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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Liu, Shida Zhang, Tianzhen Ji, Honghai Wang, Li School of Electrical and Control Engineering North China University of Technology Beijing100000 China

ISBN: (纸本)9798350361674

In this paper, a novel abnormal behavior analysis algorithm based on YOLOv8 is proposed for the target recognition problem of guide robot dog. The algorithm is based on the human posture estimation model of YOLOv8 for abnormal behavior analysis, and by extracting the coordinate information of the key points of the human body, it can make conditional decisions on abnormal behaviors such as falling forward, backward and sideways, so as to realize the real-time monitoring of the blind people and send out alarms in time. The proposed algorithm ensures the accuracy and speed of the recognition while not taking up a lot of computational resources of the computer. The proposed method is applied to a real quadruped robot platform, and the effectiveness and applicability of the proposed algorithm is verified through a large number of experiments, which in this paper are based on the actual data collected from different human posture pictures. © 2024 ieee.

关键词： Microrobots

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Near-Optimal Design of Safe Output-Feedback controllers From Noisy data

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ieee TRANSACTIONS ON AUTOMATIC control 2023年第5期68卷 2699-2714页

作者： Furieri, Luca Guo, Baiwei Martin, Andrea Ferrari-Trecate, Giancarlo Ecole Polytech Fed Lausanne EPFL Inst Mech Engn CH-1015 Lausanne Switzerland

As we transition toward the deployment of data-driven controllers for black-box cyberphysical systems, complying with hard safety constraints becomes a primary concern. Two key aspects should be addressed when input-output data are corrupted by noise: how much uncertainty can one tolerate without compromising safety, and to what extent is the control performance affected? By focusing on finite-horizon constrained linear- quadratic problems, we provide an answer to these questions in terms of the model mismatch incurred during a preliminary identification phase. We propose a control design procedure based on a quasiconvex relaxation of the original robust problem and we prove that, if the uncertainty is sufficiently small, the synthesized controller is safe and near-optimal, in the sense that the suboptimality gap increases linearly with the model mismatch level. Since the proposed method is independent of the specific identification procedure, our analysis holds in combination with state-of-the-art behavioral estimators beyond standard least squares. The main theoretical results are validated by numerical experiments.

关键词： Safety Noise measurement Behavioral sciences Trajectory Biological system modeling Numerical models data models data-driven control learning-based control linear systems optimal control robust control

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Weakly Supervised Causal Discovery Based on Fuzzy Knowledge and Complex data Complementarity

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ieee TRANSACTIONS ON FUZZY systems 2024年第12期32卷 7002-7014页

作者： Li, Wenrui Zhang, Wei Zhang, Qinghao Zhang, Xuegong Wang, Xiaowo Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

Causal discovery based on observational data is important for deciphering the causal mechanism behind complex systems. However, the effectiveness of existing causal discovery methods is limited due to inferior prior knowledge, domain inconsistencies, and the challenges of high-dimensional datasets with small sample sizes. To address this gap, we propose a novel weakly supervised fuzzy knowledge and data co-driven causal discovery method named KEEL. KEEL introduces a fuzzy causal knowledge schema to encapsulate diverse types of fuzzy knowledge, and forms corresponding weakened constraints. This schema not only lessens the dependency on expertise but also allows various types of limited and error-prone fuzzy knowledge to guide causal discovery. It can enhance the generalization and robustness of causal discovery, especially in high-dimensional and small-sample scenarios. In addition, we integrate the extended linear causal model into KEEL for dealing with the multi-distribution and incomplete data. Extensive experiments with different datasets demonstrate the superiority of KEEL over several state-of-the-art methods in accuracy, robustness and efficiency. The effectiveness of KEEL is also verified in limited real protein signal transduction process data, with the better performance than benchmark methods. In summary, KEEL is effective to tackle the causal discovery tasks with higher accuracy while alleviating the requirement for extensive domain expertise.

关键词： data models Accuracy Optimization Computational modeling Adaptation models Robustness Computational efficiency Computational complexity Complex systems Fuzzy systems Causal discovery complex system fuzzy knowledge knowledge-data co-driven weakly supervised learning

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data-driven Convexification for Frequency Nadir Constraint of Unit Commitment

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Journal of Modern Power systems and Clean Energy 2023年第5期11卷 1711-1717页

作者： Yukang Shen Wenchuan Wu Bin Wang Yue Yang Yi Lin State Key Laboratory of Power Systems(Department of Electrical Engineering) Tsinghua UniversityBeijing 100084China IEEE Electric Power Research Institute Fujian Electric Power Ltd.CompanyFuzhou 350003China

The increasing penetration of the renewable energy sources brings new challenges to the frequency security of power systems. In order to guarantee the system frequency security, frequency constraints are incorporated into unit commitment(UC) models. Due to the non-convex form of the frequency nadir constraint which makes the frequency constrained UC(FCUC) intractable, this letter proposes a revised support vector machine(SVM) based system parameter separating plane method to convexify it. Based on this data-driven convexification method, we obtain a tractable FCUC model which is formulated as a mixed-integer quadratic programming(MIQP) problem. Case studies indicate that the proposed method can obtain less conservative solution than the existing methods with higher efficiency.

关键词： Unit commitment frequency constraint support vector machine(SVM) data-driven convexification

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A Study of a data-driven Cyber-Physical control System Using an Assessment Mechanism 7

A Study of a Data-Driven Cyber-Physical Control System Using...

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ieee 7th International conference on Industrial Cyber-Physical systems (ICPS)

作者： Li, Zhifeng Hiraoka, Kei Yamamoto, Tom Hiroshima Univ Grad Sch Adv Sci & Engn Hiroshima Japan

ISBN: (纸本)9798350363029;9798350363012

In recent years, Industry 4.0 has described cyberphysical systems (CPS) to integrate physical assets with cyber capabilities, revealing a new path to next-generation process control. In such a system, as a network that integrates knowledge from various domains, CPS can handle what is difficult to achieve in the real world by building cyberspace and feedback necessary information instead of the limitations imposed by physical assets. For this purpose, it is essential to employ an assessment mechanism in cyberspace that uses the information from physical space to accurately determine the actions that are to be reflected in physical space. This study first analyzes and discusses the cyber capabilities. Second, the basic structure of a data-driven cyber-physical control system using an assessment mechanism is proposed. Third, the design of a control system based on the above structure is discussed. Finally, the effectiveness of the method is verified using a numerical example.

关键词： data-driven cyber physical system process control digital twin PID control

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Plant control for Fully Automated AI-driven Product Type Recognition 19

Plant Control for Fully Automated AI-Driven Product Type Rec...

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ieee 19th conference on Industrial Electronics and Applications (ICIEA)

作者： Handmann, Finn Abou Baker, Nermeen Handmann, Uwe Hsch Ruhr West Comp Sci Dept Bottrop Germany

ISBN: (纸本)9798350360875;9798350360868

The development of industrial automation has led to a transformation in manufacturing, with the advent of advanced production and measurement technologies driven by digitalisation, information, and communication applications. The integration of artificial intelligence (AI) and machine learning (ML) into industrial applications represents a pivotal shift in the evolution of advanced control systems. This article examines the development and implementation of an AI-driven control system into an automated process, with a particular focus on optimizing raw material recovery through the classification of recycling items. The system developed is a prime example of the interlocking of mechanics, electronics and AI, paving the way for a new era in recycling technology. In addition, this work contributes to research in the field of industry automation and underlines the transformative power of AI in promoting a sustainable circular economy.

关键词： Circular economy

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Reinforcement learning with Enhanced Safety for Optimal Dispatch of Distributed Energy Resources in Active Distribution Networks

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Journal of Modern Power systems and Clean Energy 2024年第5期12卷 1484-1494页

作者： Xu Yang Haotian Liu Wenchuan Wu Qi Wang Peng Yu Jiawei Xing Yuejiao Wang the State Key Laboratory of Power Systems Department of Electrical EngineeringTsinghua UniversityBeijing 100084China the State Grid Shandong Electric Power Company Jinan 250000China

As numerous distributed energy resources(DERs)are integrated into the distribution networks,the optimal dispatch of DERs is more and more imperative to achieve transition to active distribution networks(ADNs).Since accurate models are usually unavailable in ADNs,an increasing number of reinforcement learning(RL)based methods have been proposed for the optimal dispatch ***,these RL based methods are typically formulated without safety guarantees,which hinders their application in real *** this paper,we propose an RL based method called supervisor-projector-enhanced safe soft actor-critic(S3AC)for the optimal dispatch of DERs in ADNs,which not only minimizes the operational cost but also satisfies safety constraints during online *** the proposed S3AC,the data-driven supervisor and projector are pre-trained based on the historical data from supervisory control and data acquisition(SCADA)system,effectively providing enhanced safety for executed *** studies on several ieee test systems demonstrate the effectiveness and safety of the proposed S3AC.

关键词： Reinforcement learning(RL) safety constraint optimal dispatch active distribution network(ADN) distributed energy resource(DER)

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Dropout Kolmogorov-Arnold Networks: A Novel data-driven Impedance Modeling Approach for Voltage-Source Converters

IEEE OPEN JOURNAL OF POWER ELECTRONICS

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ieee OPEN JOURNAL OF POWER ELECTRONICS 2025年 6卷 562-582页

作者： Ali, Moetasem Mohamed, Yasser Abdel-Rady I. Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 1H9 Canada

The extensive integration of voltage-source converters (VSCs) as interfaces for renewable energy sources in power systems increases stability concerns and demands accurate VSC impedance models to characterize grid-converter interactions at various operating points. However, analytical impedance models require detailed knowledge of the VSC parameters, which are frequently inaccessible due to manufacturer confidentiality. Further, existing neural network data-driven VSC impedance identification methods adopt conventional multi-layer perceptrons, yielding complex models and demanding abundant high-quality data. This paper presents a data-driven VSC impedance identification method using Dropout Kolmogorov-Arnold Networks (DropKANs) to address these challenges effectively. The hyperparameters of the proposed DropKAN model are optimized using Optuna, outperforming the Scikit-learn, Hyperopt, and GPyOpt optimizers, and the training is optimized using the Adam optimizer and compared with Nadam and RMSprop. Comprehensive and comparative evaluation tests showed 1) the superiority of the proposed DropKAN model over the feedforward neural network, long short-term memory, and KAN models in terms of accuracy, training and prediction times, and neural network structure simplicity, even with a 50% reduction in the training data size, and 2) the versatility and robustness of the proposed DropKAN model when applied to a different VSC system.

关键词： Impedance Power conversion Converters Training Analytical models Power system stability Mathematical models Fuzzy control Accuracy Power harmonic filters data-driven models dropout Kolmogorov-Arnold network impedance modeling machine learning voltage-source converter

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Emergency load shedding strategy for high renewable energy penetrated power systems based on deep reinforcement learning

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ENERGY REPORTS 2023年 9卷 434-443页

作者： Chen, Hongwei Zhuang, Junzhi Zhou, Gang Wang, Yuwei Sun, Zhenglong Levron, Yoash Northeast Elect Power Univ Key Lab Modern Power Syst Simulat & Control & Rene Minist Educ Jilin 132012 Jilin Peoples R China Technion Israel Inst Technol Andrew & Erna Viterbi Fac Elect Engn IL-3200003 Haifa Israel

Traditional event-driven emergency load shedding determines the quantitative strategy by simulation of a specific set of expected faults, which requires high model accuracy and operation mode matching. However, due to the model complexity of renewable power generators and fluctuating power generation, traditional event-driven load shedding strategy faces the risk of mismatching in high renewable energy penetrated power systems. To address these challenges, this paper proposes an emergency load shedding method based on data-driven strategies and deep reinforcement learning (RL). Firstly, the reason for the possible mismatch of the event-driven load shedding strategy in the renewable power system is analyzed, and a typical mismatch scenario is constructed. Then, the emergency load shedding strategy is transformed into a Markov Decision Process (MDP), and the decision process's action space, state space, and reward function are designed. On this basis, an emergency control strategy platform based on the Gym framework is established for application of deep reinforcement learning in the power system emergency control strategy. In order to enhance the adaptability and efficiency of the RL intelligence agent to multi-fault scenarios, the Proximal Policy Optimization (PPO) is adopted to optimize the constructed MDP. Finally, the proposed reinforcement learning-based emergency load shedding strategy is trained and verified through a modified ieee 39-bus system. The results show that the proposed strategy can effectively make correct strategies to restore system frequency in the event-driven load shedding mismatch scenario, and have good adaptability for different faults and operation scenarios.(c) 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Emergency load shedding Mismatch scenario Deep reinforcement learning Design of decision space

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Mitigating Stochasticity in Visual Servoing of Soft Robots Using data-driven Generative Models 7

Mitigating Stochasticity in Visual Servoing of Soft Robots U...

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7th International conference on Soft Robotics (ROBOSOFT)

作者： Perovic, Gojko Pique, Francesco Di Nasso, Gabriele Falotico, Egidio Scuola Super Sant Anna BioRohot Insitute Pisa Italy Scuola Super Sant Anna Dept Excellence Robot & AI Pisa Italy

ISBN: (纸本)9798350381818

Soft robots, due to their adaptability and safety, can make a significant impact in specialized fields such as surgical robotics owing to their compliance. However, such highly flexible robots are especially susceptible to modeling and control challenges due to non-linearities, large deformations, and infinite degrees of freedom. Combined with material variations, manufacturing inconsistencies, and environmental influences, these robots exhibit stochastic behavior. To address this, the paper introduces a learning-based approach supported by a Neural Network-based Generative Model designed to address the inherent stochastic nature of such robots in visual servoing tasks. By generating large quantities of synthetic data that represent the stochastic effects, another learning model used for control can be trained to mitigate them. The method's efficacy is demonstrated in visual servoing tasks by significantly improving the accuracy of the robot. Implementing the proposed model in a feedback control loop improves the performance when compared to the open loop approach in more challenging trajectories, marking a substantial stride in overcoming the challenges associated with stochastic behavior in soft robotics.

关键词： Stochastic systems

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