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2024 International conference on Intelligent Robots and systems

作者： Srisuchinnawong, Arthicha Baech, Jonas Hyzy, Marek Piotr Kounalakis, Tsampikos Boukas, Evangelos Manoonpong, Poramate Univ Southern Denmark Embodied Artificial Intelligence & Neurorobot Lab Odense Denmark Vidyasirimedhi Inst Sci & Technol Sch Informat Sci & Technol Rayong Thailand Danish Technol Inst Odense Denmark Tech Univ Denmark Lyngby Denmark

ISBN: (纸本)9798350377712;9798350377705

While different control approaches have been developed for smooth and safe navigation, they are limited by the needs for model-based assumptions, true training target/reward function, and/or large sample data. To overcome these limitations, this study proposes a model-free neural control architecture with a generic plug-and-play online Multiple Proactive Behavior learning (MPL) module. The MPL adapts robot neural control policy in an online unsupervised manner with small sample data by correlating its sensory inputs to a local planner command. As a result, it allows a mobile robot to autonomously and quickly learn and balance various proactive behaviors related to smooth motion and collision avoidance. It also compensates for the limited planning update rates and the planning model mismatch of an arbitrary local motion planner. Compared with existing control approaches without the MPL, our control architecture with the MPL leads to (1) a 10% improvement in the smoothness of robot motion and 30% fewer collisions in a narrow static environment, and (2) trading motion smoothness for up to 70% fewer collisions in an unknown dynamic environment. Taken together, this study also demonstrates how to apply model-free neural control with unsupervised learning to existing model-based control (e.g., local motion planner) for efficient proactive behavior learning and control of mobile robots.

关键词： Mobile robots

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Security indirect adaptive iterative learning control for nonlinear systems with fading channel under DoS attacks

Security indirect adaptive iterative learning control for no...

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2024 China Automation Congress, CAC 2024

作者： Wang, Yukun Liu, Dong College of Automation Shenyang Aerospace University Shenyang China

ISBN: (纸本)9798350368604

This paper investigates the security data-driven control problem for repetitive nonlinear systems with fading channels under denial-of-service (DoS) attacks. Firstly, under the influence of fading channels, a novel data-driven indirect adaptive iterative learning control (iAILC) algorithm is designed to idealize system tracking results. The adaptive mechanism is introduced to construct the adaptive set-point tuning law, thereby regulating the learning gain instantaneously. Secondly, double dynamic linearization method is deployed to simplify the system model, wherein the considered system is transferred to linear parametric model related to data. Then, considering the stochastic DoS attacks act on the fading output, compensation mechanism is developed by historical data, which eases the effect of DoS attacks accordingly. Finally, simulation results validate the efficiency of the presented control method. © 2024 ieee.

关键词： Stochastic systems

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Online Koopman Operator-Based Feedforward Compensation Strategy for Fast Tool Servos With Robust High-Bandwidth control

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ieee TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2025年第3期72卷 2958-2967页

作者： Meng, Yixuan Li, Linlin Wang, Xiangyuan Huang, Wei-Wei Wu, Edmond Q. Zhu, Limin Shanghai Jiao Tong Univ Sch Mech Engn State Key Lab Mech Syst & Vibrat Shanghai 200240 Peoples R China Weill Cornell Med Dept Anesthesiol New York NY 10065 USA Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China

To improve the servo accuracy for fast tool servo (FTS) applications, this article presents an online Koopman operator-based feedforward compensation strategy and combines it with the robust high-bandwidth controller. The robust high-bandwidth control is achieved by employing an optimized dual-loop feedback controller integrated with a disturbance observer. To realize the almost zero dynamic lag tracking, a Koopman operator-based data-driven learning methodology is proposed for real-time tracking error prediction and compensation. Thanks to the high computational efficiency, this methodology could fulfill the error prediction in a very short time, and thus is applicable to online applications extensively encountered in FTS operations. The error elimination mechanism is deduced and analyzed theoretically. The experimental results indicate that the root-mean-squared error is only 4.2 nm for tracking a random nonuniform rational B-splines curve with a travel range of 10 mu m. The proposed control strategy enables robust high-bandwidth and almost zero dynamic lag tracking for general and real-time generated trajectories, which is significant for improving the manufacturing efficiency and accuracy of the FTS.

关键词： Accuracy Tracking Real-time systems Feedforward systems Servomotors Trajectory Bandwidth Fast tool servo (FTS) Koopman operator robust high-bandwidth control

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Deep learning Models for Predicting Political Tendency in Facebook Posts Incorporating Texts and Emojis 6

Deep Learning Models for Predicting Political Tendency in Fa...

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6th International conference on data-driven Optimization of Complex systems (DOCS)

作者： Chiu, Shu-, I Natl Chengchi Univ Dept Comp Sci Taipei Taiwan

ISBN: (纸本)9798350377859;9798350377842

COVID-19 has brought massive challenges to the world, altering people's lives. We conducted a study on people's minds during Taiwan's National Epidemic 3-Level Alert in 2021. On May 22, 2021, during a regular press conference held by the Taiwan Centers for Disease control (CDC), the Minister of Health and Welfare introduced the term 'Retrospective Adjustment', which left the entire population in shock. This study focuses on analyzing social media posts during the outbreak, specifically 6,022 selected Facebook posts that mention 'Retrospective Adjustment' between May 22, 2021, and May 25, 2021. Various models are utilized to classify the sentiment categories of these posts, considering both texts and emojis. We compare the performance of the classification models using posts with only texts and both texts and emojis. The LSTM and BiLSTM are suitable for processing posts containing texts and emojis. Conversely, the BERT model performs better when it includes only text. In the case of the BERT model with only text, the F1-score reaches 0.8 for the positive and objective posts. However, the BERT model does not perform well for negative posts. Our results indicate a lack of sensitivity towards the contextual effects of negation in the BERT model.

关键词： Natural language processing deep learning sentiment analysis emoji Facebook social networks

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data-driven control System Using Machine learning in Production Process 43rd

Data-Driven Control System Using Machine Learning in Product...

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43rd IFIP WG 5.7 International conference on Advances in Production Management systems (APMS)

作者： Tanizaki, Takashi Fukuyama, Ayumu Uchino, Kunimi Kurokawa, Tetuaki Nakagawa, Shigemasa Kataoka, Takayuki Kindai Univ Grad Sch Syst Engn 1 Takaya Umenobe Higashihiroshima 7392116 Japan Nippon Steel Texeng Co Ltd 8-8Shinkawa 1 ChomeChuo Ku Tokyo 1040033 Japan

ISBN: (纸本)9783031658969;9783031658945;9783031658938

Many manufacturing companies control their production machines to produce good products within quality standards by using the results of research on physical or chemical models. Those models are developed from knowledge of the physical or chemical changes that occur when the products are processed and operational knowledge. However, it is difficult for some companies to research physical or chemical models. We study data-driven control systems to enable the stable production of good products when it is difficult to study and develop physical and chemical models or to use operational knowledge. In this paper, we propose an algorithm that builds an alternative model from actual operation data using machine learning and finds the optimal operating conditions under which the product is within the quality standards range using 0-1 integer programming. The effectiveness of the proposed algorithm was verified using operation data generated using a simulator for a food manufacturing process.

关键词： data-driven control System Machine learning Integer Programming

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Digital Twin-Based Transfer learning for Collaborative Robot systems: A Proof of Concept 38

Digital Twin-Based Transfer Learning for Collaborative Robot...

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38th International conference on Information Networking (ICOIN)

作者： Roongpraiwan, Supat Li, Zongdian Pourghasemian, Mohsen Gacanin, Haris Sakaguchi, Kei Tokyo Inst Technol Dept Elect & Elect Engn Tokyo Japan Rhein Westfal TH Aachen Chair Distributed Signal Proc Aachen Germany

ISBN: (纸本)9798350330946;9798350330953

Nowadays, the increasing trend toward digitalization has driven the extensive adoption of collaborative robotic automation across industries, yet a significant limitation is the robots' adaptability to unexpected and dynamic environments. This research introduces a Digital Twin (DT)-based Transfer learning (TL) approach that combines DTs and Machine learning (ML) to enhance adaptability in collaborative robot systems. The proposed system uses DT cyberspace for pre-training ML algorithms and leverages TL to apply this knowledge to real-world applications. This innovative approach efficiently trains state-of-the-art ML models, delivering exceptional performance while reducing the required time and data resources. The proof-of-concept experiments, employing the proposed DT-based TL to control soccer robots, demonstrate a remarkable 96% reduction in training time while maintaining a high level of adaptability, achieving a 70% goal accuracy rate in dynamic scenarios.

关键词： digital twin collaborative robot system machine learning transfer learning proof of concept

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Fed-SHARC: Resilient Decentralized Federated learning based on Reward driven Clustering 2

Fed-SHARC: Resilient Decentralized Federated Learning based ...

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2nd ieee conference on Artificial Intelligence (CAI)

作者： Kanagavelu, Renuga Anil, Chris George Wang, Yuan Fu, Huazhu Wei, Qingsong Liu, Yong Goh, Rick Siow Mong ASTAR Inst High Performance Comp IHPC Singapore Singapore Vellore Inst Technol Chennai India

ISBN: (纸本)9798350354102;9798350354096

Federated learning (FL) is an attractive machine learning paradigm that facilitates collaborative machine learning at the decentralized level and produces insightful results. However, in cases where the participating clients have non-IID (non independent and identical data) distributions, it produces suboptimal results. Furthermore, gradient leakage attacks have been shown in federated learning to be capable of leaking confidential information from global model parameters, hence posing a severe risk to user privacy. In this work, we develop a method called Fed-SHARC, a Secure Hierarchical model Aggregation by clustering in a decentralized federated learning framework that addresses data heterogeneity issues to achieve good performance while guarding the framework against gradient leakage attacks. Secure model aggregation occurs at two stages in this hierarchical approach. Phase-1 involves exploiting differential privacy to aggregate the models of the reward-driven clustered clients. On the other hand, giving each participant with heterogeneous data the same privacy budget will have a significant impact on the performance. As an effective multi-participant budget allocation technique, we suggest an efficient weighted noise injection policy that modifies the privacy budget based on the data distributions of clients. Phase-2 involves selecting a leader from each cluster's highly rewarded clients to take part in the multi-party computation enabled inter-cluster secure model aggregation. Experiments conducted on three benchmark public datasets demonstrate the effectiveness of the proposed Fed-SHARC in terms of privacy and performance. Furthermore, we verify its resilience against gradient leakage attacks.

关键词： Decentralised Federated learning Reward-driven Clustering Multi-Party Computation Weighted noise injection policy Gradient leakage Attack

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Formulations for data-driven control Design and Reinforcement learning 17

Formulations for Data-Driven Control Design and Reinforcemen...

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ieee 17th International conference on control and Automation (ICCA)

作者： Lee, Donghwan Kim, Do Wan Korea Adv Inst Sci & Technol Dept Elect Engn Daejeon 34141 South Korea Hanbat Natl Univ Dept Elect Engn Daejeon 34158 South Korea

ISBN: (数字)9781665495721

ISBN: (纸本)9781665495721

The goal of this paper is to investigate modelfree data-driven control design strategies for unknown systems. In particular, we report new data-driven linear matrix inequalities (LMIs) and dynamic programming (DP) methods. Both continuous-time and discrete-time systems are considered. We consider data transition equations that include complete information on the system model using state-input trajectories. Instead of computing explicit system model, the data transition equations are used to construct data-dependent LMI and DP formulations. The proposed formulations provide additional insights in data-driven control designs. In addition, we regard the proposed methods as a complement rather than replacement of existing methods.

关键词： Discrete-time systems Automation control design Computational modeling Reinforcement learning Mathematical models data models

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data-driven Optimal Consensus control for Switching Multiagent systems via Joint Communication Graph

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ieee TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第4期20卷 5959-5968页

作者： He, Wenpeng Chen, Xin Zhang, Menglin Sun, Yipu Sekiguchi, Akinori She, Jinhua China Univ Geosci Sch Automat Wuhan 430074 Peoples R China Hubei Key Lab Adv Control & Intelligent Automat Co Wuhan 430074 Peoples R China Minist Educ Engn Res Ctr Intelligent Technol Geoexplorat Wuhan 430074 Peoples R China Tokyo Univ Technol Sch Engn Tokyo 1920982 Japan

This article investigates optimal consensus problems of switching multiagent systems (MASs). For such kind of MASs, local neighborhood tracking error (LNTE) system is time varying because of the switching communication graph (CG). Existing performance index defined on the LNTE system is thus invalid for the switching MASs. This article addresses this problem by defining a new augmented LNTE system. The augmented LNTE system is constructed using the joint CG and is thus time-invariant. Subsequently, the optimal consensus problems for the MASs are formulated using the augmented LNTE system. Value iteration algorithm that employs an actor-critic network is used to learn the optimal controller. The article provides a theoretical analysis demonstrating the learning stability and control stability of the value iteration method. Furthermore, we also show that the MASs will reach approximate Nash equilibrium. Simulation results proves the effectiveness of the proposed method.

关键词： Augmented local neighborhood tracking error (LNTE) joint communication graph (CG) optimal consensus switchingmultiagent system (MAS) value iteration

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data Generation using Latin Hypercube Sampling Approach for Power System Dynamic Security Assessment 1

Data Generation using Latin Hypercube Sampling Approach for ...

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1st ieee International conference on Smart Power control and Renewable Energy, ICSPCRE 2024

作者： Singh, Mahesh Pal Sinha, Nidul Nit Silchar Department of Electrical Engineering Assam India

ISBN: (纸本)9798350377002

data driven dynamic security assessment using Machine learning (ML) models based on advanced artificial intelligence techniques plays a very important role in the field of power systems. database robustness (redundant and highly informative) to manage security issues under abnormal situations and system operating limits affects ML model performance. Given current data availability of unusual circumstances and the need for more thorough techniques to producing such datasets which contains all dynamically critical locations in the power system operating space, this paper introduces a novel approach based on smart and exceptional Latin hypercube sampling techniques to generate the highly quality datasets. Convex relaxation techniques are employed to explore the highly informative regions by using certificate of infeasibility for a specified security threshold. It can efficiently build a database of all dynamically critical power system operating stages (offline) and train ML models for real-time security assessment. The suggested method is computationally efficient (having compact size dataset with minimum computational efforts, and time) and tractable for huge and complex power systems. The suggested method creates datasets for two typical ieee systems (14 and 57bus) to train the Machine learning model to assess the system's operating state (insecure or secure) including the stability margin. © 2024 ieee.

关键词： Support vector machines

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