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Unsupervised Multiple Proactive Behavior learning of Mobile Robots for Smooth and Safe Navigation

Unsupervised Multiple Proactive Behavior Learning of Mobile ...

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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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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 Multiple Access Optimization and Monitoring via Model-driven Bayesian learning

Digital Twin-Based Multiple Access Optimization and Monitori...

引用

ieee International conference on Communications (ieee ICC)

作者： Ruah, Clement Simeone, Osvaldo Al-Hashimi, Bashir Kings Coll London Dept Engn London England Kings Commun Learning & Informat Proc KCLIP Lab London England

ISBN: (纸本)9781538674628

Commonly adopted in the manufacturing and aerospace sectors, digital twin (DT) platforms are increasingly seen as a promising paradigm to control and monitor software-based, "open", communication systems, which play the role of the physical twin (PT). In the general framework presented in this work, the DT builds a Bayesian model of the communication system, which is leveraged to enable core DT functionalities such as control via multi-agent reinforcement learning (MARL) and monitoring of the PT for anomaly detection. We specifically investigate the application of the proposed framework to a simple case-study system encompassing multiple sensing devices that report to a common receiver. The Bayesian model trained at the DT has the key advantage of capturing epistemic uncertainty regarding the communication system, e.g., regarding current traffic conditions, which arise from limited PT-to-DT data transfer. Experimental results validate the effectiveness of the proposed Bayesian framework as compared to standard frequentist model-based solutions.

关键词： Digital Twin 6G Reinforcement learning Bayesian learning Model-based learning

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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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Pilot-Free Semantic Communication systems for Frequency-Selective Fading Channels

Pilot-Free Semantic Communication Systems for Frequency-Sele...

引用

ieee International conference on Communications (ieee ICC)

作者： Cao, Zijian Zhang, Hua Liang, Le Wang, Haotian Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Purple Mt Labs Nanjing 211111 Peoples R China

ISBN: (纸本)9798350333077

With the paradigm shift from traditional communication systems to machine learning-driven communication systems, deep learning-based semantic communication is considered as a potential approach to improve transmission efficiency. However, the time-varying and multipath effects of wireless channels significantly influence the performance of existing semantic communication systems. The conventional solutions to overcome channel impairments, i.e., pilot-driven channel estimation and channel equalization approaches, will complicate the system and introduce extra overhead. Therefore, this paper proposes a deep learning-driven pilot-free semantic communication framework for frequency-selective fading channels. The convolutional neural network-based channel feature extraction and data recovery modules are introduced in the receiver to implicitly extract channel information directly from the received signal and recover the semantic information. The simulation demonstrates that the proposed network can be applied to various frequency-selective fading channels and outperforms existing schemes in terms of computational complexity and semantic transmission accuracy.

关键词： deep learning pilot-free semantic communication end-to-end communication convolutional neural network

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

Data Generation using Latin Hypercube Sampling Approach for ...

引用

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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Stochastic data-driven Predictive control: Regularization, Estimation, and Constraint Tightening 20

Stochastic Data-Driven Predictive Control: Regularization, E...

引用

20th IFAC Symposium on System Identification (SYSID)

作者： Yin, Mingzhou Iannelli, Andrea Smith, Roy S. Swiss Fed Inst Technol Automat Control Lab Zurich Switzerland Univ Stuttgart Inst Syst Theory & Automat Control Stuttgart Germany

data-driven predictive control methods based on the Willems' fundamental lemma have shown great success in recent years. These approaches use receding horizon predictive control with nonparametric data-driven predictors instead of model-based predictors. This study addresses three problems of applying such algorithms under unbounded stochastic uncertainties: 1) tuning-free regularizer design, 2) initial condition estimation, and 3) reliable constraint satisfaction, by using stochastic prediction error quantification. The regularizer is designed by leveraging the expected output cost. An initial condition estimator is proposed by filtering the measurements with the one-step-ahead stochastic data-driven prediction. A novel constraint-tightening method, using second-order cone constraints, is presented to ensure high-probability chance constraint satisfaction. Numerical results demonstrate that the proposed methods lead to satisfactory control performance in terms of both control cost and constraint satisfaction, with significantly improved initial condition estimation. Copyright (c) 2024 The Authors.

关键词： data-driven control model predictive control estimation and filtering stochastic optimal control stochastic system identification

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Disturbance Rejection-Guarded learning for Vibration Suppression of Two-Inertia systems 20

Disturbance Rejection-Guarded Learning for Vibration Suppres...

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ieee 20th International conference on Automation Science and Engineering (CASE)

作者： Zhang, Fan Chen, Jinfeng Hu, Yu Gao, Zhiqiang Lv, Ge Lin, Qin Cleveland State Univ Ctr Adv Control Technol CACT 2121 Euclid Ave Cleveland OH 44115 USA Clemson Univ Dept Mech Engn 105 Sikes Hall Clemson SC 29634 USA

ISBN: (纸本)9798350358513;9798350358520

Model uncertainty presents significant challenges in vibration suppression of multi-inertia systems, which often rely on inaccurate nominal mathematical models due to system identification errors or unmodeled dynamics. An observer, such as an extended state observer (ESO), can estimate the discrepancy between the inaccurate nominal model and the true model, thus improving control performance via disturbance rejection. Conventional observer design is memoryless;once the estimated disturbance is obtained and sent to the controller, the data is discarded. In this paper, we propose a learningenabled ESO (L-ESO) with seamless integration of ESO and machine learning. The machine learning model attempts to predict the disturbance, using prior information to help the observer achieve faster convergence in disturbance estimation. Additionally, any imperfections in the machine learning model can be compensated for by the ESO, providing an assurance layer. We validated the effectiveness of this novel learningfor-control paradigm through simulation and physical tests on two-inertial motion control systems used for vibration studies. Video: https://***/OUerJ4w_esk

关键词： Machine learning Disturbance Rejection Extended State Observer Model Uncertainty

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