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IEEE Conference on Decision and control

作者： Shuling Wang Peizhe Li Shanying Zhu Cailian Chen 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 Intelligent Control and Management Shanghai China

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

The heterogeneous system consisting of the wireless control system (WCS) and mobile agent system (MAS) is ubiquitous in Industrial Internet of Things (IIoT) systems. Within this system, the positions of mobile agents may lead to shadow fading on the wireless channel that the WCS is controlled over and can significantly compromise the WCS’s performance. This paper focuses on the controller design for the MAS to ensure the performance of WCS in the presence of WCS and MAS coupling. Firstly, the constrained finite field network (FFN) with profile-dependent switching topology is adopted to proceed the operational control for the MAS. By virtue of the algebraic state space representation (ASSR) method, an equivalent form is obtained for the WCS and MAS coupling. A necessary and sufficient condition in terms of constrained set stabilization is then established to ensure the Lyapunov-like performance with expected decay rate. Finally, a graphical method together with the breath-first searching is provided to design state feedback controllers for the MAS. With this method, it is easy to check the constrained set stabilization of MAS and to ensure the performance requirements of WCS in the presence of WCS and MAS coupling. The study of an illustrative example shows the effectiveness of the proposed method.

关键词： Couplings Wireless communication Fading channels State feedback Mobile agents Optical wavelength conversion Switches

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Multi-type and Multi-level Feature Fusion Network for RGBD Indoor Semantic Segmentation

Multi-type and Multi-level Feature Fusion Network for RGBD I...

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Chinese control and Decision Conference, CCDC

作者： Yuwen Xia Chaochen Gu Kaijie Wu 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 Intelligent Control and Management Shanghai China

ISBN: (纸本)9781665478977

RGBD images, which refer to the three-channel color images augmented with depth channel, have been greatly exploited to improve the performance of scene semantic segmentation. RGBD semantic segmentation task is of great significance to help the development of robot navigation and grasping. However, most existing methods addressing RGBD semantic segmentation lack effective feature fusion modules. To address the aforementioned problem, we propose a novel network with two novel modules for feature fusion, namely Channel-Attention based Complementary Feature Fusion Module (CAC-FFM) and Cross-Layer Feature Fusion Module (CL-FFM). Specifically, CAC-FFM, which is essentially based on the channel-attention mechanism, effectively utilizes the complementary information of RGB and depth to generate fusion features, and CL-FFM captures patch-wise features from low-level feature maps to assist the training of high-level features so as to further optimize the segmentation results. Experimental results on the publicly available NYUDv2 dataset validate the effectiveness and superiority of our proposed method.

关键词： Training Cross layer design Correlation Navigation Semantic segmentation Grasping Excavation

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Building Temperature and Humidity Adaptive control for a Multi-Zone HVAC system Using Hybrid Modeling Method

Building Temperature and Humidity Adaptive Control for a Mul...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Yuliang Jiang Shanying Zhu Qimin Xu Bo Yang Xinping Guan 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 Intelligent Control and Management Shanghai China

ISBN: (纸本)9781728119496

In this paper, the heating, ventilation and air conditioning (HVAC) system as the contributor of building climate management is studied on multi-zone climate dynamics modelling and control for occupancy comfort. The first-principles model together with data-driven model as the hybrid model is proposed for comprehensive multi-zone hygro-thermal dynamics. The unmodeled climate dynamics such as unknown nonlinearity and uncertainty is modeled in a linear form based on the compact form dynamic linearization (CFDL) method. Then model free adaptive control (MFAC) scheme including controller design and parameter estimation is proposed to realize multi-zone climate control and occupancy comfort. The dynamic response performance of the hybrid model-based adaptive control and the first-principles model-based adaptive control has been tested in a multi-zone building with four different zones. The results show that the proposed hybrid model-based adaptive control scheme can achieve obvious performance improvement in minimizing steady-state error.

关键词： Adaptation models HVAC Parameter estimation Uncertainty Atmospheric modeling Buildings Ventilation

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controllability of Networked Sampled-data systems

arXiv

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arXiv 2022年

作者： Yang, Zixuan Wang, Xiaofan Wang, Lin The Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronic Engineering and Automation Shanghai University Shanghai200444 China

The controllability of networked sampled-data systems with zero-order holders on the control and transmission channels is explored, where single- and multi-rate sampling patterns are considered, respectively. The effects of sampling on the controllability of networked systems are analyzed, with some necessary and/or sufficient controllability conditions derived. Different from the sampling control of single systems, the pathological sampling of node systems could be eliminated by an appropriate design of network structure and inner couplings. While for singular topology matrices, the pathological sampling of single nodes will cause the entire system to lose controllability. Moreover, any periodic sampling will not affect the controllability of networked systems with specific node dynamics. All the results indicate that whether a networked system is under pathological sampling or not is jointly determined by mutually coupled factors. Copyright © 2022, The Authors. All rights reserved.

关键词： controllability

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Adaptive Multi-Core Real- Time Scheduling Based on Reinforcement Learning

Adaptive Multi-Core Real- Time Scheduling Based on Reinforce...

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International Conference on control and automation (ICCA)

作者： Yonghui Liang Hui Li Fei Shen Qimin Xu Shuna Hua Shanying Zhu Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Re-search Center of Intelligent Control and Management Shanghai China Key Laboratory of Wireless Sensor Network and Communications Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China North Information Control Research Academy Group Company Ltd. Nanjing China

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

With the transformation towards industrial intel-ligence, multi-core processors are increasingly being applied in real-time networked control systems to ensure secure exe-cution of sensing, computing and actuating tasks under time constraints. However, existing scheduling methods result in either low CPU utilization or many missed task deadlines in dynamic systems. In this paper, we propose a two-layer scheduling architecture to address this issue by fully exploring the complex dependency between real-time tasks. To be specific, the local layer determines task execution priorities considering both dependency between tasks and deadline constraints by utilizing a reinforcement learning approach. Moreover, to better utilize the parallel capabilities of multi-core processors and reduce temporal collisions, this paper minimizes the requested core count for the task set based on a greedy strategy. The global layer designs a scheduling algorithm based on the pre-empt method and provides schedulability analysis of multiple task sets. Experimental results validate the correctness of the proposed scheduling approach, and efficiency is demonstrated through comparisons with baseline method.

关键词： Job shop scheduling Multicore processing Scheduling algorithms Networked control systems Reinforcement learning Real-time systems Sensors

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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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Enhanced Industrial Process Modeling with Transfer-Incremental-Learning: A Parallel Sae Approach and its Application to a Sulfur Recovery Unit

SSRN

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SSRN 2023年

作者： Mou, Tianhao Liu, Jinfeng Zou, Yuanyuan Li, Shaoyuan Xibilia, Maria Gabriella Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Department of Chemical and Materials Engineering University of Alberta EdmontonABT6G 1H9 Canada Department of Engineering University of Messina Messina98166 Italy

In industrial processes, quality variable prediction is important for process control and monitoring. The emergence of deep learning (DL) methods and their excellent prediction performance present a potential paradigm revolution in the modeling of quality variable prediction. However, in real-world production, the lack of offline labeled data and the presence of time-varying data distributions commonly exist, which seriously prohibits practical applications of DL-based predictive models. This paper introduces an enhanced quality variable prediction framework, Transfer-Incremental-Learning Parallel Stacked Autoencoders (TIL-PSAE), to address this issue. TIL-PSAE establishes an effective predictive model through an integration of three key components: a parallel model structure, a transfer- learning (TL)-based offline training strategy that accumulates knowledge from other similar processes, and an incremental-learning (IL)-based online adaptation strategy that preserves offline knowledge. The model structure contains two parallel SAEs, which separately extract process-invariant features and target-process-specific features. The offline training involves sequential training using data from different domains, facilitating the accumulation of knowledge from different domains into different parts of model. During online adaptation, the accumulated knowledge remains unchanged while a new combination is learned, thus improving online prediction accuracy and avoiding knowledge forgetting. The proposed model is applied to a sulfur recovery unit of four parallel sub-units. Experimental results demonstrate the effectiveness of the proposed model, in terms of both offline prediction performance and online prediction performance. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Unsupervised Learning of 3D Scene Flow from Monocular Camera

arXiv

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arXiv 2022年

作者： Wang, Guangming Tian, Xiaoyu Ding, Ruiqi Wang, Hesheng Department of Automation Institute of Medical Robotics 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 200240 China

Scene flow represents the motion of points in the 3D space, which is the counterpart of the optical flow that represents the motion of pixels in the 2D image. However, it is difficult to obtain the ground truth of scene flow in the real scenes, and recent studies are based on synthetic data for training. Therefore, how to train a scene flow network with unsupervised methods based on real-world data shows crucial significance. A novel unsupervised learning method for scene flow is proposed in this paper, which utilizes the images of two consecutive frames taken by monocular camera without the ground truth of scene flow for training. Our method realizes the goal that training scene flow network with real-world data, which bridges the gap between training data and test data and broadens the scope of available data for training. Unsupervised learning of scene flow in this paper mainly consists of two parts: (i) depth estimation and camera pose estimation, and (ii) scene flow estimation based on four different loss functions. Depth estimation and camera pose estimation obtain the depth maps and camera pose between two consecutive frames, which provide further information for the next scene flow estimation. After that, we used depth consistency loss, dynamic-static consistency loss, Chamfer loss, and Laplacian regularization loss to carry out unsupervised training of the scene flow network. To our knowledge, this is the first paper that realizes the unsupervised learning of 3D scene flow from monocular camera. The experiment results on KITTI show that our method for unsupervised learning of scene flow meets great performance compared to traditional methods Iterative Closest Point (ICP) and Fast Global Registration (FGR). The source code is available at: https://***/IRMVLab/3DUnMonoFlow. Copyright © 2022, The Authors. All rights reserved.

关键词： Unsupervised learning

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Differentially Private Distributed Mismatch Tracking Algorithm for Constraint-Coupled Resource Allocation Problems

Differentially Private Distributed Mismatch Tracking Algorit...

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IEEE Conference on Decision and control

作者： Wenwen Wu Shanying Zhu Shuai Liu Xinping Guan 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 Intelligent Control and Management Shanghai China School of Control Science and Engineering Shandong University Jinan China

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

This paper considers privacy-concerned distributed constraint-coupled resource allocation problems over an undirected network, where each agent holds a private cost function and obtains the solution via only local communication. With privacy concerns, we mask the exchanged information with independent Laplace noise against attackers with potential access to all network communications. We propose a differentially private distributed mismatch tracking algorithm (diff-DMAC) to achieve cost-optimal distribution of resources while preserving privacy. Adopting constant stepsizes, the linear convergence property of diff-DMAC in mean square is established under the standard assumptions of Lipschitz smoothness and strong convexity. Moreover, it is theoretically proven that the proposed algorithm is ϵ-differentially private. And we also show the trade-off between convergence accuracy and privacy level. Finally, a numerical example is provided for verification.

关键词： Privacy Differential privacy Cost function Resource management Standards Convergence

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Exploring single atom catalysts of transition-metal doped phosphorus carbide monolayer for HER:A first-principles study

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Journal of Energy Chemistry 2021年第1期30卷 155-162,I0006页

作者： Dachang Chen Zhiwen Chen Xiaoxing Zhang Zhuole Lu Song Xiao Beibei Xiao Chandra Veer Singh School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072HubeiChina Department of Materials Science and Engineering University of TorontoToronto M5S 3E4OntarioCanada Department of Mechanical and Industrial Engineering University of TorontoToronto M5S 3G8OntarioCanada Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System Hubei University of TechnologyWuhan 430068HubeiChina School of Energy and Power Engineering Jiangsu University of Science and TechnologyZhenjiang 212003JiangsuChina

Hydrogen has been identified as one of the most promising sustainable and clean energy. Developing hydrogen evolution reaction(HER) catalyst with high activity is essential for satisfying the future requirements. Considering novel advantages of two-dimensional materials and high catalytic activity of atomic transition metal, in this study, using density functional theory calculation, the HER on single transitionmetal(23 different TM atoms) doped phosphorus carbide monolayer(α-PC) has been investigated. The Volmer–Tafel and Volmer–Heyrovsky reaction mechanisms, and the stability of the most promising HER catalyst are also included. The results show that Ir-αPC with high physical and thermal stability has the most optimal value of Gibbs free adsorption energy for H atom. The relationship of d band center and the HER activity shows a volcano-like curve. The calculation of reaction energy barrier indicates that the Volmer-Heyrovsky step is more favorable than the Volmer-Tafel step.

关键词： Hydrogen evolution reaction Phosphorus carbide monolayer Transition-metal doping Density functional theory

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