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第41届中国控制会议

作者： Tangwen Yin Department of Automation Shanghai Jiao Tong UniversityKey Laboratory of System Control and Information ProcessingMinistry of Education

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Exposition to risk for the benefit of automatic control creates *** and robustness are two options to reduce the inherent fragility risk of safety-critical ***,time and resource parameters that characterize the agile responsiveness and robust adaptivity are difficult to be optimized due to multilevel coupling structures with different time *** propose the quasi Markov simplex optimization(QMSO) of nonlinear functional parameters in multivariable collaborative cascade control(MCCC) to improve agility and robustness for systems with hierarchical multivariable coordination and multiple *** verify if the singularly perturbed feedback and control with quasi Markovian jumps through optimal nonlinear functional parameters can achieve the multivariable collaborative cascade control equivalent to that of singular perturbation modeling and design without decomposing slow and fast *** in aviation show that the nonlinear functional parameters in MCCC can be estimated through QMSO to improve agility and robustness for the consistence of flight *** find that the simplex optimization equivalently establishes the nonlinear singularly perturbed feedback and control with quasi Markovian jumps,without having to realize singular perturbation modeling and design,and there is no need to decompose slow and fast *** conclusion,the quasi Markov simplex estimation of nonlinear functional parameters optimizes the hierarchical multivariable coordination under multiple timescales,so that the synthesized collaborative cascade control is capable to improve agility and robustness.

关键词： Agility Hierarchical Multivariable Coordination Multiple Timescales Nonlinear Aircraft control Robustness

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Neural-based prescribed-time consensus control for multiagent systems via dynamic memory event-triggered mechanism

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Science China(Technological Sciences) 2025年第3期68卷 217-227页

作者： Xiaohong ZHENG Hui MA Qi ZHOU Hongyi LI School of Automation Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Controland Guangdong Provincial Key Laboratory for Intelligent Decision and Cooperative ControlGuangdong University of TechnologyGuangzhou510006China School of Mathematics and Statistics Guangdong University of TechnologyGuangzhou510006China College of Electronic and Information Engineering and the Chongqing Key Laboratory of Generic Technology and System of Service Robots Southwest UniversityChongqing400715China

This work investigates the implementation of distributed prescribed-time neural network(NN)control for nonlinear multiagent systems(MASs)using a dynamic memory event-triggered mechanism(DMETM).First,it introduces a composite learning technique in NN *** method leverages the prediction error within the NN update law to enhance the accuracy of the unknown nonlinearity ***,by introducing a time-varying transformation,the study establishes a distributed prescribed-time control *** notable feature of this algorithm is its ability to predetermine the convergence time independently of initial conditions or control ***,the DMETM is established to reduce the actuation frequency of the *** the conventional memoryless dynamic event-triggered mechanism,the DMETM incorporates a memory term to further increase triggering *** a distributed estimator for the leader,the DMETM-based NN prescribed-time controller is designed in a fully distributed manner,which guarantees that all signals in the closed-loop system remain bounded within the prescribed ***,simulation results are presented to validate the effectiveness of the proposed algorithm.

关键词： consensus control composite learning control dynamic memory event-triggered control prescribed-time control multiagent systems(MASs)

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Silicon Content control for Blast Furnace: Model-free DDQN-ICM Method

Silicon Content Control for Blast Furnace: Model-free DDQN-I...

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2023 China Automation Congress, CAC 2023

作者： Zhang, Baocong Ding, Xuda Liu, Wei Chen, Cailian Guan, Xinping Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Department of Automation Shanghai200240 China

ISBN: (纸本)9798350303759

Silicon content control is critical to ensure the quality of molten iron and the stable operation of the blast furnace. The complex reactions and large uncertainty of the blast furnace make it hard to control. Several predictive control-based methods were already studied in Silicon content control. However, the requirements of prediction model design and short predictive horizon limit the generalization and long-term optimization of the methods. To tackle these issues, we propose a deep double Q-learning (DDQN) and intrinsic curiosity module (ICM) based Silicon content control method. First, the deep Q-learning framework employed in our approach eliminates the need for a prediction model and supports long-range simulations, thereby enhancing algorithm generalization and long-term optimization capabilities. Second, DDQN and ICM help to avoid the over-optimistic of action taking and improve exploration efficiency to tackle the large uncertainty of the blast furnace. Third, to ensure operational safety, we have designed a regulation strategy that incorporates ϵ-greedy exploration and ICM, mitigating the risks posed by exploration actions. Finally, we validate the effectiveness of our method using on-site data, demonstrating that DDQN-ICM achieves superior performance in terms of tracking accuracy and disturbance rejection. © 2023 IEEE.

关键词： Blast furnaces

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EPM-Net: A better way for optical and infrared scene matching via edge point 12

EPM-Net: A better way for optical and infrared scene matchin...

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SPIE 12th International Symposium on Multispectral Image processing and Pattern Recognition, MIPPR 2023

作者： Huang, Shixiang Chi, Longyun Fu, Wenxing Tian, Jinwen National Key Laboratory of Multispectral Information Intelligent Processing Technology Huazhong University of Science and Technology Wuhan China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (数字)9781510674943

ISBN: (纸本)9781510674936

As a crucial auxiliary technique in inertial navigation, scene matching has been widely applied in aircraft navigation and guidance. To enhance the adaptability and efficiency of scene matching algorithms in complex environments, existing approaches have gradually shifted towards heterogeneous scene matching, such as optical/ infrared scene matching based on deep learning features, with a focus on point features. However, due to the complex and diverse nonlinear distortions between optical and infrared images, the matching accuracy often fails to meet the practical requirements of navigation and positioning. In view of this problem, an EPM-Net algorithm based on edge features is proposed to improve matching performance by extracting more stable and discriminative edge point descriptors. Experimental results on infrared datasets demonstrate that this method achieves an average matching accuracy improvement of more than four times compared to traditional and existing deep learning methods. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Deep learning

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Motor Fault Diagnosis Under Complex Working Conditions Based on Signal Fusion and CA-CNN

Motor Fault Diagnosis Under Complex Working Conditions Based...

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2024 China Automation Congress, CAC 2024

作者： Cai, Mingxuan Liu, Qi Liu, Yuxiang Yang, Bo Department of Automation Shanghai Jiao Tong University shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (纸本)9798350368604

Accurate fault diagnosis plays a key role in the safe and efficient operation of electrical motors. Aiming at multi-class motor fault diagnosis under complex working conditions, a signal fusion-based fault diagnosis framework is proposed. Measurement parameters from different sensors are first transformed by wavelet denoising and fast Fourier transform (FFT). Then, multiple parameters spectrum and working condition information are integrated and converted into high-dimension matrices. Finally, a channel-attention-based convolution neural network (CA-CNN) is proposed to extract fault patterns from a reconstructed matrix and output diagnosis results. Experiment results on both simulated and experimental datasets verify the effectiveness of the proposed method. © 2024 IEEE.

关键词： Convolutional neural networks

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A Pricing Strategy for Smart Grid Based on PSO and Distributed Iterative Algorithm

A Pricing Strategy for Smart Grid Based on PSO and Distribut...

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International Conference on Communications, Signal processing, and systems, CSPS 2023

作者： Wang, Xiaocheng Ma, Haogang Yin, Yuan Li, Zelong Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission College of Electronic and Communication Engineering Tianjin Normal University Tianjin300387 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China

ISBN: (纸本)9789819975556

This paper proposes a pricing strategy for the Market by using a Stackelberg game-based bi-level programming model. In the model, the wholesale price and the retail price is optimized by the Market to increase its profit. In the model, the Energy Provider (EP) optimize optimizes the adjustment coefficient to increase the trading probability with the Utility Company (UC). Besides, the Market can sell the power to the UC directly. Then, the UCs determine their retail price by non-cooperative game to increase their profit. The simulation results reveal that the Market determines the optimal price to maximize profits. On the other hand, the proposed price strategy of EP can increase the trading probability, which promotes more UCs to trade with EP, thus increasing its benefit. Finally, UCs get a bash equilibrium through the non-cooperative game. The research proves that the proposed strategy is a win–win strategy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Profitability

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Reinforcement Learning with Hierarchical Graph Structure for Flexible Job Shop Scheduling

Reinforcement Learning with Hierarchical Graph Structure for...

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2023 International Annual Conference on Complex systems and Intelligent Science, CSIS-IAC 2023

作者： Zhang, Linli Li, Dewei Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Dept. of Automation Shanghai China

ISBN: (纸本)9798350309003

The flexible job shop scheduling problem (FJSP) is a NP-hard combinatorial optimization challenge, having extensive real-world industrial applications. For a given instance of FJSP, a scheduling solution need to be devised, which entails determining the appropriate machine for each operation and establishing the sequence in which operations are processed on a given machine, all while adhering to certain constraints. However, many of the methods are lacking in flexibility, unable to cope effectively with multi-relational operations and machine selection scenarios. This can make the representation of state and action challenging in practice, leading to a significant increase in decision complexity. This paper considers these issues by a hierarchical graph structure with a model that synergistically integrates two modules: the environment and the decision module. The environment module is characterized by a hierarchical graph neural network which defines the multi-relational FJSP instance where all machine and operation data are stored. The decision module incorporates the Actor-Critic networks, designed to train reasoning strategies and enhance decision performance, based on the states and rewards provided at each step by the environment module. The objective is to derive optimal scheduling strategies through interactions between the decision agent and the environment, targeting the minimum resources consumed or makespan. Numerical simulations validate the efficacy of the proposed approach, showing its potential to yield superior results and thereby enhance performance. © 2023 IEEE.

关键词： Reinforcement learning

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V2X Communication Assisted Hierarchical Path Planning for Connected and Automated Vehicles

V2X Communication Assisted Hierarchical Path Planning for Co...

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2023 China Automation Congress, CAC 2023

作者： Li, Jiahang Chen, Cailian Gao, Fengkun Yang, Bo Guan, Xinping Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Department of Automation Shanghai200240 China

ISBN: (纸本)9798350303759

Connected and Automated Vehicles (CAVs) have attracted increasing attention in intelligent transportation systems. An effective path planning enables the efficient operation of CAVs so that they can coordinate traffic flow and avoid accidents. This paper presents a framework of V2X communication assisted hierarchical path planning system for CAVs that aims to improve computational efficiency and safety performance. The proposed framework consists of two stages: pre-planning and online-planning. The pre-planning stage leverages the computing and storage capabilities of Roadside Units (RSUs) to generate and filter candidate trajectories for different driving scenarios. The online-planning stage adjusts the pre-planned trajectory based on the vehicle's driving risk index, which considers the distance and relative speed to the surrounding vehicles. The performance of effectiveness, safety, and timeliness of the proposed framework is evaluated and demonstrated by a simulation study in different scenarios. Compared with conventional methods, the proposed method improves the computational efficiency by 23% and reduces the collision rate by 13 %. © 2023 IEEE.

关键词： Vehicle to Everything

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Digital Twin Based Sintering Proportioning: Improved Whale Optimization Method

Digital Twin Based Sintering Proportioning: Improved Whale O...

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2023 China Automation Congress, CAC 2023

作者： Liu, Wei Ding, Xuda Bai, Xuehan Chen, Cailian Guan, Xingping Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Department of Automation Shanghai200240 China

ISBN: (纸本)9798350303759

Sintering proportion optimization plays a crucial role in determining the final product quality of sintered iron ore. However, the traditional approach of separating the optimization processes between the procurement and technical departments often fails to achieve the global optimum. In this paper, We propose a joint optimization approach for sintering proportioning based on digital twin (DT) technology. To tackle the complex constraints of this joint optimization problem, we develop a multiple improved whale optimization algorithm (MIWOA) that overcomes the issue of getting trapped in local optima. The proposed MIWOA incorporates three strategies, including a nonlinear convergence factor, Levy flight, and Gaussian mutation. Besides, the numerical simulations are conducted in order to analyze the influence of the convergence factors. Finally, various related approaches for sintering proportioning optimization are tested, and DT-based MIWOA achieved the best performance. © 2023 IEEE.

关键词： Sintering

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Global practical stabilization of the double integrator system with an imperfect sensor and subject to a bounded disturbance

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control Theory and Technology 2024年第3期22卷 468-478页

作者： Zhiyuan Zhu Yuanlong Li Zongli Lin Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information Processing of Ministry of EducationShanghai 200240China Charles L.Brown Department of Electrical and Computer Engineering University of VirginiaP.0.Box 400743CharlottesvilleVA 22904-4743USA

This paper is concerned with global practical stabilization of the double integrator system with an imperfect sensor and subject to an additive bounded output *** imperfect sensor nonlinearity possesses the nonlinear characteristics of saturation and dead *** of the presence of output dead zone and the additive disturbance,the states cannot be expected to driven into an arbitrarily small neighborhood of the *** solve the global practical stabilization problem,we proposes a low gain-based linear dynamic output feedback law,under which the first state enters and remains in a bounded set whose size is depended on the bound of disturbance and the range of dead zone and the second state enters and remains in a pre-specified arbitrarily small set,both in finite *** results illustrate the effectiveness of our proposed control method.

关键词： Output saturation Disturbance rejection Output dead zone Low gain feedback-Linear output feedback Practical stabilization

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