. © 2024, CC BY-NC-ND.

关键词： Perturbation techniques

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Sparse Causality Analysis Approach with Time-varying Parameters for Root Cause Localization of Nonstationary process 4

Sparse Causality Analysis Approach with Time-varying Paramet...

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4th International Conference on Industrial Artificial Intelligence, IAI 2022

作者： Song, Pengyu Zhao, Chunhui Huang, Biao Ding, Jinliang College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China University of Alberta Department of Chemical and Materials Engineering EdmontonABT6G 2G6 Canada Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China

ISBN: (数字)9781665451208

ISBN: (纸本)9781665451208

Root cause diagnosis (RCD) is an important technique for maintaining the safe operation of industrial processes. Traditional RCD methods usually require stationarity assumptions. However, the process inevitably shows nonstationarity due to factors such as switching of operating conditions. Although there have been some previous studies trying to overcome the challenge of nonstationarity, these methods fail to guarantee the significance of the extracted causalities and lead to redundant relationships. To address the above issues, a sparse causal analysis model with time-varying parameters is extracted in this study. First, we propose an end-to-end information fusion and prediction task to characterize predictive relationships between variables and avoid repeated modeling. Second, we design time-varying parameters for the information fusion mechanism to cope with nonstationarity and automatically identify significant causality through sparse parameter updates. We design an update strategy that constrains the gradient information to guarantee sparsity. Finally, a causal metric is constructed for the time-varying predictive relationship to comprehensively obtain the overall causal relationship, which further guarantees causal significance. The validity of the proposed method is illustrated through a real industrial example collected from a thermal power plant. © 2022 IEEE.

关键词： Information fusion

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