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IEEE International Conference on Robotics and Biomimetics

作者： Hongye Wang Kefan Jin Hesheng Wang Shanghai Jiao Tong University China MOE Key Laboratory of Marine Intelligent Equipment and System State Key Laboratory of Ocean Engineering Shanghai Jiao Tong University China Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781665405362

Learning-based self-driving techniques and vehicle navigation approaches are the most hot topics in recent years. However, existing approaches typically assume the attack-free sensor data, the safety issue under large disturbance or external attack have not been well-solved. In this article, we build a simple FGSM-based attack method designed by minimizing the maximum value of the extracted visual features in order to greatly decrease the performance of the popular learning model for vehicle navigation and even make it totally fail. The proposed min-max operation based feature space attack method solves the problems of branch activation uncertainties and the lack of labels. Furthermore, we also provide a general adversarial training framework which can be used to overcome the proposed feature space attack. Simulational experiments in CARLA platform demonstrate the effectiveness and practical applicability of our attacking approach and defence strategy.

关键词： Training Space vehicles Visualization Uncertainty Navigation Feature extraction Robustness

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An Improved Method for Approximating the Infinite-horizon Value Function of the Discrete-time Switched LQR Problem

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IFAC-PapersOnLine 2023年第2期56卷 4138-4143页

作者： Tan Hou Yuanlong Li Zongli Lin Department of Automation Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240 China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia P.O. Box 400743 Charlottesville VA 22904-4743 U.S.A.

This paper considers the problem of approximating the infinite-horizon value function of the discrete-time switched LQR problem. In particular, we propose a new value iteration method to generate a sequence of monotonically decreasing functions that converges exponentially to the value function. This method facilitates us to use coarse approximations resulting from faster but less accurate algorithms for further value iteration, and thus, our method is capable of achieving a better approximation for a given computation time compared with the existing methods. Two numerical examples are presented in this paper to illustrate the effectiveness of our method.

关键词： Switched linear systems optimal control switched LQR problem

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Quantum gyroscopes based on double-mode surface-acoustic-wave cavities

arXiv

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arXiv 2024年

作者： Zhu, Yuting Xue, Shibei Ju, Fangfang Yuan, Haidong Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Shatin Hong Kong School of Physics and Electronics Hunan Normal University Changsha410081 China

Recent progress shows that a surface-acoustic-wave (SAW) cavity can not only induce quantum acoustic dynamics but also can form optomechanical-like systems. Its operating frequencies in the microwave band make it resistant to the thermal noise of surrounding environments, while its radiation-pressure couplings make it susceptible to weak forces. Based on these advantages, we propose a gyroscope comprising coupled microwave-SAW cavities. In this paper, we systematically consider the three indices including range, signal-to-noise ratio, and sensitivity, which are the most important to gyroscopes but only partially considered in existing works. Additionally, we establish the fundamental limits of sensitivity when the quantum input is in the vacuum state and the squeezed vacuum state. We find that squeezing improves sensitivity and can surpass the standard quantum limit. However, this improvement can only reach up to √2/2 even as the squeezed parameter approaches infinity, which is rarely noted in recent works. Finally, we also offer analytical constraints for cooperativity and squeezed parameters. These constraints can be utilized to design gyroscopes based on coupled cavities in experiments. Copyright © 2024, The Authors. All rights reserved.

关键词： Gyroscopes

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An adaptive fault detection strategy for Cz crystal growth process

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Mu, Lingxia Feng, Nan Zhang, Zhenhua Zhang, Youmin Zhang, Jian Xian University of Technology Crystal Growth Equipment and Systems Integration National Engineering Research Center Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xian710048 China University of Science and Technology Beijing School of Intelligence Science and Technology Beijing100083 China Concordia University Department of Mechanical Industrial and Aerospace Engineering MontrealQCH3G 1M8 Canada

The monitoring and fault detection for the Czochralski (Cz) crystal growth process is important to ensure the quality of the produced mono-crystalline silicon. The Cz process is a typical multi-modes industrial process, which makes the detection difficult in distinguish the change caused by the fault or mode switching. In this paper, an adaptive fault detection strategy for Cz crystal growth process is proposed. Firstly, quality-related variables are selected from numerous process variables, based on which the training dataset is constructed. Then, a fault detection model is built based on slowly-varying feature and statics indicator. A model switching strategy and updating algorithm are designed to handle the abnormal situation caused by the mode switching or fault occurrence. Three representative experiments are conducted to verify the performance of the proposed strategy, including numerical cases, a publicly available dataset, and Cz crystal growth process from our laboratory. © 1963-2012 IEEE.

关键词： Crystal growth from melt

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Lite-HRPE: A 6DoF Object Pose Estimation Method for Resource-Limited Platforms

Lite-HRPE: A 6DoF Object Pose Estimation Method for Resource...

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International Conference on control and Automation (ICCA)

作者： Xin Liu Qi Guan Shibei Xue Dezong Zhao Department of Automation Shanghai Jiao Tong University Shanghai P. R. China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai P. R. China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai P. R. China James Watt School of Engineering University of Glasgow Glasgow U.K.

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

Accurately estimating the six-degree-of-freedom pose of objects is essential for intelligent robotics. Although significant progress has been made in this area, most studies fail to account for specific hardware limitations for model deployment, which remains a major challenge for resource-constrained scenarios. To address this issue, we propose Lite-HRPE, a lightweight RGB-based pose estimation method, which leverages a multi-branch parallel structure to extract spatial and semantic information of key points for pose estimation. Additionally, Lite-HRPE adopts the G-block and G-neck modular structure and streamlines the original feature extraction network to realize a compact network structure. This allows Lite-HRPE to strike a balance between pose estimation accuracy, parameter count, computational load, and runtime speed. Our evaluation on public datasets shows that Lite-HRPE achieves a 95.7% accuracy with only 10.8% of the number of parameters and 11.8% of the FLOPs compared to Hybridpose.

关键词： Training Accuracy Runtime Pose estimation Semantics Feature extraction Real-time systems

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A Deep Reinforcement Learning Bidding Algorithm on Electricity Market

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Journal of Thermal Science 2020年第5期29卷 1125-1134页

作者： JIA Shuai GAN Zhongxue XI Yugeng LI Dewei XUE Shibei WANG Limin Department of Automation Key Laboratory of System Control and Information ProcessingShanghai Jiao Tong UniversityShanghai 200240China State Key Laboratory of Coal-based Low-carbon Energy ENN Science and Technology Development Co.Ltd.Langfang 065001China ENN Energy Power Technology(Shanghai)Co. Ltd.Shanghai 201306China

In this paper,we design a new bidding algorithm by employing a deep reinforcement learning *** use the proposed algorithm to estimate conjectural variation of the other firms and then employ this variable to generate the optimal bidding strategy so as to pursue maximal *** this algorithm,electricity generation firms can improve the accuracy of conjectural variations of competitors by dynamically learning in an electricity market with incomplete *** market will reach an equilibrium point when electricity firms adopt the proposed bidding algorithm for a repeated game of power *** simulation examples illustrate the overall energy efficiency of power network will increase by 9.90%as the market clearing price decreasing when all companies use the *** simulation examples also show that the power demand elasticity has a positive effect on the convergence of learning process.

关键词： electricity market reinforcement learning energy efficiency conjectural variation bidding strategy

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Streaming-Data-Driven Distributionally Robust Joint Operation of Multi-Microgrids and Off-Site Hydrogen Refueling Stations under Uncertainties

Streaming-Data-Driven Distributionally Robust Joint Operatio...

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IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia)

作者： Longyan Li Chao Ning Haifeng Qiu Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China School of Electrical & Electronic Engineering Nanyang Technological University Singapore

ISBN: (纸本)9798350399677

This paper proposes a novel online-learning-enabled hierarchical distributionally robust energy management framework for multi-microgrids (MMGs) with off-site hydrogen refueling stations (HRSs). The proposed framework integrates day-ahead scheduling, intra-day rescheduling and real-time control. The day-ahead scheduling is implemented via a central energy management system for optimal joint scheduling of the MMG systems and off-site HRSs, then the intra-day rescheduling is carried out to optimize the real-time operation and to generate the reference for local controllers. To adequately characterize the statistical properties of renewable and load uncertainties from data streams, we leverage a Dirichlet process mixture model to construct an online-update ambiguity set, which encodes multimodality and local moment information. Based upon this ambiguity set, an online-learning distributionally robust model predictive control (OL-DRMPC) is developed to enhance the tracking performance of energy storage references and to reduce conservatism while guaranteeing probabilistic operational constraints. Its effectiveness is verified in a case study.

关键词： Renewable energy sources Uncertainty Hydrogen Asia Mixture models Probabilistic logic Real-time systems

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Efficient Planning for Object Search Task Based on Hierarchical POMDP

Efficient Planning for Object Search Task Based on Hierarchi...

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IEEE International Conference on Robotics and Biomimetics

作者： Wenrui Zhao Weidong Chen Institute of Medical Robotics Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China

ISBN: (纸本)9781665405362

Searching for a target object in clutter by a robot needs to solve perception uncertainty due to occlusion among objects, the robot has to move objects to gather information about where the target may locate. Meanwhile, we hope that the robot could execute as few actions as possible to get the target. Partially observable Markov decision process (POMDP) is proper to model this task, but it is hard to solve for large scale problems. And action feasibility of robotic manipulation must be identified by motion planning, which is time-consuming. We adopt hierarchical POMDP to reduce the complexity of POMDP planning. Besides, we propose methods to reduce the times of motion planning, the key idea is assigning reward to actions related to feasibility probability, instead of identifying action feasibility at the beginning. Simulations under various scenes show that our methods are effective and efficient.

关键词： Uncertainty Conferences Biomimetics Markov processes Search problems Planning Complexity theory

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Robust Adatpive Sliding Mode Tracking control for an AUV

Robust Adatpive Sliding Mode Tracking Control for an AUV

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Chinese Automation Congress (CAC)

作者： Jiaqi Zheng Wenbin Yu Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9781665426473

ISBN: (纸本)9781665426480

In our work, the research on trajectory tracking with time-varying disturbances is investigated via robust adaptive sliding mode control (RASMC). We first design a nonsingular sliding mode surface for tracking error system and show that the sliding mode surface is continuous. Then, since the underwater environment is very complex and the AUV’s mathematical model is strongly nonlinear and highly coupled with uncertainties, we propose the robust adaptive law to ensure the superior robust performance of AUV system. The obtained results suggest that the proposed controller can reduce chattering and tracking error by designing a nonsingular sliding mode surface.

关键词： Uncertainty Automation Adaptive systems Trajectory tracking Robustness Mathematical models Task analysis

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Robust Simultaneous Tracking and Local Dense Structured Mapping at Scenes lack of Geometric Features

Robust Simultaneous Tracking and Local Dense Structured Mapp...

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International Conference on Mechatronics and Machine Vision in Practice (M2VIP)

作者： Yueling Shen Zhijian Qiao Xu Wang Hesheng Wang Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781665431545

In this paper, we present a simultaneous tracking and dense structured mapping method aimed at robotic tasks including obstacle avoidance, path planning and manipulation. This is achieved through (1) sparse-to-dense pose estimation with both keypoints and dense vertex images for keeping robust at scenes lack of geometric features, (2) dense local map algorithm, which clears map outside the neighborhood of the robot for the consistency of the system at large-scale scenes, (3) structured analysis to obtain elevation and norms of surfaces, (4) the proposed dynamic mode for ghosting-free mapping in dynamic scenes. Experiments are conducted to evaluate each part of the system. The effectiveness and efficiency are proved by quantitative experiments.

关键词： Meters Surface reconstruction Three-dimensional displays Mechatronics Heuristic algorithms Pose estimation Path planning

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