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第40届中国控制会议

作者： Yuyu Liu Ding Liu Lu Kang National & Local Engineering Research Center of Crystal Growth Equipment and System Integration Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology

Different power sources in Seawater Pumping-Storing/Wind/Solar Hybrid Power Generating system supply power to the island *** order to ensure the reliability of the power supplying and improve the economical efficiency of the island micro-grid system at the same time,the output of each power source in the island micro-grid system needs to be optimized and dispatched according to historical wind power and photo-voltaic *** this paper,the mathematical model of Seawater Pumping-Storing/Wind/Solar Hybrid Power Generating system is established,and the one-time cost,*** the joint system are taken as the objective *** immune particle swarm algorithm is used to figure out the optimal output of each power source in the system after considering the restrictions including power balance of the system,the power constraints of each generator set and the online power of wind power and *** example shows that the optimization result of improved PSO algorithm is better than the original one,and it can also achieve the optimized dispatching of the joint system economically and effectively.

关键词： Seawater pumped-storage power station IPSO Optimized Dispatching

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Initial Alignment Method for SINS of UAV Based on Digital Filter Pretreatment

Initial Alignment Method for SINS of UAV Based on Digital Fi...

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Chinese Automation Congress (CAC)

作者： Wenbo Yang Jie Xu Shaoyuan Li Jianjun Wang Zhifeng Wang Shanghai Polytechnic University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781665426480

The two-stage method, which is divided into coarse alignment and fine alignment, is a traditional initial alignment method for SINS (Strapdown inertial navigation system, SINS). First, the coarse alignment is carried out based on the transformation relationship between the geographic information of the earth and the output of inertial measurement unit, and then the fine alignment is carried out based on optimal estimation. However, this method can mainly solve the alignment problem under angular interference. Aiming at in-motion alignment for SINS of UAVs (Unmanned aerial vehicle, UAV) under linear interference, this paper introduced the idea of digital filter and designed the digital filter by analyzing the frequency characteristic of useful signal. Then the reference velocity information is filtered to realize isolation of linear interference at specific frequency. Thus, the in-motion alignment problem under the combined action of angular interference and linear interference is effectively solved. Finally, numerical simulation results show the effectiveness of the above method.

关键词： Maximum likelihood detection Measurement units Interference IIR filters Nonlinear filters Inertial navigation Autonomous aerial vehicles

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A power optimization control method based on IMC for 3D printer

A power optimization control method based on IMC for 3D prin...

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2020 International Conference on Mechatronics Technology and Intelligent Manufacturing, ICMTIM 2020

作者： Xie, Ruidong Yang, Haichuan Yi, Yingmin Key Lab of Manufacturing Equipment of Shaanxi Province Xi'an University of Technology Xi'an710048 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an710048 China

Aiming at the effect of selective laser sintering technology used in 3D printing, the recursive least squares method was applied to fitting a control system model for quantification the relationship between laser power and corresponding temperature in this paper. The internal model control method is considered based on the establishing model. The controller is put into the actual 3D printing power control system for controlling the laser power. In the experiment, both internal model controller and the PID controller for the 3D printer are researched and deployed for comparing the their effects in the paper. The experimental result illustrates that the IMC based on the recursive least squares method is of effectiveness. © 2020 Institute of Physics Publishing. All rights reserved.

关键词： 3D printers

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Reinforcement Learning based Trajectory Planning for Autonomous Vehicles

Reinforcement Learning based Trajectory Planning for Autonom...

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Chinese Automation Congress (CAC)

作者： Zhaoxu Wang Jingzheng Tu Cailian Chen Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai P. R. China

ISBN: (纸本)9781665426480

The trajectory planning of autonomous vehicles requires making safe sequential decisions instantaneously. The most significant challenge is the uncertainties brought by complex interactions with other road users in diverse driving scenarios. Previous rule-based solutions lack generalization and only suit for limited simple environments. Moreover, another approach generates feasible action sequences based on motion planning, which ignores the interactions between vehicle participants. This paper proposes a hierarchical framework based on reinforcement learning for autonomous driving. Instead of directly mapping the sensor information to the low-level control signal, we first choose a desired state and generate a target trajectory in a Frenet frame according to the desired state. Then a low-level controller is used to track the target trajectory. A reinforcement learning algorithm combining planning and learning is introduced to choose the desired state. This trajectory planning framework is evaluated on a lane switching case in a simulated environment. The simulation results demonstrate the effectiveness of the proposed method.

关键词： Uncertainty Target tracking Trajectory planning Simulation Roads Reinforcement learning Switches

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Threshold-adaptive Unsupervised Focal Loss for Domain Adaptation of Semantic Segmentation

arXiv

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arXiv 2022年

作者： Yan, Weihao Qian, Yeqiang Wang, Chunxiang Yang, Ming The Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China University of Michigan-Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University Shanghai200240 China

Semantic segmentation is an important task for intelligent vehicles to understand the environment. Current deep learning methods require large amounts of labeled data for training. Manual annotation is expensive, while simulators can provide accurate annotations. However, the performance of the semantic segmentation model trained with the data of the simulator will significantly decrease when applied in the actual scene. Unsupervised domain adaptation (UDA) for semantic segmentation has recently gained increasing research attention, aiming to reduce the domain gap and improve the performance on the target domain. In this paper, we propose a novel two-stage entropy-based UDA method for semantic segmentation. In stage one, we design a threshold-adaptative unsupervised focal loss to regularize the prediction in the target domain, which has a mild gradient neutralization mechanism and mitigates the problem that hard samples are barely optimized in entropy-based methods. In stage two, we introduce a data augmentation method named cross-domain image mixing (CIM) to bridge the semantic knowledge from two domains. Our method achieves state-of-the-art 58.4% and 59.6% mIoUs on SYNTHIA-to-Cityscapes and GTA5-to-Cityscapes using DeepLabV2 and competitive performance using the lightweight BiSeNet. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Semi-Global Consensus Problems of Discrete-Time Multi-Agent systems in the Presence of Input Constraints

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Journal of Shanghai Jiaotong university(Science) 2020年第3期25卷 288-298页

作者： WANG Jian SHI Liangren Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai Jiao Tong UniversityShanghai 200240China Shanghai Key Laboratory of Navigation and Location-Based Services Shanghai Jiao Tong UniversityShanghai 200240China Academy of Information Technology and Electrical Engineering Shanghai Jiao Tong UniversityShanghai 200240China

This paper studies the consensus problems of the discrete-time multi-agent systems (MASs) in the presence of input saturation constraints over directed communication *** kinds of consensus problems,of the leaderless consensus problem with no leader agent and the containment control problem with multiple leader agents,are investigated in this *** gain feedback consensus algorithms based on the improved discretetime parametric algebraic Riccati equation (ARE) are proposed to solve the consensus *** the MAS without any leader,the trajectories of all agents converge together when the communication networks contain a directed spanning *** the MAS with multiple leaders,the trajectories of all follower agents converge to the convex hull spanned by the leader agents when there is at least one leader agent which is trackable for each follower *** examples are provided to verify the theoretical results.

关键词： leaderless consensus containment control discrete-time low gain input saturation

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Hybrid integrated modeling of thermal field temperature in Cz silicon single crystal growth process based on VMD-CNN-LSTM-ELM

Hybrid integrated modeling of thermal field temperature in C...

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第40届中国控制会议

作者： Yin Wan Ding Liu Junchao Ren National & Local Engineering Research Center of Crystal Growth Equipment and System Integration Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology

Czochralski（Cz） silicon single crystal growth is a dynamic time-varying process with complex physical changes,multi-phase coupling of multiple fields, model uncertainty and large hysteresis and nonlinearity, while mechanistic models based on a large number of assumptions are difficult to be applied practically. In order to establish a stable and reliable model of crystal growth process, this paper proposes a variational modal decomposition--convolutional neural network-long and short-term memory network-extreme learning machine（VMD-CNN-LSTM-ELM） nonlinear hybrid integrated modeling method based on the idea of data-driven modeling to construct a model between heater power and thermal field temperature. The model consists of feature extraction, deep learning network, and optimization algorithm. First, VMD is used to decompose the original data into multiple Intrinsic Mode Function（IMF） from low to high frequencies, preserving the variation characteristics of the signal at different frequencies. Second, for each eigenmode function sequence, CNN feature extraction and LSTM prediction are performed in turn. Then, ELM is used to nonlinearly integrate the prediction results of multiple sequences to obtain the final prediction output. Finally, the simulation experimental results based on industrial data show that the proposed VMD-LSTMELM based thermal field temperature has excellent prediction performance and generalization ability.

关键词： Cz silicon single crystal growth Data-driven modeling Thermal field temperature Hybrid integrated modeling

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Unsupervised Learning of Depth and Pose Based on Monocular Camera and Inertial Measurement Unit (IMU)

Unsupervised Learning of Depth and Pose Based on Monocular C...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Yanbo Wang Hanwen Yang Jianwei Cai Guangming Wang Jingchuan Wang Yi Huang University of Michigan-Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University Shanghai China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Weitong Vision Technology Co. Ltd.

The main content of the research in this paper is the estimation of depth and pose based on monocular vision and Inertial Measurement Unit (IMU). The usual depth estimation network and pose estimation network require depth ground truth or pose ground truth as a supervised signal for training, while the depth ground truth and pose ground truth are hard to obtain, and monocular vision based depth estimation cannot predict absolute depth. In this paper, with the help of IMU, which is inexpensive and widely used, we can obtain angular velocity and acceleration information. Two new supervision signals are proposed and the calculation expressions are given. Among them, the model trained with acceleration constraint shows a good ability to estimate the absolute depth during the test. It can be considered that the model can estimate the absolute depth. We also derive the method of estimating the scale factor during the test from the acceleration constraint, and also achieve good results as the acceleration constraint does. In addition, this paper also studies the method of using IMU information as pose network input and as selecting conditions. Moreover, it analyzes and discusses the experimental results. At the same time, we also evaluate the effect of the pose estimation of the relevant models. This article starts by reviewing the achievements and deficiencies of the work in this field, combines the use of IMU, puts forward three new methods such as a new loss function, and conducts a test analysis and discussion of relevant indicators on the KITTI data set.

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Choose Your Simulator Wisely: A Review on Open-source Simulators for Autonomous Driving

arXiv

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arXiv 2023年

作者： Li, Yueyuan Yuan, Wei Zhang, Songan Yan, Weihao Shen, Qiyuan Wang, Chunxiang Yang, Ming The Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The Innovation Center of Intelligent Connected Vehicles Global Institute of Future Technology Shanghai Jiao Tong University Shanghai200240 China The Global Institute of Future Technology Shanghai Jiao Tong University Shanghai200240 China

Simulators play a crucial role in autonomous driving, offering significant time, cost, and labor savings. Over the past few years, the number of simulators for autonomous driving has grown substantially. However, there is a growing concern about the validity of algorithms developed and evaluated in simulators, indicating a need for a thorough analysis of the development status of the simulators. To bridge the gap in research, this paper analyzes the evolution of simulators and explains how the functionalities and utilities have developed. Then, the existing simulators are categorized based on their task applicability, providing researchers with a taxonomy to swiftly assess a simulator’s suitability for specific tasks. Recommendations for select simulators are presented, considering factors such as accessibility, maintenance status, and quality. Recognizing potential hazards in simulators that could impact the confidence of simulation experiments, the paper dedicates substantial effort to identifying and justifying critical issues in actively maintained open-source simulators. Moreover, the paper reviews potential solutions to address these issues, serving as a guide for enhancing the credibility of simulators. Copyright © 2023, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Efficient Depth Selection for the Implementation of Noisy Quantum Approximate Optimization Algorithm

arXiv

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arXiv 2022年

作者： Pan, Yu Tong, Yifan Xue, Shibei Zhang, Guofeng State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou310027 China Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Department of Applied Mathematics The Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong

Noise on near-term quantum devices will inevitably limit the performance of Quantum Approximate Optimization Algorithm (QAOA). One significant consequence is that the performance of QAOA may fail to monotonically improve with depth. In particular, optimal depth can be found at a certain point where the noise effects just outweigh the benefits brought by increasing the depth. In this work, we propose to use the model selection algorithm to identify the optimal depth with a few iterations of regularization parameters. Numerical experiments show that the algorithm can efficiently locate the optimal depth under relaxation and dephasing noises. © 2022, CC BY.

关键词： Optimization

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