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Supervised reinforcement learning based trajectory tracking control for autonomous vehicles ⁎

IFAC-PapersOnLine 2024年第10期58卷 140-145页

作者： András Mihály Van Tan Vu Trong Tu Do Péter Gáspár Systems and Control Laboratory Institute for Computer Science and Control (SZTAKI) Hungarian Research Network Kende u. 13-17 H-1111 Budapest Hungary Department of Automotive Mechanical Engineering Faculty of Mechanical Engineering University of Transport and Communications Hanoi Vietnam Mechanical and Power Engineering Faculty Electric Power University Hanoi Vietnam

Recent developments in autonomous vehicle technologies and applications gain a lot of interest by the public, as the popularity of both driver assistance and automated driving systems increase. One of the most promising aspect of the autonomous vehicle compared to conventional human driven vehicle is the increased level of safety. Machine learning techniques enables to achieve fast and efficient control actions compared to model based techniques. However, the advantages of a more conservative model based controller are their better robustness properties. In this paper a synergy of the two control philosophy is presented through a trajectory tracking control design for autonomous vehicles. A supervised reinforcement learning (RL) control method is introduced, where a robust Linear Parameter Varying (LPV) controller supervises the operation of the trained RL agent. Thus, in case sensor noise is detected, the guaranteed stability LPV controller takes over the steering control action. In order to demonstrate the operation of the proposed method, three different simulations have been evaluated and compared in CarSim simulation environment.

关键词： autonomous vehicle control supervised reinforcement learning trajectory tracking

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Manipulating the Posterior Support of a Discrete Bayesian Estimator Under Full Sensor control

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IFAC-PapersOnLine 2023年第2期56卷 252-257页

作者： Daniel Selvaratnam Farhad Farokhi Iman Shames Henrik Sandberg Decision and Control Systems School of Electrical Engineering & Computer Science KTH Royal Institute of Technology 114 28 Stockholm Sweden Department of Electrical and Electronic Engineering The University of Melbourne Parkville VIC 3010 Australia College of Engineering & Computer Science Australian National University Canberra ACT 2601 Australia

The asymptotic implausibility problem is introduced from the perspective of an adversary that seeks to drive the belief of a recursive Bayesian estimator away from a particular set of parameter values. It is assumed that the adversary controls all sensors informing the estimator, and can transmit false measurements stochastically according to a fixed distribution of its choice. First, we outline a method for verifying whether a given distribution solves the problem. We then consider the class of spoofing attacks, and show that the asymptotic implausibility problem has a solution if and only if it can be solved by a spoofing attack. Attention is restricted to finite parameter and observation spaces.

关键词： Network Security Bayesian Estimation Cyber-Physical Systems Network Systems Resilient control Systems Signal Processing

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Correction to: Generalized Robot Vision-Language Model via Linguistic Foreground-Aware Contrast (International Journal of computer Vision, (2025), 10.1007/s11263-024-02340-z)

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International Journal of computer Vision 2025年第7期133卷 4971-4971页

作者： Liu, Kangcheng Wang, Chaoqun Han, Xiaodong Liu, Yong-Jin Chen, Baoquan Changsha China Pasadena United States The School of Control Science and Engineering Shandong University Jinan China The School of Control Engineering Minjiang University Fuzhou China The Department of Computer Science and Technology Tsinghua University Beijing China The School of Artificial Intelligence Peking University Beijing China

The original version of this article contained inadvertent typos requiring the following revisions. The article title was given as "Generalized Robotic Vision-Language Learning Model via Linguistic Foreground-Aware Contrast" but should have been "Generalized Robot Vision-Language Model via Linguistic Foreground-Aware Contrast". The affiliation details for Author Chaoqun Wang were given as "The department of computer science and Technology, Tsinghua University, Beijing, China" but should have been "The School of control science and engineering, Shandong University, Jinan, China". The affiliation details for Author Xiaodong Han were given as "The School of control science and engineering, Shandong University, Jinan, China" but should have been "The School of control engineering, Minjiang University, Fuzhou, China". The affiliation details for Author Yong-Jin Liu were given as "The School of control engineering, Minjiang University, Fuzhou, China" but should have been "The department of computer science and Technology, Tsinghua University, Beijing, China". In this article, the part of the caption to Fig. 3 "Please refer to the colored figures online for detailed information" was inadvertently omitted. The complete caption of Fig. 3 is given here. Figure 3 Visualizations of projected point correlation maps over the indoor ScanNet (1st-4th rows) and the outdoor KITTI (5th-8th rows) with respect to the query points highlighted by yellow crosses. The View 1 and View 2 in each sample show the intra-view and cross-view correlations, respectively. We compare FAC with the state-of-the-art CSC (Hou et al., 2021) on segmentation (rows 1–4) and ProCo (Yin et al., 2022) on detection (rows 5–8). FAC clearly captures better feature correlations within and across views (columns 3–4). Please refer to the colored figures online for detailed information In this article, the footnote "The bold highlights the results of our proposed approaches" to Table 1 was inadvertently omitted. In Tab

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Development of an Executive Design of a Station for Measuring the Curvature of Metallurgical Charge for Rolling 21st

Development of an Executive Design of a Station for Measurin...

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21st Polish control Conference, PCC 2023

作者： Placzek, Marek Krauze, Piotr Ryfa, Arkadiusz Jablonka, Tomasz Balys, Karol Helmich, Magdalena Badora, Dawid Florkiewicz, Bartosz Polak, Kamil Slonka, Piotr Trybulowski, Mateusz Faculty of Mechanical Engineering Silesian University of Technology Konarskiego 18A Gliwice44-100 Poland Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice44-100 Poland Faculty of Energy and Environmental Engineering Silesian University of Technology Konarskiego 18 Gliwice44-100 Poland MOSTOSTAL ZABRZE Biprohut S.A Dubois 16 Gliwice44-100 Poland

ISBN: (纸本)9783031351723

The work is a report on the implementation of interdisciplinary group project classes conducted in accordance with the Project Based Learning approach. The aim of the project was to develop an automated station for measuring the curvature of metallurgical charge and to create its technical documentation. The station is intended to work in cooperation with devices of an automated line for spreading and transporting metallurgical charges, which is a part of the technological line for hot rolling. The task of the station is to identify the curvature value, so that a charge with exceeded parameters does not proceed to the next stage of the process (the goal is significant in terms of eliminating defects). The final outcome of the project was to be a complete technical documentation of the designed station along with the measurement algorithm. Additionally, a laboratory test station and a system design for use in real industrial conditions were developed. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Hot rolling

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Privacy-Preserving Task-Oriented Semantic Communications Against Model Inversion Attacks

arXiv

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arXiv 2023年

作者： Wang, Yanhu Guo, Shuaishuai Deng, Yiqin Zhang, Haixia Fang, Yuguang School of Control Science and Engineering Shandong University Jinan250061 China Department of Computer Science City University of Hong Kong Hong Kong

Semantic communication has been identified as a core technology for the sixth generation (6G) of wireless networks. Recently, task-oriented semantic communications have been proposed for low-latency inference with limited bandwidth. Although transmitting only task-related information does protect a certain level of user privacy, adversaries could apply model inversion techniques to reconstruct the raw data or extract useful information, thereby infringing on users’ privacy. To mitigate privacy infringement, this paper proposes an information bottleneck and adversarial learning (IBAL) approach to protect users’ privacy against model inversion attacks. Specifically, we extract task-relevant features from the input based on the information bottleneck (IB) theory. To overcome the difficulty in calculating the mutual information in high-dimensional space, we derive a variational upper bound to estimate the true mutual information. To prevent data reconstruction from task-related features by adversaries, we leverage adversarial learning to train encoder to fool adversaries by maximizing reconstruction distortion. Furthermore, considering the impact of channel variations on privacy-utility trade-off and the difficulty in manually tuning the weights of each loss, we propose an adaptive weight adjustment method. Numerical results demonstrate that the proposed approaches can effectively protect privacy without significantly affecting task performance and achieve better privacy-utility trade-offs than baseline methods. © 2023, CC BY.

关键词： Economic and social effects

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Abstracting Linear Stochastic Systems via Knowledge Filtering

Abstracting Linear Stochastic Systems via Knowledge Filterin...

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IEEE Conference on Decision and control

作者： M.H.W. Engelaar L. Romao Y. Gao M. Lazar A. Abate S. Haesaert Department of Electrical Engineering (Control Systems Group) Eindhoven University of Technology The Netherlands Department of Computer Science University of Oxford United Kingdom

In this paper, we propose a new model reduction technique for linear stochastic systems that builds upon knowledge filtering and utilizes optimal Kalman filtering techniques. This new technique will reduce the dimension of the noise disturbance and will allow any controller designed for the reduced model to be refined into a controller for the original stochastic system, while preserving any specification on the output. Although initially the reduced model will be time-varying, a method will be provided with which the reduced model can become time-invariant if it satisfies some minor technical conditions. We present our theoretical findings with an example that supports the proposed framework and illustrates how model reduction and controller refinement of stochastic systems can be achieved. We finish the paper by considering specific examples to analyze both completeness with respect to controller synthesis and model order reduction with respect to the state.

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Improving routing efficiency in vehicular AD Hoc Networks

Improving routing efficiency in vehicular AD Hoc Networks

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International Conference on control, Decision and Information Technologies (CoDIT)

作者： W. Akermi N. Alyaoui A. B. Guiloufi K. Chabbir N. Nasri Automatic Industrial Systems Department Applied Science and Technology Gafsa University Gafsa Tunisia Laboratory of Modeling Analysis and Control of Systems (MACS) National Engineering School of Gabes (ENIG) Gabes University Gabes Tunisia Computer Science and Telecommunications Department Higher Institute of Applied Science and Technology Gafsa University Gafsa Tunisia Preparatory Institute for Engineering Studies of Gabes Laboratory of Modeling Analysis and Control of Systems (MACS) National Engineering School of Gabes (ENIG) Gabes University Gabes Tunisia Computer Science Department-Gafsa Gafsa University Gafsa Tunisia Laboratory of Smart Systems for Engineering and E-Health Based on Technologies of Image and Telecommunications (SETIT) Sfax University Sfax Tunisia

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

Vehicular networks, also known as Vehicular Ad Hoc Networks (VANETs), have become a crucial technology for vehicle communication, providing wireless connectivity among vehicles and between vehicles and infrastructure. However, the dynamic and complex nature of these networks presents unique challenges in routing and resource management. In this study, we explore the use of artificial intelligence (AI) to improve the performance of vehicular networks, focusing on two widely used routing protocols: Optimized Link State Routing (OLSR) and Destination-Sequenced Distance Vector (DSDV).We aim to evaluate the performance of these traditional routing protocols within the context of vehicular networks by integrating machine learning techniques such as neural network regression (MLRegression). Our goal is to understand how the integration of artificial intelligence can enhance the prediction and optimization of vehicular network performance. Using MLRegression, we aim to develop accurate prediction models for performance metrics such as latency, throughput, packet delivery rate, and more, based on network parameters and environmental conditions. This comparative evaluation, which involves incorporating an AI-based approach, will allow us to better understand the strengths and weaknesses of each protocol and provide recommendations for improving vehicular networks.

关键词： Measurement Wireless communication Adaptation models Adaptive systems Vehicular ad hoc networks Routing Throughput Routing protocols Vectors Vehicle dynamics

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Weighted Fuzzy Rough Sets Feature Selection for High Dimensional Classification Problems

Weighted Fuzzy Rough Sets Feature Selection for High Dimensi...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Simon Peter Khabusi Yo-Ping Huang Van Phong Vu Department of Electrical Engineering National Taipei University of Technology Taipei Taiwan Department of Electrical Engineering National Penghu University of Science and Technology Penghu Taiwan Department of Automatic Control Ho Chi Minh City University of Technology and Education Ho Chi Minh City Vietnam

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Feature selection holds significant importance in knowledge mining as it plays a pivotal role in selecting and preserving the most informative features within a dataset while discarding irrelevant, redundant, or noisy attributes. This process contributes to enhancing model performance, reducing computational complexity, and refining interpretability, thus facilitating more accurate and efficient data analysis. In high-dimensional datasets, the necessity for feature selection becomes more pronounced due to the heightened risk of encountering the curse of dimensionality. Therefore, this study proposes a weighted fuzzy rough quickreduct (FRQR) feature selection approach employing feature weights to handle the equal situation problem inherent in FRQR. The proposed method is evaluated on ten publicly available datasets with feature sizes ranging from 2000 to 15154. The selected features are used to train and test random forest candidate models whose estimates are then combined according to the posterior probabilities by Bayesian Model Averaging (BMA). The performance of the model on the selected features is evaluated on four performance metrics. The essentiality of the selected features is further determined by comparing the model classification performance achieved on the non-selected features and all the dataset features. The results indicate competitiveness in the performance metric values achieved on selected features over the other two feature categories affirming the efficacy of the proposed method.

关键词： Data analysis Posterior probability Refining Rough sets Feature extraction Distance measurement Data models Noise measurement Data mining Diseases

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Traffic Pattern Classification in Smart Cities Using Deep Recurrent Neural Network

arXiv

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arXiv 2024年

作者： Ismaeel, Ayad Ghany Krishnadas, J. Manishankar, S. Natarajan, Yuvaraj Mahmood, Sarmad Nozad Alani, Sameer Shather, Akram H. Computer Technology Engineering College of Engineering Technology Al-Kitab University Kirkuk36001 Iraq Department of Computer Science and Engineering Sahrdaya College of Engineering and Technology Thrissur Kodakara680684 India Department of Computer Science and Engineering Sri Shakthi Institute of Engineering and Technology Coimbatore641062 India Electronic and Control Engineering Techniques Technical Engineering College Northern Technical University Kirkuk36001 Iraq Computer Center University of Anbar Ramadi55431 Iraq Department of Computer Engineering Technology Al-Kitab University Altun Kopru Kirkuk36001 Iraq

This paper examines the use of deep recurrent neural networks to classify traffic patterns in smart cities. We propose a novel approach to traffic pattern classification based on deep recurrent neural networks, which can effectively capture traffic patterns' dynamic and sequential features. The proposed model combines convolutional and recurrent layers to extract features from traffic pattern data and a SoftMax layer to classify traffic patterns. Experimental results show that the proposed model outperforms existing methods regarding accuracy, precision, recall, and F1 score. Furthermore, we provide an in-depth analysis of the results and discuss the implications of the proposed model for smart cities. The results show that the proposed model can accurately classify traffic patterns in smart cities with a precision of as high as 95%. The proposed model is evaluated on a real-world traffic pattern dataset and compared with existing classification methods. © 2024, CC BY-NC-ND.

关键词： Classification (of information)

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Exploiting Robots as Healthcare Resources for Epidemics Management and Support Caregivers 10

Exploiting Robots as Healthcare Resources for Epidemics Mana...

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10th Italian Workshop on Artificial Intelligence and Robotics, AIRO 2023

作者： Napoli, Christian Napoli, Christian Ponzi, Valerio Puglisi, Adriano Russo, Samuele Tibermacine, Imad Eddine Department of Computer Control and Management Engineering Sapienza University of Rome via Ariosto 25 Roma00185 Italy Institute for Systems Analysis and computer Science Italian National Research Council Via dei Taurini 19 Roma00185 Italy Department of Medical Surgical Sciences and Translational Medicine Sapienza University of Rome Via di Grottarossa 1035 Roma00189 Italy Department of Psychology Sapienza University of Rome via dei Marsi 78 Roma00185 Italy

The COVID-19 emergency has exposed the fragility of many Health Care Systems around the world. Two major critical factors have been related to the management of critical care accesses and the availability of healthcare operators. COVID-like diseases are generally transmitted by airborne pathogens that grant a high contagion rate and rapidity. Unfortunately, health operators and medical doctors are the designated first victims of such epidemics. Infected operators (symptomatic or not) must be put at rest due to the potential contagion risk for the patients. In the midst of global health catastrophes such as the COVID-19 pandemic, the healthcare sector is always looking for new ways to improve patient care while reducing the risk of disease transmission. This study takes an innovative approach to the integration of robotic technology in hospital settings in order to increase operational efficiency, protect healthcare personnel, and improve patient outcomes. This study digs into the strategic deployment of robotic resources, with an emphasis on their function in epidemic control and front-line caregiver assistance. We look at how these robots may do different duties, decreasing human-to-human contact and the possibility of viral transmission. We pave the road for a safer, more efficient, and resilient healthcare environment by using the potential of robots as important healthcare resources. © 2024 Copyright for this paper by its authors.

关键词： COVID-19

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