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

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Research on Optimization Seeking Method of Transmission Effi...

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

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

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IEEE Transactions on Industrial Cyber-Physical Systems

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

Obstacle Based Fast Marching Tree for Global Motion Planning 48

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Obstacle Based Fast Marching Tree for Global Motion Planning

48th Annual Conference of the IEEE industrial Electronics Society, IECON 2022

作者： Hou, Jiale Liu, Zhitao Su, Hongye Zhejiang University Polytechnic Institute Hangzhou China Zhejiang University Institute of Cyber-Systems and Control State Key Laboratory of Industrial Control Technology Hangzhou China

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

In this paper we introduce a novel method to improve the general performance of fast marching tree for collision-free path planning called obstacle-based fast marching tree. Our proposed OB-FMT∗ samples the map in discussion initially to obtain the vertexes of all obstacles and connect those vertexes to build a collision-free roadmap, an A*algorithm is adopted to find an optimal path in the built roadmap. Then multiple prolate hyper spheroids with focuses on vertexes of optimal path are established to limit the sampling range. Within the limited sampling point set, we utilize FMT∗ to find optimal solution with high efficiency. Theoretical analyses are given to prove the probabilistic completeness and computational complexity of our proposed method. Experiments in multiple scenarios show that our algorithm has a better performance comparing with other existing algorithms including RRT*, FMT*, Informed RRT*. © 2022 IEEE.

关键词： Motion planning

SOMTP: Self-Supervised Learning-Based Optimizer for MPC-Based Safe Trajectory Planning Problems in Robotics

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arXiv 2024年

作者： Liu, Yifan Wang, You Li, Guang State Key Laboratory of Industrial Control Technology Institute of Cyber Systems and Control Zhejiang University Hangzhou310027 China

Model Predictive control (MPC)-based trajectory planning has been widely used in robotics, and incorporating control Barrier Function (CBF) constraints into MPC can greatly improve its obstacle avoidance efficiency. Unfortunately, traditional optimizers are resource-consuming and slow to solve such non-convex constrained optimization problems (COPs) while learning-based methods struggle to satisfy the non-convex constraints. In this paper, we propose SOMTP algorithm, a self-supervised learning-based optimizer for CBF-MPC trajectory planning. Specifically, first, SOMTP employs problem transcription to satisfy most of the constraints. Then the differentiable SLPG correction is proposed to move the solution closer to the safe set and is then converted as the guide policy in the following training process. After that, inspired by the Augmented Lagrangian Method (ALM), our training algorithm integrated with guide policy constraints is proposed to enable the optimizer network to converge to a feasible solution. Finally, experiments show that the proposed algorithm has better feasibility than other learning-based methods and can provide solutions much faster than traditional optimizers with similar optimality. Copyright © 2024, The Authors. All rights reserved.

关键词： Model predictive control

Application Design of Virtual Immersion Command Information System 9

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Application Design of Virtual Immersion Command Information ...

9th International Conference on Big Data and Information Analytics, BigDIA 2023

作者： Liu, Renjie Chen, Hongli Wang, Mingyang Zhu, Jian Zhang, Zhenqian Zhang, Ming Beijing Institute of Control and Electronic Technology Key Laboratory of Information System and Technology Beijing China China Industrial Control Systems Cyber Emergency Response Team Laboratory of Artificial Intelligence Integration Development and Security Application Beijing China

ISBN: (纸本)9798350330076

Based on the existing resources of general command information system, the virtually immersive display system is constructed. In this paper, the application design of virtual and augmented reality display system was discussed. The main contents included the overall system design, business requirements, application design of virtual reality (VR) and augmented reality (AR) table display system, expected results, and equipment selection. The deployment and application of VR and AR display system were introduced. Therefore, the hardware selection of VR and AR were HTC Vive and XLOOG X200 products, respectively. The Unreal Engine 4 was selected as the software development platform. This solution meets the current immersive display requirements. The system integrates VR and AR display application capabilities and can fully serve the command and control requirements in the future virtual field. © 2023 IEEE.

关键词： Virtual reality

MOVE: Multi-skill Omnidirectional Legged Locomotion with Limited View in 3D Environments

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arXiv 2024年

作者： Li, Songbo Luo, Shixin Wu, Jun Zhu, Qiuguo Institute of Cyber-Systems and Control Zhejiang University 310027 China State Key Laboratory of Industrial Control Technology 310027 China

Legged robots possess inherent advantages in traversing complex 3D terrains. However, previous work on low-cost quadruped robots with egocentric vision systems has been limited by a narrow front-facing view and exteroceptive noise, restricting omnidirectional mobility in such environments. While building a voxel map through a hierarchical structure can refine exteroception processing, it introduces significant computational overhead, noise, and delays. In this paper, we present MOVE, a one-stage end-to-end learning framework capable of multi-skill omnidirectional legged locomotion with limited view in 3D environments, just like what a real animal can do. When movement aligns with the robot's line of sight, exteroceptive perception enhances locomotion, enabling extreme climbing and leaping. When vision is obstructed or the direction of movement lies outside the robot's field of view, the robot relies on proprioception for tasks like crawling and climbing stairs. We integrate all these skills into a single neural network by introducing a pseudo-siamese network structure combining supervised and contrastive learning which helps the robot infer its surroundings beyond its field of view. Experiments in both simulations and real-world scenarios demonstrate the robustness of our method, broadening the operational environments for robotics with egocentric vision. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

A Time-delay Approach for Stabilization of Linear systems with Unknown control Direction

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A Time-delay Approach for Stabilization of Linear Systems wi...

IEEE Conference on Decision and control

作者： Gaofeng Pan Yang Zhu Zhengguang Wu State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou 310027 China and Ningbo Innovation Center Zhejiang University Ningbo China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

We propose a novel stability analysis for linear systems with unknown control directions, focusing on stabilization through extremum seeking to minimize a Lyapunov function. Our approach draws inspiration from Lie bracket systems, employing a transformation using time-delay analysis to convert the closed-loop system into a nominal time-delay system. Leveraging techniques from Input-to-state Stability (ISS), we provide a proof of semiglobal practical asymptotic stability. Compared to the preceding analysis based on Lie bracket systems, the use of time-delay methods transforms intricate mathematical discussions and proofs into a structured computational framework, facilitating a more intuitive understanding. Also, we propose a method for selecting controller parameters and can provide a more comprehensive explanation for the current high-gain parameter choices. Finally, we present a new control parameter to reduce the requirements of the frequency of the sinusoidal signals.

关键词： Linear systems Asymptotic stability Focusing Transforms Stability analysis Robustness Closed loop systems Frequency control Lyapunov methods

Toward Understanding key Estimation in Learning Robust Humanoid Locomotion

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arXiv 2024年

作者： Wang, Zhicheng Wei, Wandi Yu, Ruiqi Wu, Jun Zhu, Qiuguo Institute of Cyber-Systems and Control Zhejiang University 310027 China State Key Laboratory of Industrial Control Technology 310027 China

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. © 2024, CC BY.

关键词： Anthropomorphic robots

Walking with Terrain Reconstruction: Learning to Traverse Risky Sparse Footholds

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arXiv 2024年

作者： Yu, Ruiqi Wang, Qianshi Wang, Yizhen Wang, Zhicheng Wu, Jun Zhu, Qiuguo Institute of Cyber-Systems and Control Zhejiang University 310027 China State Key Laboratory of Industrial Control Technology 310027 China

Traversing risky terrains with sparse footholds presents significant challenges for legged robots, requiring precise foot placement in safe areas. To acquire comprehensive exteroceptive information, prior studies have employed motion capture systems or mapping techniques to generate heightmap for locomotion policy. However, these approaches require specialized pipelines and often introduce additional noise. While depth images from egocentric vision systems are cost-effective, their limited field of view and sparse information hinder the integration of terrain structure details into implicit features, which are essential for generating precise actions. In this paper, we demonstrate that end-to-end reinforcement learning relying solely on proprioception and depth images is capable of traversing risky terrains with high sparsity and randomness. Our method introduces local terrain reconstruction, leveraging the benefits of clear features and sufficient information from the heightmap, which serves as an intermediary for visual feature extraction and motion generation. This allows the policy to effectively represent and memorize critical terrain information. We deploy the proposed framework on a low-cost quadrupedal robot, achieving agile and adaptive locomotion across various challenging terrains and showcasing outstanding performance in real-world scenarios. Video at: ***/Rj9v5EZsn-M. Copyright © 2024, The Authors. All rights reserved.

关键词： Multipurpose robots