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Data-driven output regulation control for constrained linear systems

Science China(Information Sciences) 2025年第3期68卷 338-353页

作者： Chaoyu XIA Yi DONG Chaoli WANG Shengyuan XU Shanghai Research Institute for Intelligent Autonomous Systems Tongji University College of Electronic and Information Engineering Shanghai Research Institute for Intelligent Autonomous SystemsTongji University Department of Control Science and Engineering School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and Technology Department of Automation Nanjing University of Science and Technology

This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking performance while satisfying the state and input constraints, even when system matrices are not available. We first establish a sufficient condition necessary for the existence of a solution pair to the regulator equation and propose a data-based approach to obtain the feedforward and feedback control gains for state feedback control using linear programming. Furthermore, we design a refined Luenberger observer to accurately estimate the system state, while keeping the estimation error within a predefined set. By combining output regulation theory, we develop an output feedback control strategy. The stability of the closed-loop system is rigorously proved to be asymptotically stable by further leveraging the concept of λ-contractive sets.

关键词： output regulation constrained system data-driven.

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Prototype-Based Interpretable Graph Neural Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1486-1495页

作者： Ragno, Alessio Rosa, Biagio La Capobianco, Roberto Sapienza University of Rome Department of Computer Control and Management Engineering Rome00185 Italy Sony AI Zurich8952 Switzerland

Graph neural networks have proved to be a key tool for dealing with many problems and domains, such as chemistry, natural language processing, and social networks. While the structure of the layers is simple, it is difficult to identify the patterns learned by the graph neural network. Several works propose post hoc methods to explain graph predictions, but few of them try to generate interpretable models. Conversely, the topic of the interpretable models is highly investigated in image recognition. Given the similarity between image and graph domains, we analyze the adaptability of prototype-based neural networks for graph and node classification. In particular, we investigate the use of two interpretable networks, ProtoPNet and TesNet, in the graph domain. We show that the adapted networks manage to reach better or higher accuracy scores than their respective black-box models and comparable performances with state-of-the-art self-explainable models. Showing how to extract ProtoPNet and TesNet explanations on graph neural networks, we further study how to obtain global and local explanations for the trained models. We then evaluate the explanations of the interpretable models by comparing them with post hoc approaches and self-explainable models. Our findings show that the application of TesNet and ProtoPNet to the graph domain produces qualitative predictions while improving their reliability and transparency. © 2020 IEEE.

关键词： Computer architecture

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Synchronous control of H-type Linear Motor System via Super-Twisting Sliding Mode

Synchronous Control of H-type Linear Motor System via Super-...

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2024 China Automation Congress, CAC 2024

作者： Liu, Jiawei Li, Xiaolei Tang, Feilong Kui, Qixin Spasic, Miodrag D. Department of Control Science and Engineering Harbin Institute of Technology Harbin China Department of Control Systems Faculty of Electronic Engineering University of Nis Nis Serbia

ISBN: (纸本)9798350368604

In this paper, a synchronous control strategy based on super-twisting sliding mode algorithm is proposed to enhance the tracking accuracy and robustness of H-type linear motor systems. Such systems are widely utilized in precision manufacturing owing to their high precision and fast response. Firstly, the dynamic model is established based on the coupling dynamics analysis of the H-type system. Secondly, a super-twisting sliding mode controller (STSMC) with linear terms is designed, which effectively reduces the chattering associated with conventional sliding mode control, while maintaining good robustness. Thirdly, a super-twisting extended state observer (STESO) is introduced to handle large fluctuations in the early estimation process of linear extended state observer (LESO), so as to effectively observe and compensate the uncertainties and external disturbances. Finally, experimental results demonstrate that the STESO-based STSMC greatly improves the anti-disturbance ability and tracking accuracy of the H-type system. © 2024 IEEE.

关键词： Sliding mode control

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A Real-Time Machine Learning Based Solution for Privacy Enforcement in Video Recordings and Live Streaming 9

A Real-Time Machine Learning Based Solution for Privacy Enfo...

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9th International Conference of Yearly Reports on Informatics, Mathematics, and engineering, ICYRIME 2024

作者： Conforti, Pietro Manganelli Emanuele, Matteo Mandelli, Lorenzo Department of Computer Control and Management Engineering Sapienza University of Rome Italy

These past years the world had to deal with a whole new situation brought by Covid-19. Everyone’s routine changed and we started passing way more time than before on virtual meeting, virtual chats and similar. With this, many privacy problems arised from all the video data generated by a single user. Google and Zoom introduced the possibility to blur out the background while using a front face camera, but this did not solve many privacy concerns ranging from showing people in videos without their permission, to the leaking of sensible data and information from videos uploaded online. We propose a solution build over the use of computer vision techniques like image segmentation and classification for context recognition for a privacy enforcement solution capable of fitting the user’s personal need, blurring out selectively specific objects from a video based on the user’s preferences for each room in which they are. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Differential privacy

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UAV longitudinal autopilot design using SLC and TECS controllers

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CEAS Aeronautical Journal 2024年第2期15卷 351-362页

作者： Ghelem, Nourddine Boudana, Djamel Bouchhida, Ouahid Laboratory of Process Control LCP Department of Engineering Control Systems and Applied Mathematics National Polytechnic School ENP of Algiers 10 St Hacen Badi El Harrach Algiers Algeria Department of Electrical Engineering LREA Laboratory University of Medea Medea Algeria

UAVs are becoming increasingly prevalent in a wide range of fields, including surveillance, photography, agriculture, transportation, and communications. Hence, research institutions have developed a range of linear and non-linear controllers that can enhance the stability and efficiency of these UAVs while performing their assigned tasks. In this paper, we present a longitudinal autopilot design for the Sky Sailor solar UAV using two different control techniques. The first technique is a classic controller known as SLC (Successive Loop Closure), which is based on consecutive closed loops using a PID controller. The second technique is the TECS (Total Energy control System) controller, which uses the specified total energy rate and energy distribution rate to regulate the airspeed and altitude of the UAV. After detailing the working principle of each controller and calculating the tuning parameters for each one, we applied the two techniques to the non-linear model that describes the behavior of the Sky Sailor UAV using MATLAB Simulink. The simulation results showed that the TECS controller is superior to the SLC controller in terms of stability and energy efficiency. As such, this technique is an excellent choice for solar drones, as it can increase their endurance. © Deutsches Zentrum für Luft- und Raumfahrt e.V. 2024.

关键词： controllers

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Conversational AI for Framed Autonomy in AI-augmented Business Process management 22

Conversational AI for Framed Autonomy in AI-augmented Busine...

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Best Dissertation Award, Doctoral Consortium, and Demonstration and Resources Forum at 22nd International Conference on Business Process management, BPM-D 2024

作者： Casciani, Angelo Department of Computer Control and Management Engineering Sapienza University of Rome Italy

AI-augmented Business Process management systems (ABPMSs) represent an emerging category of process-aware information systems driven by AI technology. These systems autonomously manage the execution flow of business processes (BPs) within predefined frames, encompassing procedural and declarative specifications, which may sometimes conflict. Despite operating autonomously within these boundaries, ABPMSs require dynamic conversations with human agents. These conversations not only respond to user queries but also initiate discussions to inform them of BP progression and provide recommendations for performance improvement. This research proposal aims to leverage Conversational AI to support the ABPMS’s framed autonomy, functioning as a Decision Support System (DSS). This involves explaining system’s choices and suggesting actions when constraints are violated. This technique enables intelligent, context-aware interactions with ABPMSs, fostering user trust. Our findings indicate that Conversational AI has the potential to significantly enhance the interpretability and usability of ABPMSs, thereby facilitating improved decision-making and process optimization. © 2024 Copyright for this paper by its authors.

关键词： Chatbots

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DanteLLM: Let's Push Italian LLM Research Forward! 30

DanteLLM: Let's Push Italian LLM Research Forward!

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Bacciu, Andrea Campagnano, Cesare Trappolini, Giovanni Silvestri, Fabrizio Department of Computer Control and Management Engineering Sapienza University of Rome Italy

ISBN: (纸本)9782493814104

In recent years, the dominance of Large Language Models (LLMs) in the English language has become evident. However, there remains a pronounced gap in resources and evaluation tools tailored for non-English languages, underscoring a significant disparity in the global AI landscape. This paper seeks to bridge this gap, specifically focusing on the Italian linguistic context. We introduce a novel benchmark, and an open LLM Leaderboard, designed to evaluate LLMs' performance in Italian, providing a rigorous framework for comparative analysis. In our assessment of currently available models, we highlight their respective strengths and limitations against this standard. Crucially, we propose "DanteLLM", a state-of-the-art LLM dedicated to Italian. Our empirical evaluations underscore Dante's superiority, as it emerges as the most performant model on our benchmark, with improvements by up to 6 points. This research not only marks a significant stride in Italian-centric natural language processing but also offers a blueprint for the development and evaluation of LLMs in other languages, championing a more inclusive AI paradigm. Our code at: https://***/RSTLess-research/DanteLLM. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Benchmarking

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Instance-Wise MRI Reconstruction Based on Self-Supervised Implicit Neural Representation 46

Instance-Wise MRI Reconstruction Based on Self-Supervised Im...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Yang, Song-Xiao Li, Yi-Zhou Okutomi, Masatoshi Tokyo Institute of Technology School of Engineering Department of Systems and Control Engineering Tokyo Japan

ISBN: (纸本)9798350371499

Accelerated MRI involves a trade-off between sampling sufficiency and acquisition time. Supervised deep learning methods have shown great success in MRI reconstruction from under-sampled measurements, but they typically require a large set of fully-sampled MR images for training, which can be difficult to obtain. In this paper, we present a novel fully self-supervised method based on implicit neural representation, which requires only a single under-sampled MRI instance for training. To effectively guide the self-supervised learning process, we introduced multiple novel supervisory signals in both the image and frequency domains. Experimental results indicate that the proposed method outperforms existing self-supervised methods and even a supervised method, demonstrating its strong reliability and flexibility. Our code is publicly available at https://***/YSongxiao/*** relevance - The proposed method can significantly enhance the image quality of under-sampled MR images without the need of ground-truth fully-sampled MR images for supervision and additional prior images for guidance. © 2024 IEEE.

关键词： Self-supervised learning

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The Unscented Kalman Filter for Nonlinear Parameter Identification of Adaptive Cruise control systems

arXiv

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arXiv 2023年第8期8卷 4094页

作者： Ampountolas, Konstantinos The Automatic Control & Autonomous Systems Laboratory Department of Mechanical Engineering University of Thessaly Volos38334 Greece

This paper develops and investigates a dual unscented Kalman filter (DUKF) for the joint nonlinear state and parameter identification of commercial adaptive cruise control (ACC) systems. Although the core functionality of stock ACC systems, including their proprietary control logic and parameters, is not publicly available, this work considers a car-following scenario with a human-driven vehicle (leader) and an ACC engaged ego vehicle (follower) that employs a constant timeheadway policy (CTHP). The objective of the DUKF is to determine the CTHP parameters of the ACC by using real-time observations of space-gap and relative velocity from the vehicle's onboard sensors. Real-time parameter identification of stock ACC systems is essential for assessing their string stability, large-scale deployment on motorways, and impact on traffic flow and throughput. In this regard, L2and L∞string stability conditions are considered. The observability rank condition for nonlinear systems is adopted to evaluate the ability of the proposed estimation scheme to estimate stock ACC system parameters using empirical data. The proposed filter is evaluated using empirical data collected from the onboard sensors of two 2019 SUV vehicles, namely Hyundai Nexo and SsangYong Rexton, equipped with stock ACC systems;and is compared with batch and recursive least-squares optimization. The set of ACC model parameters obtained from the proposed filter revealed that the commercially implemented ACC system of the considered vehicle (Hyundai Nexo) is neither L2nor L∞string stable. Copyright © 2023, The Authors. All rights reserved.

关键词： Adaptive cruise control

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Toward the minimum vertex cover of complex networks using distributed potential games

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Science China(Information Sciences) 2023年第1期66卷 244-263页

作者： Jie CHEN Xiang LI Adaptive Networks and Control Lab Department of Electronic Engineering Fudan University Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous SystemsTongji University

Vertex cover of complex networks is essentially a major combinatorial optimization problem in network science, which has wide application potentials in engineering. To optimally cover the vertices of complex networks, this paper employs a potential game for the vertex cover problem, designs a novel cost function for network vertices, and proves that the solutions to the minimum value of the potential function are the minimum vertex covering(MVC) states of a general complex network. To achieve the optimal(minimum) covering states, we propose a novel distributed time-variant binary log-linear learning algorithm,and prove that the MVC state of a general complex network is attained under the proposed optimization algorithm. Furthermore, we estimate the upper bound of the convergence rate of the proposed algorithm,and show its effectiveness and superiority using numerical examples with representative complex networks and optimization algorithms.

关键词： minimum vertex cover Nash equilibrium convergence rate scale-free network random graph

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