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arXiv 2025年

作者： Lin, Junxiao Ji, Shuhang Wu, Yuze Wu, Tianyue Han, Zhichao Gao, Fei State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China

How to endow aerial robots with the ability to operate in close proximity remains an open problem. The core challenges lie in the propulsion system’s dual-task requirement: generating manipulation forces while simultaneously counteracting gravity. These competing demands create dynamic coupling effects during physical interactions. Furthermore, rotor-induced airflow disturbances critically undermine operational reliability. Although fully-actuated unmanned aerial vehicles (UAVs) alleviate dynamic coupling effects via six-degree-of-freedom (6-DoF) force-torque decoupling, existing implementations fail to address the aerodynamic interference between drones and environments. They also suffer from oversized designs, which compromise maneuverability and limit their applications in various operational scenarios. To address these limitations, we present FLOAT Drone (FuLly-actuated cOaxial Aerial roboT), a novel fully-actuated UAV featuring two key structural innovations. By integrating control surfaces into fully-actuated systems for the first time, we significantly suppress lateral airflow disturbances during operations. Furthermore, a coaxial dual-rotor configuration enables a compact size while maintaining high hovering efficiency. Through dynamic modeling, we have developed hierarchical position and attitude controllers that support both fully-actuated and underactuated modes. Experimental validation through comprehensive real-world experiments confirms the system’s functional capabilities in close-proximity operations. Copyright © 2025, The Authors. All rights reserved.

关键词： Drones

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Digital Twin Empowered Operational Status Monitoring of High-Power Induction Furnaces: A PINN-Based Approach

Digital Twin Empowered Operational Status Monitoring of High...

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International Conference on control and Automation (ICCA)

作者： Zhao Zhang Shen Li Wei-Jie Mao State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

In the realm of industrial automation and smart manufacturing, improving the efficiency of high-power induction furnaces (IFs) has become an essential goal. Precise monitoring of the operational status is indispensable for achieving targeted optimization goals. The challenges encountered in this endeavor are twofold in nature. IF, as a highly complex thermoelectric system, spans multiple disciplinary fields, complicating the achievement of precise modeling. Furthermore, its electrical dynamic parameters continuously change throughout the melting process, eluding direct measurement methods. In this paper, we introduce a novel equivalent circuit framework that simplifies the analysis of the IF operational status. Secondly, we establish a PINN-based digital twin (DT) for IF to simulate the dynamics throughout the entire melting process, thereby supporting the status monitoring. state-space constraints are introduced to overcome the deficiencies of the traditional PINNs in handling multidimensional systems. Finally, we assess the feasibility of the approach using actual operational data spanning the complete working cycle of IF.

关键词： Multidimensional systems Automation Furnaces Artificial neural networks Real-time systems Digital twins Integrated circuit modeling

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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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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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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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Research on Multi-Task Scheduling in Smart Warehouse systems Based on Dual-Task Chains

Research on Multi-Task Scheduling in Smart Warehouse Systems...

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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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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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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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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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