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Command filter-based I&I adaptive control for MIMO uncertain systems with input saturation and disturbances

Science China(Information Sciences) 2023年第12期66卷 64-79页

作者： Qi HAN Zhitao LIU Hongye SU Xiangbin LIU State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and ControlZhejiang University School of Electronic Information Engineering Beijing Jiaotong University

This paper develops a control strategy based on immersion and invariance(I&I) adaptive methodology for a class of multi-input multi-output(MIMO) systems in the presence of parametric uncertainty, input saturation, and external disturbance. To avoid the analytic calculation in the backstepping process, a highgain auxiliary system is constructed to compensate for the effect of command filter error. The first-order command filters are also employed in the construction procedure of the I&I adaptive law to simplify its design and remove the structural conditions on the regressors. A filter-based disturbance observer is developed to counteract the effect of the external disturbance produced by a partially known exogenous *** overcome the input saturation nonlinearity, a smooth function is introduced to approximate the input saturation with an extended state and a bounding estimation law. Stringent analysis guarantees the stability of closed-loop system. Finally, simulated examples confirm the effectiveness of the suggested method.

关键词： adaptive control command filtered backstepping disturbance observer immersion and invariance MIMO systems

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FLOAT Drone: A Fully-actuated Coaxial Aerial Robot for Close-Proximity Operations

arXiv

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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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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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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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RoG-SAM: A Language-Driven Framework for Instance-Level Robotic Grasping Detection

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IEEE Transactions on Multimedia 2025年 27卷 3057-3068页

作者： Mei, Yunpeng Sun, Jian Peng, Zhihong Deng, Fang Wang, Gang Chen, Jie Beijing Institute of Technology State Key Lab of Autonomous Intelligent Unmanned Systems Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China Tongji University Department of Control Science and Engineering Shanghai201804 China

Robotic grasping is a crucial topic in robotics and computer vision, with broad applications in industrial production and intelligent manufacturing. Although some methods have begun addressing instance-level grasping, most remain limited to predefined instances and categories, lacking flexibility for open-vocabulary grasp prediction based on user-specified instructions. To address this, we propose RoG-SAM, a language-driven, instance-level grasp detection framework built on Segment Anything Model (SAM). RoG-SAM utilizes open-vocabulary prompts for object localization and grasp pose prediction, adapting SAM through transfer learning with encoder adapters and multi-head decoders to extend its segmentation capabilities to grasp pose estimation. Experimental results show that RoG-SAM achieves competitive performance on single-object datasets (Cornell and Jacquard) and cluttered datasets (GraspNet-1Billion and OCID), with instance-level accuracies of 91.2% and 90.1%, respectively, while using only 28.3% of SAM's trainable parameters. The effectiveness of RoG-SAM was also validated in real-world environments. © 1999-2012 IEEE.

关键词： Robot vision

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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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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 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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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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