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94th Annual Meeting of the International Association of Applied Mathematics and Mechanics, GAMM 2024

作者： Dierkes, Eva Kappertz, Lars Solovievskyi, Viacheslav Hackenberg, Annika Büskens, Christof Steinbeis Innovation Center for Optimization and Control Grasberg Germany Center for Industrial Mathematics University of Bremen Bremen Germany

This work introduces an optimally controlled forecast-based energy management system (EMS) that integrates energy demand and production forecast with optimal control techniques to optimize the management of renewable energy sources. Although controlling the energy generation is not feasible for weather-dependent power plants, it is possible to predict the amount of energy produced. Self-sufficiency can be improved by regulating individual devices and storage units based on anticipated energy production and demand. For electricity tariffs much larger than feed-in tariffs, such demand-side management can notably improve the profitability of (private) renewable energy systems. The EMS is based on long-term predictions that enable high-level control decisions and specific controls for the next controlling steps on a low level. Weather forecasts and forecasts of the power demand and state behavior of each device are considered to evaluate both cost function and constraints for the next prediction horizon. The system’s performance is evaluated through a case study, demonstrating its ability to control the energy distribution successfully. The proposed EMS presents a promising approach to smart energy management that can contribute to a more sustainable future. © 2024 The Author(s). Proceedings in Applied Mathematics & Mechanics published by Wiley-VCH GmbH.

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Indoor Localization for an Autonomous Model Car: A Marker-Based Multi-Sensor Fusion Framework 19

Indoor Localization for an Autonomous Model Car: A Marker-Ba...

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19th IEEE International Conference on Automation Science and Engineering, CASE 2023

作者： Li, Xibo Patel, Shruti Stronzek-Pfeifer, David Büskens, Christof University of Bremen Group Optimization and Optimal Control Center for Industrial Mathematics Bremen28359 Germany

ISBN: (纸本)9798350320695

Global navigation satellite systems readily provide accurate position information when localizing a robot outdoors. However, an analogous standard solution does not exist yet for mobile robots operating indoors. This paper presents an integrated framework for indoor localization and experimental validation of an autonomous driving system based on an advanced driver-assistance system (ADAS) model car. The global pose of the model car is obtained by fusing information from fiducial markers, inertial sensors and wheel odometry. In order to achieve robust localization, we investigate and compare two extensions to the Extended Kalman Filter;first with adaptive noise tuning and second with ?2-testing for measurement outlier detection. An efficient and low-cost ground truth measurement method using a single LiDAR sensor is also proposed to validate the results. The performance of the localization algorithms is tested on a complete autonomous driving system with trajectory planning and model predictive control. © 2023 IEEE.

关键词： Fault detection

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Benchmarking solution methods for parameter identification in dynamical systems

Benchmarking solution methods for parameter identification i...

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93rd Annual Meeting of the International Association of Applied Mathematics and Mechanics, GAMM 2023

作者： Wiesner, Marek Büskens, Christof Optimization and Optimal Control Center for Industrial Mathematics University of Bremen Bremen Germany

Accurate models are crucial for simulating, optimizing, and controlling real-world processes. Parameter identification—the task of estimating the unknown parameters of a dynamical system based on measurements—is challenging and there exist various methods to approach it. Integration-based methods, such as shooting methods and full discretization, approximate the model output by numerically solving the dynamical system. Gradient matching methods, on the other hand, avoid solving the dynamical system and focus on minimizing the error between the measurement slope and the state derivatives instead. All approaches have advantages and disadvantages and a recipe for which method is best in a particular situation does not exist. In this paper, we present the results of a benchmark comparing single shooting, multiple shooting, full discretization, and gradient matching using a comprehensive database of test problems. We investigate if there is a notable difference in the performance of the various methods and if one approach is superior to the others. From the benchmark, we conclude that the integration-based methods outperform gradient matching. While full discretization exhibits robustness, single and multiple shooting provide higher precision. Additionally, we observe that finding an optimal configuration of the methods across a diverse set of parameter identification problems remains challenging and often requires fine-tuning. © 2023 The Authors. Proceedings in Applied Mathematics and Mechanics published by Wiley-VCH GmbH.

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Unbalanced few-shot classification based on ME-MAML framework 43

Unbalanced few-shot classification based on ME-MAML framewor...

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43rd Chinese control Conference, CCC 2024

作者： Liao, Zherun Lv, Zheng Sun, Qiao Zhao, Jun Dalian University of Technology Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian116024 China Electric Operations Control Center State Grid Dalian Power Supply Company Dalian China

ISBN: (纸本)9789887581581

Aiming at the few-shot problem of lack of sufficient samples and uneven samples, which is common in medical image classification tasks, this paper proposes a meta-learning framework ME-MAML (Medical ModelAgnostic Meta-Learning) for few-shot medical image classification tasks. The framework aims to train a set of parameters with good generalization performance for the multi-class medical image classification task by learning the base class of medical images, and complete the generalization for the new class of medical image classification task by learning a small number of new classes of medical images. ME-MAML improves the MAML (Model-Agnostic Meta-Learning) meta-learning framework from the aspects of the feature extraction network, the loss function and the algorithmic framework, which makes the framework more suitable for few-shot medical image classification tasks. The experimental results demonstrate that for few-shot medical image classification task, the framework has better classification performance compared to supervised learning frameworks as well as advanced meta-learning frameworks. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Contrastive Learning

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Industrial Smoke Images Generation Network Based on Bidirectional Recurrent Network 43

Industrial Smoke Images Generation Network Based on Bidirect...

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43rd Chinese control Conference, CCC 2024

作者： Chen, Kai Lv, Zheng Sun, Qiao Zhao, Jun Wang, Wei Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Dalian University of Technology Ministry of Education Dalian116024 China Electric Operations Control Center State Grid Dalian Power Supply Company Dalian China

ISBN: (纸本)9789887581581

The monitoring of smoke emissions serves as a crucial reference for guiding operations in the steel industry. However, due to the scarcity of industrial smoke datasets, most deep learning methods encounter difficulties when addressing this problem. To address the issue of limited smoke data, this paper proposes a novel data augmentation method called the bidirectional recurrent generative network. This algorithm combines the concepts of recurrent generative networks and Laplacian pyramid networks to learn smoke generation from smoke-free blast furnaces and eliminate smoke from smoke blast furnaces. In order to overcome the limitation of recurrent generative networks in accurately depicting target contours, this paper introduces contour feature vectors, which provide precise characterization of the target contours through constraint-based feature vectors. Furthermore, to alleviate the difficulty of generation, the Laplacian pyramid model is introduced, which reduces parameters and learning time by learning from low-resolution images. To validate the effectiveness of the proposed algorithm, extensive ablation experiments and comparative trials are conducted on four different smoke image datasets from industrial scenarios with limited quantities. The results demonstrate significant improvements achieved by this paper's algorithm. Compared to other generation methods, the proposed algorithm exhibits superior performance in smoke generation tasks, offering an effective solution to address the challenges of scarce industrial smoke data and the training difficulties faced by related deep learning algorithms. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Laplace transforms

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Spacecraft Attitude Fault-tolerant Maneuver control with Flexible Prescribed Performance 7

Spacecraft Attitude Fault-tolerant Maneuver Control with Fle...

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7th International Symposium on Autonomous Systems, ISAS 2024

作者： Yang, Ze Yang, Baoqing Ji, Ruihang Ma, Jie Fang, Xu Ge, Shuzhi Sam Harbin Institute of Technology Control and Simulation Center Harbin China National University of Singapore Electrical and Computer Engineering Singapore Dalian University of Technology Intelligent Control and Optimization For Industrial Equipment of Ministry of Education Dalian China

ISBN: (纸本)9798350363173

This paper studies fault-tolerant control of the spacecraft attitude control system with prescribed performance under the coexistence of actuator faults, disturbances and uncertainties. First, a flexible appointed-time prescribed performance function (FAPPF) is proposed, which can significantly improve prescribed performance control robustness to unknown initial errors and avoid repeated and cumbersome parameter adjustments. Secondly, an equivalent fusion error is designed based on FAPPF, and terminal sliding mode is introduced to design a fault-tolerant controller, so that the attitude and angular rate meet the prescribed performance constraints at the same time. Further, the improved gray wolf optimization algorithm (IGWO) is introduced for online tolerant allocation of actuator control, which satisfies saturation and fault constraints while reducing energy consumption. Finally, numerical simulation proves the controller's superiority and robustness. © 2024 IEEE.

关键词： controllers

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Research on anomaly detection of underwater sensor networks based on LSCF neural network 24

Research on anomaly detection of underwater sensor networks ...

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2nd International Conference on Frontiers of Intelligent Manufacturing and Automation, CFIMA 2024

作者： Qian, Junlei Li, Yihang Jia, Tao Zeng, Kai North China University of Science and Technology Tangshan Iron and Steel Enterprise Process Control and Optimization Technology Innovation Center Tangshan China North China University of Science and Technology Tangshan China

ISBN: (纸本)9798400710681

To address the difficulties in detecting anomalous data in underwater sensor networks and the issue of concept drift in streaming data, a method by constructing an LSTM-CNN Fusion network is proposed. The Long Short-Term Memory neural network and the One-Dimensional Convolutional Neural Network are used as the two base learners of the LSCF network. The Adaptive Sliding Window drift detector is utilized to detect whether data drift occurs. The two base learners are trained independently, and their prediction results are combined through soft voting. The performance of the model is validated using two public datasets, with results showing detection accuracies of 97% and 95% for anomalous data, significantly higher than the average detection accuracy of other models. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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PERIODIC SYSTEMS WITH TIME DEPENDENT MAXIMAL MONOTONE OPERATORS

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Acta Mathematica Scientia 2024年第4期44卷 1280-1300页

作者： Zhenhai LIU Nikolaos S.PAPAGEORGIOU Center for Applied Mathematics of Guangri Yulin Normal UniversityYulin 537000China Guangri Key Laboratory of Universities Optimization Control and Engineering Calculation College of Mathematics and PhysicsGuangri Minzu UniversityNanning 530006China Department of Mathematics National Technical UniversityZografou Campus15780 AthensGreece

We consider a first order periodic system in R^(N),involving a time dependent maximal monotone operator which need not have a full domain and a multivalued *** prove the existence theorems for both the convex and nonconvex *** also show the existence of extremal periodic solutions and provide a strong relaxation ***,we provide an application to nonlinear periodic control systems.

关键词： periodic boundary condition time-dependent maximal monotone operator convex and nonconvex problems extremal solutions strong relaxation

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Time-Varying Formation Tracking control of Heterogeneous Multi-Agent Systems With Intermittent Communications and Directed Switching Networks

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 294-296页

作者： Yuhan Wang Zhuping Wang Hao Zhang Huaicheng Yan the Shanghai Research Institute for Intelligent Autonomous Systems Tongji University the National Key Laboratory of Autonomous Intelligent Unmanned Systems the Frontiers Science Center for Intelligent Autonomous System of Ministry of Education the College of Electronic and Information Engineering the Department of Control Science and Engineering Tongji University the Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education the School of Information Science and Engineering East China University of Science and Technology

Dear Editor,This letter is concerned with the problem of time-varying formation tracking for heterogeneous multi-agent systems(MASs) under directed switching networks. For this purpose, our first step is to present so... 详细信息

关键词： Switches Topology Observers Formation control Event detection Switched systems Multi-agent systems control theory Autonomous systems Stability

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Low-Cost LISN Design for Overcoming CubeSat EMC Challenges 1

Low-Cost LISN Design for Overcoming CubeSat EMC Challenges

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1st International Conference on Global Aeronautical Engineering and Satellite Technology, GAST 2024

作者： El Hachimi, Youssef Daghouri, Amina Ouhammam, Abdelaali Mahmoudi, Hassane El Hani, Soumia University Center for Research in Space Technologies Emi Mohammed v University in Rabat Power Electronics Smart Control Techniques Automatic and Robotics Team Emi Morocco Ensam University Center for Research in Space Technologies Emi Mohammed v University in Rabat Energy Optimization Diagnosis and Control Team Morocco

ISBN: (纸本)9798350371598

Cubesats have emerged as a cost-effective solution for a wide range of space-based applications, making them increasingly popular for both research and commercial missions. However, ensuring Electromagnetic Compatibility (EMC) compliance is crucial for the success of CubeSat missions. To maximize the chances of mission success, EMC considerations should be integrated into the early design phases of the CubeSat and carefully tested and verified throughout the development process. This paper provides an overview of the EMC aspects related to CubeSats, including the challenges that developers and researchers face when dealing with EMC, and propose a Low cost Line Impedance Stabilization Network that can be used for EMC pre-compliance testing of subsystems before integration. © 2024 IEEE.

关键词： Electromagnetic compatibility

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