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Towards backward compatibility of ADRC: revisiting classical state-feedback control with integral compensator

Bulletin of the Polish Academy of Sciences: Technical Sciences 2025年第2期73卷

作者： Mrotek, Mikolaj Michalski, Jacek Madonski, Rafal Pazderski, Dariusz Retinger, Marek Faculty of Control Robotics and Electrical Engineering Institute of Robotics and Machine Intelligence Poznan University of Technology Piotrowo 3a Poznan60-965 Poland Faculty of Automatic Control Electronics and Computer Science Department of Automatic Control and Robotics Silesian University of Technology Akademicka 16 Gliwice44-100 Poland Faculty of Control Robotics and Electrical Engineering Institute of Automatic Control and Robotics Poznan University of Technology Piotrowo 3a Poznan60-965 Poland

In this paper, the problem of backward compatibility of active disturbance rejection control (ADRC) is investigated. The goal is to contextualize ADRC to deliver its interpretations from the established field of linear control systems. For this study, a control algorithm, denoted here as integral disturbance rejection control (IDRC), is considered that combines classical state-feedback control with an integral compensator. At first, an interpretation of ADRC is involved in terms of existing state-space control approaches. Next, a transition to the frequency domain is performed, which is justified as a significant part of practical control engineering is conducted in that domain. For assumed specific plant structures, both ADRC and IDRC are then holistically compared in terms of transfer function representation and frequency characteristics, as well as steady-state convergence conditions. Such a juxtaposition helps to highlight the similarities and differences of both approaches, whereas the utilized bandwidth parameterization is shown to bring the control system to the same form, thus indicating some interesting practical aspects. Finally, the theoretical results concerning both considered control structures are validated in a set of numerical simulations and experiments conducted on a laboratory hardware testbed. © 2025 The Author(s).

关键词： Disturbance rejection

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A General Control-Theoretic Approach for Reinforcement Learning: Theory and Algorithms

arXiv

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arXiv 2024年

作者： Chen, Weiqin Squillante, Mark S. Wu, Chai Wah Paternain, Santiago Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute United States Mathematical Sciences Department IBM Research

We devise a control-theoretic reinforcement learning approach to support direct learning of the optimal policy. We establish various theoretical properties of our approach, such as convergence and optimality of our analog of the Bellman operator and Q-learning, a new control-policy-variable gradient theorem, and a specific gradient ascent algorithm based on this theorem within the context of a specific control-theoretic framework. We empirically evaluate the performance of our control-theoretic approach on several classical reinforcement learning tasks, demonstrating significant improvements in solution quality, sample complexity, and running time of our approach over state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Position: scarce resource allocations that rely on machine learning should be randomized 24

Position: scarce resource allocations that rely on machine l...

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Proceedings of the 41st International Conference on Machine Learning

作者： Shomik Jain Kathleen Creel Ashia Wilson Institute for Data Systems and Society MIT Department of Philosophy & Religion and Khoury College of Computer Sciences Northeastern University Institute for Data Systems and Society and Department of Electrical Engineering and Computer Science MIT

Contrary to traditional deterministic notions of algorithmic fairness, this paper argues that fairly allocating scarce resources using machine learning often requires randomness. We address why, when, and how to randomize by proposing stochastic procedures that more adequately account for all of the claims that individuals have to allocations of social goods or opportunities.

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BER Improvement of Cooperative Spectrum Sharing of NOMA in 5G Network 3

BER Improvement of Cooperative Spectrum Sharing of NOMA in 5...

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3rd IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer engineering, MI-STA 2023

作者： Hassan, Mohamed Saeed, Rashid A. Mokhtar, Rania A. Hamid, Khalid Ali, Elmustafa Sayed Odeh, Nidhal Singh, Manwinder Khalifa, Othman O. Islam, Shayla Lovely Professional University Department of Wireless Communication Punjab Phagwara144001 India Taif University Department of Computer Engineering College of Computers and Information Technology Taif21944 Saudi Arabia University of Science & Technology Department of Communication Systems Engineering Omdurman Sudan Department of Electronics Engineering Khartoum Sudan University of Wollongong School of Electrical Computer and Telecommunications Engineering Australia International Islamic University Malaysia Department of Electrical and Computer Engineering Gombak Malaysia Libyan Center for Engineering Resarch and Information Technology Bani Waleed Libya Institute of Computer Science and Digital Innovation Ucsi University Malaysia Department of Computer Science Kuala Lumpur56000 Malaysia

ISBN: (纸本)9798350319897

Non-orthogonal multiple access is one of the best methods for addressing the needs of 5G wireless services (NOMA). The proliferation of mobile devices in recent years has increased the importance of cooperative spectrum sharing and utilization in wireless communication due to increases in the bit error rate (BER) brought on by collisions and interference. In this study, MIMO and Massive MIMO (M-MIMO) in the downlink (DL) NOMA power domain (PD) in conjunction with the Cooperative Cognitive Radio Network are proposed as two new ways for improving and evaluating BER in the 5G network (CCRN). Customers of NOMA compete for available channels on the CCRN in this first strategy. The second approach creates a dedicated channel for NOMA users. Three scenarios with varying distances, power location coefficients, and transmission power are used to evaluate the proposed methods in the MATLAB software program. It is assumed that four users will share a 90 MHz BW using QPSK modulation in all three scenarios. When evaluating the performance of the suggested system under the presumption of frequency-selective Rayleigh fading, concurrent interference cancellation (SIC) and unstable channel conditions are also considered. © 2023 IEEE.

关键词： 5G mobile communication systems

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Towards AI-driven Next Generation Personalized Healthcare and Well-being

Towards AI-driven Next Generation Personalized Healthcare an...

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2024 Ital-IA Intelligenza Artificiale - Thematic Workshops, Ital-IA 2024

作者： Aksu, Fatih Bria, Alessandro Caragliano, Alice Natalina Caruso, Camillo Maria Chen, Wenting Cordelli, Ermanno Coser, Omar Francesconi, Arianna Furia, Leonardo Guarrasi, Valerio Iannello, Giulio Lauretti, Clemente Manni, Guido Marino, Giustino Paolo, Domenico Ruffini, Filippo Shen, Linlin Sicilia, Rosa Soda, Paolo Tamantini, Christian Tortora, Matteo Wu, Zhuoru Zollo, Loredana Department of Biomedical Sciences Humanitas University Milan Italy Department of Electrical and Information Engineering University of Cassino and Southern Latium Cassino Italy Unit of Computer Systems and Bioinformatics Department of Engineering University Campus Bio-Medico of Rome Italy City University of Hong Kong Hong Kong Unit of Advanced Robotics and Human-Centered Technologies Department of Engineering University Campus Bio-Medico of Rome Italy Shenzhen University China Department of Diagnostics and Intervention Radiation Physics Biomedical Engineering Umeå University Sweden

In the last few years Artificial Intelligence (AI) is emerging as a game changer in many areas of society and, in particular, its integration in medicine heralds a transformative approach towards personalized healthcare and well-being, promising significant improvements in diagnostic precision, therapeutic outcomes, and patient care. Our research explores the cutting-edge realms of multimodal AI, resilient AI, and healthcare robotics, aiming to harness the synergy of diverse data modalities and advanced computational models to redefine healthcare paradigms. This multidisciplinary effort seeks to bridge technology and clinical practice, advancing AI-driven next generation personalized healthcare and well-being. © 2024 Copyright for this paper by its authors.

关键词： Personalized medicine

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A Multimodal Learning-based Approach for Autonomous Landing of UAV

A Multimodal Learning-based Approach for Autonomous Landing ...

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IEEE/ASME International Conference on Mechtronic and Embedded systems and Applications, MESA

作者： Francisco S. Neves Luís M. Branco Maria I. Pereira Rafael M. Claro Andry M. Pinto Department of Electrical and Computer Engineering (DEEC) Faculty of Engineering of University of Porto (FEUP) Institute for Systems and Computer Engineering - Technology and Science (INESC TEC) Porto Portugal

ISBN: (数字)9798331516239

ISBN: (纸本)9798331516246

In the field of autonomous Unmanned Aerial Vehicles (UAVs) landing, conventional approaches fall short in delivering not only the required precision but also the resilience against environmental disturbances. Yet, learning-based algorithms can offer promising solutions by leveraging their ability to learn the intelligent behaviour from data. On one hand, this paper introduces a novel multimodal transformer-based Deep Learning detector, that can provide reliable positioning for precise autonomous landing. It surpasses standard approaches by addressing individual sensor limitations, achieving high reliability even in diverse weather and sensor failure conditions. It was rigorously validated across varying environments, achieving optimal true positive rates and average precisions of up to 90%. On the other hand, it is proposed a Reinforcement Learning (RL) decision-making model, based on a Deep Q-Network (DQN) rationale. Initially trained in simulation, its adaptive behaviour is successfully transferred and validated in a real outdoor scenario. Furthermore, this approach demonstrates rapid inference times of approximately 5ms, validating its applicability on edge devices.

关键词： Training Wind Image edge detection Decision making Detectors Reinforcement learning Robot sensing systems Autonomous aerial vehicles Transformers Resilience

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IoT with Evolutionary Algorithm Based Deep Learning for Smart Irrigation System

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computers, Materials & Continua 2022年第4期71卷 1713-1728页

作者： P.Suresh R.H.Aswathy Sridevi Arumugam Amani Abdulrahman Albraikan Fahd N.Al-Wesabi Anwer Mustafa Hilal Mohammad Alamgeer Department of Computer Science and Engineering KPR Institute of Engineering and TechnologyCoimbatore641407India Department of Electrical Sciences Adithya Institute of TechnologyCoimbatore641107India Department of Computer Science College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia&Faculty of Computer and ITSana’a UniversitySana’aYemen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlkharjSaudi Arabia Department of Information Systems King Khalid UniversityMuhayel AseerSaudi Arabia

In India, water wastage in agricultural fields becomes a challengingissue and it is needed to minimize the loss of water in the irrigation *** the conventional irrigation system needs massive quantity of waterutilization, a smart irrigation system can be designed with the help of recenttechnologies such as machine learning (ML) and the Internet of Things (IoT).With this motivation, this paper designs a novel IoT enabled deep learningenabled smart irrigation system (IoTDL-SIS) technique. The goal of theIoTDL-SIS technique focuses on the design of smart irrigation techniquesfor effectual water utilization with less human interventions. The proposedIoTDL-SIS technique involves distinct sensors namely soil moisture, temperature, air temperature, and humidity for data acquisition purposes. The sensordata are transmitted to the Arduino module which then transmits the sensordata to the cloud server for further process. The cloud server performs the dataanalysis process using three distinct processes namely regression, clustering,and binary classification. Firstly, deep support vector machine (DSVM) basedregression is employed was utilized for predicting the soil and environmentalparameters in advances such as atmospheric pressure, precipitation, solarradiation, and wind speed. Secondly, these estimated outcomes are fed intothe clustering technique to minimize the predicted error. Thirdly, ArtificialImmune Optimization Algorithm (AIOA) with deep belief network (DBN)model receives the clustering data with the estimated weather data as inputand performs classification process. A detailed experimental results analysisdemonstrated the promising performance of the presented technique over theother recent state of art techniques with the higher accuracy of 0.971.

关键词： Smart irrigation precision agriculture internet of things deep learning machine learning

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Self-Righting Shell for Robotic Hexapod

Self-Righting Shell for Robotic Hexapod

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IEEE International Conference on robotics and Automation (ICRA)

作者： Katelyn King Shai Revzen Department of Biomedical Engineering Robotics Institute University of Michigan Ann Arbor Department of Electrical Engineering and Computer Science Department of Ecology and Evolutionary Biology University of Michigan Ann Arbor

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Decimeter scale robots in human environments are small relative to obstacles they encounter, making them prone to flipping over and needing to self-right. We present a multifaceted shell that by its geometry alone enables the hexapedal robot MediumANT to passively self-right without the need for additional sensory feedback. We designed the shell by specifying the cross-sectional geometry in the yz and xy planes such that the robot returns to an upright position by rolling around the longitudinal (x) axis, and then tweaked the design to reduce the number of faces. We then attached the shell to the robot by modifying some of its chassis structural plates to extend to and support the shell. We evaluated the effectiveness of the shell in two experimental scenarios: passive righting – balancing the robot on each face of the shell before releasing the robot – and an intentional fall – walking the robot off a ledge at various approach angles. As intended by our design, the robot recovered the upright orientation from all starting faces in the passive righting test and righted itself and continued walking in all falling trials. This work presents an example of using biologically inspired simplicity to solve what would otherwise be a technically challenging problem.

关键词： Legged locomotion Geometry Structural plates Robot sensing systems Biology Faces

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OPTIMAL MULTI-OBJECTIVE BEST ARM IDENTIFICATION WITH FIXED CONFIDENCE

arXiv

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arXiv 2025年

作者： Chen, Zhirui Karthik, P.N. Chee, Yeow Meng Tan, Vincent Y.F. Department of Electrical and Computer Engineering Department of Industrial Systems Engineering and Management National University of Singapore Singapore Department of Industrial Systems Engineering and Management National University of Singapore Singapore Department of Mathematics Department of Electrical and Computer Engineering National University of Singapore Singapore Department of Artificial Intelligence Indian Institute of Technology Hyderabad India

We consider a multi-armed bandit setting with finitely many arms, in which each arm yields an M-dimensional vector reward upon selection. We assume that the reward of each dimension (a.k.a. objective) is generated independently of the others. The best arm of any given objective is the arm with the largest component of mean corresponding to the objective. The end goal is to identify the best arm of every objective in the shortest (expected) time subject to an upper bound on the probability of error (i.e., fixed-confidence regime). We establish a problem-dependent lower bound on the limiting growth rate of the expected stopping time, in the limit of vanishing error probabilities. This lower bound, we show, is characterised by a max-min optimisation problem that is computationally expensive to solve at each time step. We propose an algorithm that uses the novel idea of surrogate proportions to sample the arms at each time step, eliminating the need to solve the max-min optimisation problem at each step. We demonstrate theoretically that our algorithm is asymptotically optimal. In addition, we provide extensive empirical studies to substantiate the efficiency of our algorithm. While existing works on pure exploration with multi-objective multi-armed bandits predominantly focus on Pareto frontier identification, our work fills the gap in the literature by conducting a formal investigation of the multi-objective best arm identification problem. Copyright © 2025, The Authors. All rights reserved.

关键词： Pareto principle

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SF-DQN: Provable Knowledge Transfer using Successor Feature for Deep Reinforcement Learning 41

SF-DQN: Provable Knowledge Transfer using Successor Feature ...

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41st International Conference on Machine Learning, ICML 2024

作者： Zhang, Shuai Fernando, Heshan Devaka Liu, Miao Murugesan, Keerthiram Lu, Songtao Chen, Pin-Yu Chen, Tianyi Wang, Meng Department of Data Science New Jersey Institute of Technology NewarkNJ United States Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY United States IBM Thomas J.Watson Research Center IBM Research Yorktown HeightsNY United States

This paper studies the transfer reinforcement learning (RL) problem where multiple RL problems have different reward functions but share the same underlying transition *** this setting, the Q-function of each RL problem (task) can be decomposed into a successor feature (SF) and a reward mapping: the former characterizes the transition dynamics, and the latter characterizes the task-specific reward *** Q-function decomposition, coupled with a policy improvement operator known as generalized policy improvement (GPI), reduces the sample complexity of finding the optimal Q-function, and thus the SF & GPI framework exhibits promising empirical performance compared to traditional RL methods like ***, its theoretical foundations remain largely unestablished, especially when learning the successor features using deep neural networks (SF-DQN).This paper studies the provable knowledge transfer using SFs-DQN in transfer RL *** establish the first convergence analysis with provable generalization guarantees for SF-DQN with *** theory reveals that SF-DQN with GPI outperforms conventional RL approaches, such as deep Q-network, in terms of both faster convergence rate and better *** experiments on real and synthetic RL tasks support the superior performance of SF-DQN & GPI, aligning with our theoretical findings. Copyright 2024 by the author(s)

关键词： Deep reinforcement learning

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