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Annual Conference of industrial Electronics Society

作者： Mengting Zhang Zhitao Liu Hongye Su State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

Dynamic wireless charging (DWC) of electric vehicles (EVs) is a promising technology that can promote the widespread of EVs. However, the output power fluctuation occurs due to the varying mutual inductance between the receiver coil and the transmitter coils caused by the EV motion, which would lead to control performance deterioration and even instability of the system. To mitigate the power fluctuation, this paper proposes a control strategy based on exact output regulation (EOR) theory for the receiver-side buck converter of the DWC system to compensate the disturbance arising from the mutual inductance fluctuation. The mutual inductance fluctuation can be approximated as a sinusoidal signal and the FEA analysis informanation is used to obtain an predefined exosystem that can characterize the dynamics of the mutual inductance fluctuation. A state feedback controller combined with a state observer is designed to achieve a constant output voltage of the DWC system by compensating for such disturbance. The simulation results verify the effectiveness and superiority of the proposed control strategy as compared with the traditional control methods.

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Autonomous Trajectory Planning Based on Bézier Curve with Curvature Constraints and Piecewise-Jerk Speed-Time Optimization

Autonomous Trajectory Planning Based on Bézier Curve with C...

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IEEE International Conference on Robotics and Biomimetics

作者： You Wang Ziyi Zou Ziang Zhang Xiaoqing Guan Boyu Lin Xiang Li Guang Li State Key Laboratory of Industrial Control Technology Institute of Cyber Systems and Control Zhejiang University Hangzhou China

In this paper, we propose an efficient trajectory planning algorithm with path smoothing based on the Bézier curve with curvature constraints and piecewise-jerk speed-time optimization. We use hybrid A* to generate a rough path and construct a safe corridor by inflating the path. After that, we formulate the smooth problem as a nonlinear programming(NLP) with piecewise Bézier curves. Since the curvature constraints for Bézier curves are difficult, we employ quartic Bézier Curves with special forms and compute the closed-form solution for the maximum curvature to simplify the representation of the maximum curvature. By using the special Bézier curves, we realize the gear shifts and easily guarantee the security, continuity, and feasibility of the path. Meanwhile, we add time variables based on PJSO, improving the quality of trajectory within an acceptable increase in time, making the allocation of time and speed better. Simulation and real-world experiments with a car-like robot in various environments confirm that our algorithm can generate a smooth, feasible, and high-quality trajectory for robots.

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Blind Image Despeckling Using a Multiscale Attention-Guided Neural Network

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第1期5卷 205-216页

作者： Guo, Yu Lu, Yuxu Liu, Ryan Wen Zhu, Fenghua Wuhan University of Technology School of Navigation Wuhan430063 China Wuhan University of Technology Chongqing Research Institute Chongqing401151 China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing100190 China

Coherent imaging systems have been applied in the detection of target of interest, natural resource exploration, ailment diagnosis, etc. However, it is easy to generate speckle-degraded images due to the coherent interference of reflected echoes, restricting these practical applications. Speckle noise is a granular interference that affects the observed reflectivity. It is often modeled as multiplicative noise with a negative exponential distribution. This nonlinear property makes despeckling of imaging data an intractable problem. To enhance the despeckling performance, we propose to blindly remove speckle noise using an intelligent computing-enabled multiscale attention-guided neural network (termed MSANN). In particular, we first introduce the logarithmic transformation to convert the multiplicative speckle noise model to an additive version. Our MSANN, essentially a feature pyramid network, is then exploited to restore degraded images in the logarithmic domain. To enhance the generalization ability of the MSANN, a multiscale feature enhancement attention module is incorporated into the MSANN to extract multiscale features for improving the imaging quality. A multiscale mixed loss function is further presented to increase the network robustness during training. The final despeckled images are naturally equivalent to the exponential versions of the output of the MSANN. Experimental results have shown that the MSANN has the capacity of effectively removing the speckle noise while preserving essential structures. It can achieve superior despeckling results in terms of visual quality and quantitative measures. © 2020 IEEE.

关键词： Additives

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Transformer-Based Macroscopic Regulation for High-Speed Railway Timetable Rescheduling

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IEEE/CAA Journal of Automatica Sinica 2023年第9期10卷 1822-1833页

作者： Wei Xu Chen Zhao Jie Cheng Yin Wang Yiqing Tang Tao Zhang Zhiming Yuan Yisheng Lv Fei-Yue Wang Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China Signal&Communication Research Institute China Academy Railway SciencesBeijing 100081China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049 State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China IEEE State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 Macao Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China

Unexpected delays in train operations can cause a cascade of negative consequences in a high-speed railway *** such cases,train timetables need to be ***,timely and efficient train timetable rescheduling is still a challenging problem due to its modeling difficulties and low optimization *** paper presents a Transformer-based macroscopic regulation approach which consists of two stages including Transformer-based modeling and policy-based ***,the relationship between various train schedules and operations is described by creating a macroscopic model with the Transformer,providing the better understanding of overall operation in the high-speed railway ***,a policy-based approach is used to solve a continuous decision problem after macro-modeling for fast *** experiments on various delay scenarios are *** results demonstrate the effectiveness of the proposed method in comparison to other popular methods.

关键词： High-speed railway reinforcement learning train timetable rescheduling Transformer

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A Time-delay Approach for Extremum Seeking of Nonlinear Static Maps:

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IFAC-PapersOnLine 2023年第1期56卷 258-263页

作者： Yang Zhu Emilia Fridman Institute of Cyber-Systems and Control State Key Laboratory of Industrial Control Technology Zhejiang University China School of Electrical Engineering Tel-Aviv University Israel

This paper develops a constructive time-delay approach to averaging for gradient-based extremum seeking (ES) control of nonlinear static maps of non-quadratic form. Under the assumption that some prior knowledge of the nonlinear map with its derivatives is available, for the first time, we derive a quantitative analysis for ES close-loop systems with upper bounds on the tuning parameter that preserves the exponential stability and on the convergence error of extremum seeking. By transforming the ES system into a time-delay neutral type system with distributed delays, the developed method gives an accurate perturbed system of ES without employing any approximate calculation, and suggests a direct Lyapunov-Krasovskii approach in the form of linear matrix inequalities (LMIs), for the transformed time-delay plant to derive efficient stability conditions.

关键词： Extremum Seeking Time-delay Nonlinear Static Non-quadratic

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Principal Trotter observation error with truncated commutators

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Physical Review A 2024年第6期110卷 062614-062614页

作者： Langyu Li Institute of Cyber-system and Control College of Control Science and Engineering Zhejiang University Hangzhou Zhejiang 310027 China and State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou Zhejiang 310027 China

Hamiltonian simulation is one of the most promising applications of quantum computers, and the product formula is one of the most important methods for this purpose. Previous related work has mainly focused on the worst-case, average-case, or actual-case scenarios. In this work we consider the simulation error under a fixed observable. Specifically, errors that commute with this observable become less significant. To illustrate this point, we define the observation error as the expectation under the observable and provide a commutativity-based upper bound using the Baker-Campbell-Hausdorff formula. Furthermore, we observe that the evolution order plays a critical role in determining the observation error. By leveraging a simulated annealing algorithm, we design an evolution order optimization algorithm, to identify an order that enhances commutativity with the observable, where the observation error indicated by this upper bound can be significantly compressed. In the experiment with the Heisenberg model, the observation bound compresses the Trotter number by nearly half compared to recent commutator bounds. The experiment on the hydrogen molecule Hamiltonian demonstrates that optimizing the order can lead to nearly half the reduction in the Trotter number.

关键词： Hamiltonians

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Accelerated Distributed Aggregative Optimization

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IEEE Transactions on Automatic control 2025年

作者： Liu, Jiaxu Chen, Song Cai, Shengze Xu, Chao Chu, Jian Zhejiang University School of Mathematical Sciences Zhejiang Hangzhou310027 China Zhejiang University State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang Hangzhou310027 China

This paper delves into the investigation of a distributed aggregative optimization problem within a network. In this scenario, each agent possesses its own local cost function, which relies not only on the local state variable but also on an aggregated function of state variables from all agents. To expedite the optimization process, we amalgamate the heavy ball and Nesterov's accelerated method with distributed aggregative gradient tracking, resulting in the proposal of two innovative algorithms, aimed at resolving the distributed aggregative optimization problem. Our analysis demonstrates that the proposed algorithms can converge to an optimal solution at a global linear convergence rate when the objective function is strongly convex with the Lipschitz-continuous gradient, and when the parameters (e.g., step size and momentum coefficients) are chosen within specific ranges. Additionally, we present several numerical experiments to verify the effectiveness, robustness and superiority of our proposed algorithms. © 2025 IEEE.

关键词： Optimization algorithms

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BEV-ODOM: Reducing Scale Drift in Monocular Visual Odometry with BEV Representation

arXiv

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arXiv 2024年

作者： Wei, Yufei Lu, Sha Han, Fuzhang Xiong, Rong Wang, Yue Laboratory of Industrial Control and Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310058 China

Monocular visual odometry (MVO) is vital in autonomous navigation and robotics, providing a cost-effective and flexible motion tracking solution, but the inherent scale ambiguity in monocular setups often leads to cumulative errors over time. In this paper, we present BEV-ODOM, a novel MVO framework leveraging the Bird's Eye View (BEV) Representation to address scale drift. Unlike existing approaches, BEVODOM integrates a depth-based perspective-view (PV) to BEV encoder, a correlation feature extraction neck, and a CNNMLP- based decoder, enabling it to estimate motion across three degrees of freedom without the need for depth supervision or complex optimization techniques. Our framework reduces scale drift in long-term sequences and achieves accurate motion estimation across various datasets, including NCLT, Oxford, and KITTI. The results indicate that BEV-ODOM outperforms current MVO methods, demonstrating reduced scale drift and higher accuracy. © 2024, CC BY-NC-SA.

关键词： Motion estimation

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Multi-Task Learning-Enabled Automatic Vessel Draft Reading for Intelligent Maritime Surveillance

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IEEE Transactions on Intelligent Transportation systems 2024年第5期25卷 4552-4564页

作者： Qu, Jingxiang Liu, Ryan Wen Zhao, Chenjie Guo, Yu Xu, Sendren Sheng-Dong Zhu, Fenghua Lv, Yisheng Wuhan University of Technology Hubei Key Laboratory of Inland Shipping Technology School of Navigation Wuhan430063 China State Key Laboratory of Maritime Technology and Safety Wuhan430063 China National Taiwan University of Science and Technology Graduate Institute of Automation and Control and the Advanced Manufacturing Research Center Taipei10617 Taiwan Institute of Automation State Key Laboratory of Multimodal Artificial Intelligence Systems Chinese Academy of Sciences Beijing100190 China

The accurate and efficient vessel draft reading (VDR) is an important component of intelligent maritime surveillance, which could be exploited to assist in judging whether the vessel is normally loaded or overloaded. The computer vision technique with an excellent price-to-performance ratio has become a popular medium to estimate vessel draft depth. However, the traditional estimation methods easily suffer from several limitations, such as sensitivity to low-quality images, high computational cost, etc. In this work, we propose a multi-task learning-enabled computational method (termed MTL-VDR) for generating highly reliable VDR. In particular, our MTL-VDR mainly consists of four components, i.e., draft mark detection, draft scale recognition, vessel/water segmentation, and final draft depth estimation. We first construct a benchmark dataset related to draft mark detection and employ a powerful and efficient convolutional neural network to accurately perform the detection task. The multi-task learning method is then proposed for simultaneous draft scale recognition and vessel/water segmentation. To obtain more robust VDR under complex conditions (e.g., damaged and stained scales, etc.), the accurate draft scales are generated by an automatic correction method, which is presented based on the spatial distribution rules of draft scales. Finally, an adaptive computational method is exploited to yield an accurate and robust draft depth. Extensive experiments have been implemented on the realistic dataset to compare our MTL-VDR with state-of-the-art methods. The results have demonstrated its superior performance in terms of accuracy, robustness, and efficiency. The computational speed exceeds 40 FPS, which satisfies the requirements of real-time maritime surveillance to guarantee vessel traffic safety. © 2000-2011 IEEE.

关键词： Real time systems

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Learning adaptive manipulation of objects with revolute joint: A case study on varied cabinet doors opening

arXiv

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arXiv 2023年

作者： Yu, Hongxiang Guo, Dashun Zhou, Zhongxiang Wang, Yue Xiong, Rong The State Key Laboratory of Industrial Control and Technology The Institute of Cyber-Systems and Control Zhejiang University Hangzhou310058 China

This paper introduces a learning-based framework for robot adaptive manipulating the object with a revolute joint in unstructured environments. We concentrate our discussion on various cabinet door opening tasks. To improve the performance of Deep Reinforcement Learning in this scene, we analytically provide an efficient sampling manner utilizing the constraints of the objects. To open various kinds of doors, we add encoded environment parameters that define the various environments to the input of out policy. To transfer the policy into the real world, we train an adaptation module in simulation and fine-tune the adaptation module to cut down the impact of the policy-unaware environment parameters. We design a series of experiments to validate the efficacy of our framework. Additionally, we testify to the model's performance in the real world compared to the traditional door opening method. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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