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arXiv 2022年

作者： Sharf, Miel Besselink, Bart Johansson, Karl Henrik Division of Decision and Control Systems Kth Royal Institute of Technology Digital Futures Stockholm10044 Sweden Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Groningen9700 AK Netherlands

Designing large-scale control systems to satisfy complex specifications is hard in practice, as most formal methods are limited to systems of modest size. Contract theory has been proposed as a modular alternative to formal methods in control, in which specifications are defined by assumptions on the input to a component and guarantees on its output. However, current contract-based methods for control systems either prescribe guarantees on the state of the system, going against the spirit of contract theory, or can only support rudimentary compositions. In this paper, we present a contract-based modular framework for discrete-time dynamical control systems. We extend the definition of contracts by allowing the assumption on the input at a time k to depend on outputs up to time k -1, which is essential when considering the feedback connection of an unregulated dynamical system and a controller. We also define contract composition for arbitrary interconnection topologies, under the pretence of well-posedness, and prove that this notion supports modular design, analysis and verification. This is done using graph theory methods, and specifically using the notions of topological ordering and backward-reachable nodes. Lastly, we use k-induction to present an algorithm for verifying vertical contracts, which are claims of the form "the conjugation of given component-level contracts is a stronger specification than a given contract on the integrated system". These algorithms are based on linear programming, and scale linearly with the number of components in the interconnected network. A numerical example is provided to demonstrate the scalability of the presented approach, as well as the modularity achieved by using it. Copyright © 2022, The Authors. All rights reserved.

关键词： Linear programming

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Distributed Constrained Online Nonconvex Optimization with Compressed Communication

arXiv

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arXiv 2025年

作者： Zhang, Kunpeng Xu, Lei Yi, Xinlei Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Department of Mechanical Engineering University of Victoria VictoriaBCV8W 2Y2 Canada Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai201210 China Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen GroningenAG 9747 Netherlands Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures Stockholm10044 Sweden

This paper considers distributed online nonconvex optimization with time-varying inequality constraints over a network of agents. For a time-varying graph, we propose a distributed online primal–dual algorithm with compressed communication to efficiently utilize communication resources. We show that the proposed algorithm establishes an O(T max{1−θ1,θ1}) network regret bound and an O(T 1−θ1/2) network cumulative constraint violation bound, where T is the number of iterations and θ1 ∈ (0, 1) is a user-defined trade-off parameter. When Slater’s condition holds (i.e, there is a point that strictly satisfies the inequality constraints at all iterations), the network cumulative constraint violation bound is reduced to O(T 1−θ1). These bounds are comparable to the state-of-the-art results established by existing distributed online algorithms with perfect communication for distributed online convex optimization with (time-varying) inequality constraints. Finally, a simulation example is presented to validate the theoretical results. Copyright © 2025, The Authors. All rights reserved.

关键词： Convex optimization

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Luxury Goods Identification based on Multi-Head Convolutional Neural Network with Attractor Loss

Luxury Goods Identification based on Multi-Head Convolutiona...

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Chinese Automation Congress (CAC)

作者： Binchuan Qi Wei Gong Li Li Zhenzhen Jiao Jian Jin The Department of Control Science and Engineering Tongji University Shanghai China Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China iF-Labs Beijing Teleinfo Technology Co. Ltd. CAICT Beijing China China Academy of Information and Communications Technology Beijing China

The counterfeit goods trade is a global issue. Traditionally, customized equipment, elaborate labeling, and expert inspection are the primary ways to identify fakes. In recent years, deep learning technology has been used for counterfeit identification based on image detection and classification. There are two critical challenges. First, in real-world situations, the counterfeiting patterns are diverse, while the genuine ones are often identical. This is because counterfeit goods may be partially assembled from some parts of genuine products. Second, authenticating a counterfeit as genuine can cause a lot of trouble. To address these issues, we propose a novel end-to-end algorithm that combines a new Multi-Head Convolution Neural Network (MH-CNN) with a new Loss function named Attractor Loss (AtL) and applies it to image classification of real and fake goods. Technically, our method takes multiple images of different positions of an object as input. Then it outputs 1) the classification probability of belonging to a real class, and 2) a feature vector that is used for nearest neighbor retrieval. On engineering implementation, we integrate an MH-CNN structure, which contains a shared CNN backbone for extracting deep features and multiple heads for computing the deep representation of different positions. To improve the recognition performance, we propose a joint training method to train the MH-CNN model and propose a new AtL to filter vector representation of images from real class and pull them closer for enhancing the recognition rate without loss of accuracy. Extensive experimental results demonstrate the superior performance of our proposed method.

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Cooperative Filtering with Range Measurements: A Distributed Constrained Zonotopic Method

arXiv

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arXiv 2023年

作者： Ding, Yu Cong, Yirui Wang, Xiangke Cheng, Long College of Intelligence Science and Technology National University of Defense Technology Changsha410073 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

This article studies the distributed estimation problem of a multi-agent system with bounded absolute and relative range measurements. Parts of the agents are with high-accuracy absolute measurements, which are considered as anchors;the other agents utilize low-accuracy absolute and relative range measurements, each derives an uncertain range that contains its true state in a distributed manner. Different from previous studies, we design a distributed algorithm to handle the range measurements based on extended constrained zonotopes, which has low computational complexity and high precision. With our proposed algorithm, agents can derive their uncertain range sequentially along the chain topology, such that agents with low-accuracy sensors can benefit from the high-accuracy absolute measurements of anchors and improve the estimation performance. Simulation results corroborate the effectiveness of our proposed algorithm and verify our method can significantly improve the estimation accuracy. Copyright © 2023, The Authors. All rights reserved.

关键词： Multi agent systems

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An Improved Method for Small Target Recognition Based on Faster RCNN 17th

An Improved Method for Small Target Recognition Based on Fas...

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17th Chinese Intelligent systems Conference, CISC 2021

作者： Liu, Qun-po Wang, Qi-jing Hanajima, Naohiko Su, Bo Department of Robotics Engineering Henan Polytechnic University 2001 Century Avenue Jiaozuo Henan454003 China Henan International Joint Laboratory of Direct Drive and Control of Intelligent Equipment 2001 Century Avenue Jiaozuo Henan454003 China College of Information and Systems Muroran Institute of Technology 27-1 Mizumoto-cho Muroran-shi Hokkaido050-8585 Japan

ISBN: (纸本)9789811663239

As one of the most challenging tasks in the field of deep learning, target detection has achieved remarkable results. The existing algorithms have achieved excellent results in the detection of large and medium-sized targets, but the detection accuracy of small targets is not ideal because of their small size and weak features. A small target recognition method based on improved Faster_Rcnn that aims at the problem of low accuracy in small target detection is proposed in this paper. Since the ROI Pooling method in Faster Rcnn might cause quantization errors in the operation process, resulting in inaccurate positioning and other problems during detection, an improved ROI Align method is adopted to eliminate the quantization errors. The cell public data set is used for the experiment, the results show that the average recognition accuracy of the recognition model with improved algorithm is 92.11%, which is an increase of 7%. The method also obtains good effect on NWPU VHR-10 data set, which indicates that it has good universality. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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Research on Data Standardization and Unified Data Interface Based on Digital Station System

Research on Data Standardization and Unified Data Interface ...

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IEEE Advanced Information Management,Communicates,Electronic and Automation control Conference (IMCEC)

作者： Wang Xiaojin Sun Shucai Xie Yehua Jiang Tao Liu Hongkun NARI TECHNOLOGY CO. LTD NARI-TECH Nanjing Control Systems Ltd. NARI Group Corporation/State Grid Electric Power Research Institute Nanjing China

ISBN: (纸本)9781665479691

At present, the development of energy and power is faced with a major strategic task of ensuring safe and reliable supply, accelerating the clean and low-carbon transformation, and helping to achieve the “dual carbon” goal. The construction of digital station information system bears the historical mission of energy transformation, has great and far-reaching significance, is an important part of a clean, low-carbon, safe and efficient energy system, and has new features such as clean, low-carbon, safe and controllable, flexible and efficient, intelligent, friendly, open and interactive. In this paper, the equipment model data standardization technology and unified data interface technology based on the digital station information system are studied to solve the problems faced in the construction of the digital station information system, such as multiple islands of information, multiple fault patterns, and low data value. According to the principle of unified collection, storage and utilization of distribution network information, the SG-CIM model specification is used as the basis for modeling to provide standardized data; The unified data interface of digital station information is formed through unified basic software and hardware facilities, unified equipment information model, and unified application and development specifications to achieve equipment centric panoramic data aggregation and sharing, and visual presentation of operation and maintenance business. Realize the coordination and interaction of digital station information system, and provide a strong support way for the country to promote the “double carbon” goal.

关键词： Visualization Standardization Distribution networks Data models Software Hardware Software reliability

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Path Loss Map Construction Based on RadioResUNet

Path Loss Map Construction Based on RadioResUNet

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Chinese Automation Congress (CAC)

作者： Wenhao Du Wei Gong Li Li Zhenzhen Jiao Jian Jin The Department of Control Science and Engineering Tongji University Shanghai China Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China iF-Labs Beijing Teleinfo Technology Co. Ltd. CAICT Beijing China China Academy of Information and Communications Technology Beijing China

Propagation path loss prediction is critical in wireless communication, playing a significant role in wireless network planning, signal transmission optimization, and wireless coverage evaluation. However, traditional path loss prediction methods often rely on complex physical models and environmental parameters, leading to time-consuming and computationally intensive calculations. Several machine learning methods have been proposed to predict path loss, but their accuracy still needs improvement. To address these issues, this paper proposes a novel machine learning method called RadioResUNet, which is an improved version of ResUNet. RadioResUnet incorporates progressive multilevel encoder, attention mechanism, depthwise separable convolution, and fine-grained downsampling into ResUnet to adapt to the path loss prediction problem. Compared to traditional path loss prediction methods, our method directly predicts path loss from the input (city map) by learning map features and spatial relationships, reducing the reliance on complex physical models and environmental parameters, simplifying the computation process, and enhancing path loss prediction accuracy. Through experiments on the RadioMapSeer dataset, we demonstrate that our proposed method outperforms the existing methods in terms of prediction accuracy.

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Learning Based Model Predictive control for Quadcopters with Dual Gaussian Process

arXiv

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arXiv 2021年

作者： Liu, Yuhan Tóth, Roland The Harbin Institute of Technology Department of Control Science and Engineering Harbin China The Eindhoven University of Tech. Dep. of Electrical Eng. Control Systems Group Eindhoven Netherlands The Institute for Computer Science and Control Budapest Hungary

An important issue in quadcopter control is that an accurate dynamic model of the system is nonlinear, complex, and costly to obtain. This limits achievable control performance in practice. Gaussian process (GP) based estimation is an effective tool to learn unknown dynamics from input/output data. However, conventional GP-based control methods often ignore the computational cost associated with accumulating data during the operation of the system and how to handle forgetting in continuous adaption. In this paper, we present a novel Dual Gaussian Process (DGP) based model predictive control strategy that improves the performance of a quadcopter during trajectory tracking. The bio-inspired DGP structure is a combination of a long-term GP and a short-term GP, where the long-term GP is used to keep the learned knowledge in memory and the short-term GP is employed to rapidly compensate unknown dynamics during online operation. Furthermore, a novel recursive online update strategy for the short-term GP is proposed to successively improve the learnt model during online operation in an efficient way. Numerical simulations are used to demonstrate the effectiveness of the proposed strategy. © 2021, CC BY-NC-SA.

关键词： Model predictive control

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Distributed model predictive control of buildings and energy hubs

arXiv

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arXiv 2021年

作者： Lefebure, Nicolas Khosravi, Mohammad De Badyn, Mathias Hudoba Bünning, Felix Lygeros, John Jones, Colin Smith, Roy S. Department of Mechanical Engineering Swiss Federal Institute of Technology Lausanne Switzerland Automatic Control Laboratory Swiss Federal Institute of Technology Zürich Switzerland Empa Urban Energy Systems Laboratory Überlandstrasse 129 Dübendorf8600 Switzerland

Model predictive control (MPC) strategies can be applied to the coordination of energy hubs to reduce their energy consumption. Despite the effectiveness of these techniques, their potential for energy savings are potentially underutilized due to the fact that energy demands are often assumed to be fixed quantities rather than controlled dynamic variables. The joint optimization of energy hubs and buildings' energy management systems can result in higher energy savings. This paper investigates how different MPC strategies perform on energy management systems in buildings and energy hubs. We first discuss two MPC approaches;centralized and decentralized. While the centralized control strategy offers optimal performance, its implementation is computationally prohibitive and raises privacy concerns. On the other hand, the decentralized control approach, which offers ease of implementation, displays significantly lower performance. We propose a third strategy, distributed control based on dual decomposition, which has the advantages of both approaches. Numerical case studies and comparisons demonstrate that the performance of distributed control is close to the performance of the centralized case, while maintaining a significantly lower computational burden, especially in large-scale scenarios with many agents. Finally, we validate and verify the reliability of the proposed method through an experiment on a full-scale energy hub system in the NEST demonstrator in Dübendorf, Switzerland. Copyright © 2021, The Authors. All rights reserved.

关键词： Predictive control systems

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Trajectory Tracking for Directional Drilling in Underground Coal Mine Using Robust Model Predictive control

Trajectory Tracking for Directional Drilling in Underground ...

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IEEE International Conference on Industrial technology (ICIT)

作者： Jiajun Wu Chengda Lu Hao Li Hongchao Wei Min Wu School of Future Technology China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China School of Automation China University of Geosciences Wuhan China Xi'an Research Institute Co. Ltd. China Coal Technology and Engineering Group Xi'an China China Coal Research Institute Beijing China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

This paper studies the problem of trajectory tracking control for directional drilling in underground coal mine by devising a robust model predictive control method. First, a directional drilling trajectory extension model is obtained by the linearization of a drilling tool kinematic model through the Taylor series expansion in the borehole depth domain. Then, in view of the influence of the uncertainty of the drilling tool slope and external interference, a robust model predictive control method is introduced in the form of an optimal control problem. A sufficient condition is obtained to calculate the corresponding controller gain matrix on the premise of satisfying the stability of the closedloop control system and the $H_{\infty}$ performance index. At last, the effectiveness of our method is illustrated through simulation analysis. Compared with model predictive control, robust model predictive control directly incorporates the uncertainty of the drilling tool slope and external interference into the control design, enabling it to better adapt to the complex and variable underground coal mine environment.

关键词： Drilling Uncertainty Trajectory tracking Stability criteria Predictive models Taylor series Coal mining Trajectory Thermal stability Predictive control

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