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International Conference on Intelligent Transportation

作者： Zixuan Zhang Yuan Cao Shuai Su Junlin Zhang Wei Wang School of Electric and Information Engineering Beijing Jiaotong University Beijing China State Key Laboratory of Rail Traffic Control and Safety and the Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University Beijing China Traffic Control Technology Co. Ltd Beijing China Department of Automation Traffic Control Technology Co. Ltd Tsinghua University Beijing China

The integrated control of separated power electric multiple units (EMUs) and cars is a significant research field, the cars cooperative control problem of the separated power EMUs is investigated in this paper. Firstly, assuming that the separated power EMUs are composed of different numbers of powered cars and trailer cars connected by elastic couplers, and considering the electric traction/braking force, air braking force, and nonlinear basic resistance, a multi-particle model is established. Then a novel cost function is designed considering speed and position tracking errors, in-train force, traction energy consumption, and regenerative energy. Based on the model predictive control (MPC) methodology, a cooperative control approach is proposed to optimize the operation performance of separated power EMUs. The proposed control approach can improve EMUs tracking accuracy, and reduce internal force and net energy consumption during EMUs operation. Finally, the proposed approach is verified by numerical simulations.

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Knowledge-Based Scene Text Recognition for Industrial Applications

Knowledge-Based Scene Text Recognition for Industrial Applic...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Guowei Deng Jingzheng Tu Cailian Chen Jianping He Xinyi Le Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Industrial Intelligent Control and Management Shanghai China

ISBN: (纸本)9781728119496

Scene text recognition (STR) methods combined with semantic information have made great progress to recognize texts in natural scenes, most of which are daily words. However, research on mining semantic information in industrial texts attracts less attention. Since industrial texts follow a different semantic pattern defined by industry standards, it challenges many existing methods to conduct accurate semantic reasoning. In this paper, we abstract the industry standards into two aspects of prior knowledge: the grouping property and the prior lexicon. Correspondingly, a knowledge-based language model is proposed with several group-wise correlation modules and a lexicon-based reasoning module to learn semantic rules from both data and the prior knowledge. Besides, we transfer the prior knowledge into data by generating synthetic pure text datasets according to the industry standards’ rules, which introduces more prior knowledge to the language model. Furthermore, a novel STR framework is presented by combining the knowledge-based language model and an attention-based vision model. For evaluation, two industrial text datasets called CIN and SB are collected from real-world industrial field surveillance. Experiments indicate that our method’s word-level accuracy outperforms state-of-the-art methods with 15% and 10.61% on CIN and SB datasets respectively.

关键词： Industries Correlation Text recognition Surveillance Semantics Knowledge based systems Data models

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Evolutionary dynamics with temporal higher-order interactions

arXiv

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arXiv 2023年

作者： Wang, Xiaochen Zhou, Lei Tian, Zhenglong Li, Aming Center for Systems and Control College of Engineering Peking University Beijing100871 China School of Automation Beijing Institute of Technology Beijing100081 China Center for Multi-Agent Research Institute for Artificial Intelligence Peking University Beijing100871 China

Humans interact and cooperate in structured societies, which are often represented by complex networks. Previous explorations mainly focus on pairwise and static network structures, where each link connects two nodes permanently and never changes. However, empirical human collective interactions go beyond static time-invariant one-to-one relationships. Recently, researchers have made vital progress in modelling higher-order interactions using hypernetworks, where a link may connect more than two individuals. Here, we study collective cooperation through temporal hypernetworks, capturing the time-varying higher-order interactions in human societies. We find that temporal hypernetworks may promote cooperation compared with their static counterparts, and the traditional static pairwise network may underestimate the positive effect of local interactions on fostering cooperators. Moreover, temporal hypernetworks with sparse components and higher-order interactions can facilitate cooperation. Surprisingly, we report that when the scale of interactions is of the same order as population size, moderately small hyperlink sizes best facilitate cooperation. Our findings underscore the significant impact of real-world temporal higher-order interactions on the evolution of cooperation. © 2023, CC BY.

关键词： Complex networks

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Mixed Fault-Tolerant Sampled-Data Fuzzy control for Nonlinear Parabolic PDE Systems Under Deception Attacks

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International Journal of Fuzzy Systems 2022年第8期24卷 3513-3531页

作者： Li, Qian-Qian Wang, Zi-Peng Wu, Huai-Ning Li, Yueyang Li, Han-Xiong School of Electrical Engineering University of Jinan Jinan China Faculty of Information Technology Beijing University of Technology Beijing China Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical Engineering Beihang University Beijing China Department of Systems Engineering and Engineering Management City University of Hong Kong Kowloon Tong Hong Kong

This paper addresses mixed $$H_2/H_\infty$$ fault-tolerant sampled-data (SD) fuzzy control for nonlinear space-varying parabolic partial differential equation (PDE) system under deception attacks. Firstly, a T–S fuzzy PDE model is given to exactly describe the nonlinear space-varying parabolic PDE system. Secondly, a fault-tolerant SD fuzzy controller via the spatial linear matrix inequalities (SLMIs) is developed based on a Lyapunov functional which is continuous at sampling times but not necessary to be positive definite in sampling intervals such that the closed-loop PDE system is exponentially stable with a mixed $$H_2/H_\infty$$ performance. Then, to solve the SLMIs, the fault-tolerant SD fuzzy control problem for space-varying parabolic PDE system is formulated as linear matrix inequality feasibility problem. Furthermore, the design condition of the suboptimal mixed $$H_2/H_\infty$$ fault-tolerant SD controller subject to deception attacks can be derived by considering the property of membership functions. Lastly, two examples are given to illustrate the design method.

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WDNet: A Water Depth Perception Network with Global Information

WDNet: A Water Depth Perception Network with Global Informat...

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Chinese control Conference (CCC)

作者： Zhihui Huang Yifeng Tang Chuancong Tang Yifan Xu ChuanShang Luo Bin Liu Hai-Tao Zhang the Key Laboratory of Image Processing and Intelligent Control and the State Key Laboratory of Digital Manufacturing Equipment and Technology School of Artificial Intelligence and Automation the Engineering Research Center of Autonomous Intelligent Unmanned Systems Huazhong University of Science and Technology Wuhan China

Deep perception of the unmanned surface vehicle's surroundings is an inaccessible part of its fully autonomous navigation mission. The existing methods, whether based on traditional stereo matching or deep learning, do not fully consider the characteristics of water environment, resulting in severe error depths in weak textures (sky, calm lake) and water reflections regions, that increases the risk of running aground or collision. What is worse that there is not a public dataset for depth estimation in the water environment. Therefore, this work proposes a self-supervised model for depth estimation named Water Depth Perception Network (WDNet) to address these problems. The decoder of this network has a wider receptive field and can effectively handle the depth error in the weak texture region. Besides, the WDNet is trained with a novel and effective loss function which assist the network to reduce errors in sky and water region, and some indexes are proposed to evaluate the model's performances in sky and water region. Finally, our proposed WDNet achieves a 0.1056 absolute relative error in ranging, the average number of error pixels in the sky area drops from 15803.87 to 580.91, which only accounted for 0.29% of the image, and the error in water region drops from 51.04 to 6.75, all of them are superior to the performance of baseline model.

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Koopman Operator Learning control of the Piezoelectric Actuator

Koopman Operator Learning Control of the Piezoelectric Actua...

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Chinese control Conference (CCC)

作者： Xiang Huang Hai-Tao Zhang Engineering Research Center of Autonomous Intelligent Unmanned Systems the Key Laboratory of Image Processing and Intelligent Control and the State Key Laboratory of Digital Manufacturing Equipment and Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

The piezoelectric actuator is one kind of device that can drive nanoscale motion. However, the nonlinear hysteresis effect induced by its natural material greatly degrades its positioning accuracy. To handle this challenging issue, this work develops a Koopman model predict control (Koopman- MPC) framework for the piezoelectric actuator. Specifically, the Koopman operator theory is adapted for modeling the piezoelectric actuator dynamics. A simple yet powerful linear model spanned in a high-dimensional space is thus constructed to characterize the hysteresis dynamics. Subsequently, upon the established Koopman model, an MPC scheme is put forward for tracking control of piezoelectric actuators. Therein, by sustained optimizing a cost function containing future outputs and control increments, the control input is obtained. Moreover, extensive tracking simulations are carried out on a simulated piezoelectric actuator for verifying the feasibility and effectiveness of the Koopman- Mpc scheme.

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Discrete-Time Implementation of an Adaptive Gain First-Order Sliding Mode control Law

Discrete-Time Implementation of an Adaptive Gain First-Order...

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IEEE Conference on Decision and control

作者： Lars Watermann Lukas Eisenzopf Stefan Koch Markus Reichhartinger Martin Horn Johann Reger Control Engineering Group Technische Universität Ilmenau Ilmenau Germany Institute of Automation and Control Graz University of Technology Graz Austria

ISBN: (纸本)9781665436601

A discrete-time implementation of a continuous-time adaptive gain sliding mode control law for a system with matched disturbance is presented. The discrete-time control algorithm is derived from the solution of the nominal continuous-time closed-loop dynamics. This approach ensures elimination of discretization chattering as well as proper disturbance rejection. Slight modifications of the resulting discrete-time control law are proposed to guarantee ultimate boundedness of the sliding variable and the adaptive gain which is formally proven by means of Lyapunov arguments. Prevention of discretization chattering and disturbance attenuation properties are validated in a simulation and compared to other approaches.

关键词： Adaptation models Upper bound Adaptive systems Heuristic algorithms Conferences Attenuation Sliding mode control

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Event-triggered Consensus control of Heterogeneous Multi-agent Systems: Model- and Data-based Analysis

arXiv

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arXiv 2022年

作者： Wang, Xin Sun, Jian Wang, Gang Chen, Jie The School of Automation The Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing10081 China The Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China The Department of Control Science and Engineering Tongji University Shanghai201804 China The State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China

This article deals with model- and data-based consensus control of heterogenous leader-following multi-agent systems (MASs) under an event-triggering transmission scheme. A dynamic periodic transmission protocol is developed to significantly alleviate the transmission frequency and computational burden, where the followers can interact locally with each other approaching the dynamics of the leader. Capitalizing on a discrete-time looped-functional, a model-based consensus condition for the closed-loop MASs is derived in form of linear matrix inequalities (LMIs), as well as a design method for obtaining the distributed controllers and event-triggering parameters. Upon collecting noise-corrupted state-input measurements during open-loop operation, a data-driven leader-following MAS representation is presented, and employed to solve the data-driven consensus control problem without requiring any knowledge of the agents' models. This result is then extended to the case of guaranteeing an H∞ performance. A simulation example is finally given to corroborate the efficacy of the proposed distributed event-triggering scheme in cutting off data transmissions and the data-driven design method. Copyright © 2022, The Authors. All rights reserved.

关键词： Linear matrix inequalities

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USV Path Planning Based on ACO-SA Algorithm

USV Path Planning Based on ACO-SA Algorithm

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Chinese control Conference (CCC)

作者： Chuanshang Luo Yifeng Tang Zhihui Huang Yifan Xu Bin Liu Hai-Tao Zhang the Engineering Research Center of Autonomous Intelligent Unmanned Systems the Key Laboratory of Image Processing and Intelligent Control and the State Key Laboratory of Digital Manufacturing Equipment and Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

This paper solves USV path planning problem constrained by multiple factors via ant-colony optimization algorithm. First, this paper uses the ways of multi-objective optimization to model the USV path planning problem. An improved ant colony algorithm named ACO-SA is put forward afterwards to effectively solve the problem. The algorithm is a combination of ACO algorithm(ant colony algorithm) and SA algorithm(simulated annealing algorithm), which has three improments: change the initial distribution of pheromone to guide the search when the algorithm has just started running; change the heuristic function and state transition probability taking three factors into consideration; change the pheromone update rule and make the ants compete for the right to update pheromone by simulated annealing algorithm, and update the best solution by the same algorithm. Finally, simulation experiment and field experiment are conducted to check the validity of ACO-SA algorithm.

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Hand Detection and Gesture Classification based on Transformer

Hand Detection and Gesture Classification based on Transform...

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Chinese automation Congress (CAC)

作者： Genghui Pei Chi Xu Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Automation China University of Geosciences Engineering Research Center of Intelligent Technology for Geo-Exploration Ministy of Education Wuhan China Engineering Research Center of Intelligent Technology for Geo-Geo-Exploration Ministy of Education Wuhan China

Hand detection and gesture recognition are an important branch of artificial intelligence, which are widely used in daily life, such as interaction between human and machines, communication using signals, virtual reality and so on. In order to realize gesture recognition in unconstrained situations, we propose a novel end-to-end model based on Transformer, which can detect multiple hands in a picture. Our network can be roughly divided into the following three stages. In the first stage, we will extract feature maps of different layers by convolutional network from monocular RGB images. In the second stage, features are sent into the codec module based on Transformer, and hand detection is further carried out through multi-layer perceptrons with the decoding vectors as the input. In the third stage, we combine the attention weight maps obtained in the second step with the features of different layers in the first step to complete the gesture recognition task. From the experiment results, our network accurately identifies the positions of multiple hands in the picture and correctly recognizes hand gestures.

关键词： Solid modeling Codecs automation Gesture recognition Virtual reality Transformers Feature extraction

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