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IEEE International Conference on Networking, Sensing and control

作者： Chenwei Zhang Weimin Wu State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

ISBN: (数字)9798350365221

ISBN: (纸本)9798350365238

With the advancement of warehousing technology, the allocation of multiple tasks in multi-AGV systems has become an important factor in order to decrease task completion time in the warehouse. This paper develops a multi-task scheduling algorithm that employs a dual-task chain approach to maximize task execution benefits. For the multi-task allocation problem, this paper proposes a dual-task chain-based task allocation algorithm (DTTA), which uses the dual-task chain strategy as a mechanism for selecting multiple tasks, taking into consideration derived tasks to enhance solution quality. Simulation experiments show that the proposed algorithm and model perform better than other methods.

关键词： Scheduling algorithms Simulation Warehousing System recovery Multitasking Sensors Batteries Resource management Collision avoidance

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Toward Understanding key Estimation in Learning Robust Humanoid Locomotion

Toward Understanding Key Estimation in Learning Robust Human...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Zhicheng Wang Wandi Wei Ruiqi Yu Jun Wu Qiuguo Zhu Institute of Cyber-Systems and Control Zhejiang University China State Key Laboratory of Industrial Control Technology China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Accurate state estimation plays a critical role in ensuring the robust control of humanoid robots, particularly in the context of learning-based control policies for legged robots. However, there is a notable gap in analytical research concerning estimations. Therefore, we endeavor to further understand how various types of estimations influence the decision-making processes of policies. In this paper, we provide quantitative insight into the effectiveness of learned state estimations, employing saliency analysis to identify key estimation variables and optimize their combination for humanoid locomotion tasks. Evaluations assessing tracking precision and robustness are conducted on comparative groups of policies with varying estimation combinations in both simulated and real-world environments. Results validated that the proposed policy is capable of crossing the sim-to-real gap and demonstrating superior performance relative to alternative policy configurations.

关键词： Robust control Legged locomotion Humanoid robots Stairs Software Robustness Hardware Encoding state estimation Intelligent robots

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An Interpretable Semi-Supervised Learning Approach for Battery Lifespan Early Prognostic

An Interpretable Semi-Supervised Learning Approach for Batte...

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Chinese control and Decision Conference, CCDC

作者： Shuxin Zhang Zhitao Liu Tao An Jiankang Guo Hongye Su State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Early estimation of battery lifespan holds significant importance in efficient battery health management. This paper presents an Interpretable Semi-supervised learning approach for estimating battery lifespans during their initial degradation stage. Specifically, a Physics-informed Autoencoder is introduced in the pretraining stage to extract features reflecting the early degradation mode of batteries by incorporating an electrochemical model into data-driven method. Moreover, to improve interpretability, an aging mechanism-based attention neural network is proposed to integrate electrode-level degradation mode with cyclic-based features for early battery lifespan prognostics. Additionally, a Bayesian Neural Network is incorporated into the model for uncertainty estimation. The effectiveness of the proposed method is validated using the LiFePO 4 dataset, demonstrating high performance in the early estimation of battery lifespan.

关键词： Degradation Uncertainty Accuracy Neural networks Estimation Semisupervised learning Aging

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Optimal Prices for Coordinated Operation of Coupled Transportation and Power systems

Optimal Prices for Coordinated Operation of Coupled Transpor...

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Chinese Automation Congress (CAC)

作者： Jia Li Bin Li Qincheng Li Zhitao Liu Hongye Su The State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

The growing adoption of electric vehicles (EVs) necessitates an in-depth analysis of the effects of centralized charging on both traffic congestion and power distribution networks (PDNs). This study presents a coupled model of transportation and power systems that incorporates EV travel behavior and charging costs, emphasizing their interaction between the urban transportation network and the PDN via dynamic wireless energy transmission technology. The equilibrium of the coupled system was then determined through an iterative algorithm aimed at minimizing the total social cost. Example results validate the model's accuracy and effectiveness. Furthermore, the study demonstrates that the proposed approach can significantly alleviate traffic congestion while simultaneously satisfying the charging needs of EVs and maintaining the stability of the power distribution network

关键词： Costs Power system dynamics Transportation Wireless power transfer Power system stability Mathematical models Stability analysis Vehicle dynamics Traffic congestion Thermal stability

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Optimal Traffic-Power Flow of Transportation-Microgrid Coupled System Based on Spatiotemporal Coupling

Optimal Traffic-Power Flow of Transportation-Microgrid Coupl...

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Chinese Automation Congress (CAC)

作者： Bin Li Jia Li Qincheng Li Zhitao Liu Hongye Su State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

The growing adoption of electric vehicles and dynamic wireless charging has strengthened the spatiotemporal interconnection between the transportation network (TN) and the microgrid (MG). This paper presents an optimal traffic-power flow (OTPF) model for the transportation-microgrid coupled system (TMCS) to enhance the coordination of its operations. The OTPF model is transformed into bi-level programming, with the optimal power flow model of MGs at the upper level and the dynamic traffic assignment model of the TN at the lower level. The upper level is a mixed-integer second-order cone programming, while the lower level is formulated as a dynamic user equilibrium. A fixed-point algorithm is developed to tackle this bi-level programming. Case studies show that the OTPF model of the TMCS can be efficiently solved to capture the spatiotemporal variations of traffic flow and power flow.

关键词： Couplings Wireless communication Optimization models Transportation Programming Spatiotemporal phenomena Numerical models Dynamic programming Vehicle dynamics Load flow

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Balancing Operation Cost and Carbon Emissions in Multi-Energy Microgrid Scheduling Using Joint Operator Evolutionary Algorithm

Balancing Operation Cost and Carbon Emissions in Multi-Energ...

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Chinese control and Decision Conference, CCDC

作者： Bin Li Qincheng Li Jia Li Zhitao Liu Hongye Su State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

A multi-energy microgrid (MEMG), incorporating a combined heat and power (CHP) system and electric vehicles (EVs), is an important infrastructure to meet multiple energy demands and reduce carbon emissions. However, operation cost minimization and carbon emissions minimization in the MEMG conflict with each other. Therefore, this paper proposes a multi-objective optimal scheduling model for the MEMG, aimed at achieving a balance between operation cost and carbon emissions. Moreover, a novel multi-objective evolutionary algorithm (MOEA) called joint operator evolutionary algorithm (JOEA) is developed to solve this model. Experimental results indicate that JOEA has excellent performance on the proposed multi-objective optimal scheduling problem. In addition, JOEA is compared with several well-known MOEAs on this engineering problem and 20 benchmark problems. The comparison results show that JOEA outperforms other MOEAs and has competitive performance.

关键词： Costs Cogeneration Carbon dioxide Optimal scheduling Evolutionary computation Microgrids Benchmark testing

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An Effective Adaptive Large Neighborhood Search Algorithm for Multi-AGV Dispatching Problem in Matrix Manufacturing systems

An Effective Adaptive Large Neighborhood Search Algorithm fo...

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IEEE International Conference on Networking, Sensing and control

作者： Boyu Li Weimin Wu State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

ISBN: (数字)9798350365221

ISBN: (纸本)9798350365238

With the increasing prevalence of automated guided vehicles (AGV s) in industry, the AG V dispatching has emerged as a research hotspot. Recently, matrix manufacturing systems (MMS) have been favored for their suitability in mass-customized production. However, fluctuating and diverse transport demands in MMS pose challenges for effective AGV scheduling. This paper investigates a multi-AGV dispatching problem (AGVDP) in MMS, involving time-constrained pick-up and delivery tasks. A mixed-integer linear programming model is developed to minimize total transportation costs, including travel distance, early arrival time, and utilized AGV s. To achieve this, an effective adaptive large neighborhood search algorithm (ALNS) is proposed. In ALNS, a scope-based nearest neighbor heuristic is introduced for initial construction. Diverse problem-specific operators are designed to generate large neighborhoods. To enhance ALNS's robustness to instance variations, an adaptive simulated annealing acceptance criterion is proposed. Computational experiments using a well-established benchmark validate the superiority of the proposed ALNS.

关键词： Adaptation models Sensitivity Transportation Simulated annealing Search problems Dispatching Sensors Time factors Tuning Manufacturing systems

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Whole-body Dynamic Collision Avoidance with Time-varying control Barrier Functions

Whole-body Dynamic Collision Avoidance with Time-varying Con...

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Chinese control and Decision Conference, CCDC

作者： Jihao Huang Xuemin Chi Zhitao Liu Hongye Su State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Recently, there has been increasing attention in robot research towards the whole-body collision avoidance. In this paper, we propose a safety-critical controller that utilizes time-varying control barrier functions (time varying CBFs) constructed by Robo-centric Euclidean Signed Distance Field (RC-ESDF) to achieve dynamic collision avoidance. The RCESDF is constructed in the robot body frame and solely relies on the robot’s shape, eliminating the need for real-time updates to save computational resources. Additionally, we design two control Lyapunov functions (CLFs) to ensure that the robot can reach its destination. To enable real-time application, our safety-critical controller which incorporates CLFs and CBFs as constraints is formulated as a quadratic program (QP) optimization problem. We conducted numerical simulations on two different dynamics of an L-shaped robot to verify the effectiveness of our proposed approach.

关键词： Silver Navigation Shape Numerical simulation Real-time systems Trajectory Collision avoidance

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Research on Optimization Seeking Method of Transmission Efficiency of Orbital Inspection Robot Based on DWPT System

Research on Optimization Seeking Method of Transmission Effi...

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IEEE Conference and Expo Transportation Electrification Asia-Pacific (ITEC Asia-Pacific)

作者： Youcheng Chen Zhitao Liu Xuchi Xue Hongye Su State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

ISBN: (数字)9798331529277

ISBN: (纸本)9798331529307

In wireless power transmission system, due to the changes of coupling coefficient, load impedance and other factors, it destroys the system constant voltage, constant power output and transmission efficiency optimal working condition. Therefore, this paper proposes an optimization seeking method for the maximum transmission efficiency of the DWPT system of an orbital inspection robot with constant voltage and constant power output. Firstly, the LCC-S topology mathematical model with additional PI control circuit and the convex optimization model of transmission efficiency are established, and the gradient descent algorithm is used to iteratively search for the maximum value of transmission efficiency when the orbital coupling coefficient changes. Then, based on the mutual inductance model between coils, the coupling coefficients are converted into coil inductance parameters L 1 and L 2 , and the coil self-inductance is utilized for matching regulation to keep the transmission efficiency at the optimal value. Finally, in order to further optimize and predict the transmission efficiency, the fitting effects of the four fitting curves of the transmission efficiency are observed by the coefficient of determination and the residual paradigm, and the optimal fitting curves are used to calculate the maximum and average transmission efficiency, which reduces the system transmission loss and improves the charging efficiency.

关键词： Coils Couplings Inductance Fitting Inspection Orbits Mathematical models Integrated circuit modeling Optimization Robots

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Real-Time Intrusion Detection Based on Decision Fusion in industrial control systems

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on industrial cyber-Physical systems 2024年 2卷 143-153页

作者： Xue, Yawen Pan, Jie Geng, Yangyang Yang, Zeyu Liu, Mengxiang Deng, Ruilong Information Engineering University Zhengzhou450001 China Singapore University of Technology and Design ITrust 487372 Singapore The University of Sheffield Department of Automatic Control and Systems Engineering SheffieldS10 2TN United Kingdom Zhejiang University State Key Laboratory of Industrial Control Technology The College of Control Science and Engineering Hangzhou310027 China Huzhou Institute of Industrial Control Technology Huzhou313000 China

industrial control systems (ICSs) are becoming increasingly interconnected as the rapid convergence of information technology (IT) and operation technology (OT) networks, and meanwhile massive attack surfaces have been exposed. However, traditional intrusion detection systems (IDSs) are difficult to be directly deployed in ICSs due to the hard real-time requirement and rare patching chance. Besides, the design of effective and practical IDSs is hampered by the lack of benchmarking ICS cybersecurity datasets. To bridge the gaps, this paper makes the first attempt by open-sourcing the developed ICS cybersecurity datasets and proposing a decision fusion based real-time IDS. Firstly, we design a customized cybersecurity dataset in a full-hardware and high-fidelity platform, including 7 types of cyber threats tailored for ICSs. The collected dataset includes network traffic, sensor readings, actuator status, and system parameters, providing the state-of-the-art benchmark dataset for ICSs consisting of cross-layer characteristics. Furthermore, we design an online decision fusion-based IDS by strategically integrating 4 widely-used machine learning models. The proposed IDS is deployed on a real-time running ethanol distillation, surpassing the performance of single detection models in terms of precision and F1-score, which substantially enhances intrusion detection accuracy and cybersecurity of ICS. © 2023 IEEE.

关键词： Intrusion detection

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