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IEEE Conference on Decision and control

作者： Apostolos I. Rikos Wei Jiang Themistoklis Charalambous Karl H. Johansson Department of Electrical and Computer Engineering Division of Systems Engineering Boston University Boston MA US Hong Kong China Department of Electrical and Computer Engineering School of Engineering University of Cyprus Nicosia Cyprus Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo Finland Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden

In this paper, we focus on an asynchronous distributed optimization problem. In our problem, each node is endowed with a convex local cost function, and is able to communicate with its neighbors over a directed communication network. Furthermore, we assume that the communication channels between nodes have limited bandwidth, and each node suffers from processing delays. We present a distributed algorithm which combines the Alternating Direction Method of Multipliers (ADMM) strategy with a finite time quantized averaging algorithm. In our proposed algorithm, nodes exchange quantized valued messages and operate in an asynchronous fashion. More specifically, during every iteration of our algorithm each node (i) solves a local convex optimization problem (for the one of its primal variables), and (ii) utilizes a finite-time quantized averaging algorithm to obtain the value of the second primal variable (since the cost function for the second primal variable is not decomposable). We show that our algorithm converges to the optimal solution at a rate of O (1/ k) (where $k$ is the number of time steps) for the case where the local cost function of every node is convex and not-necessarily differentiable. Finally, we demonstrate the operational advantages of our algorithm against other algorithms from the literature.

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Distributed Low-Effort Load Balancing in the Presence of Time-Delays

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IFAC-PapersOnLine 2023年第2期56卷 5191-5196页

作者： Themistoklis Charalambous Stefano Panzieri Gabriele Oliva Department of Electrical and Computer Engineering School of Engineering University of Cyprus Nicosia Cyprus University Roma Tre via della Vasca Navale 79 00146 Rome Italy Unit of Automatic Control Department of Engineering Università Campus Bio-Medico di Roma Rome Italy

In this paper, we investigate the problem of distributed load balancing under network capacity constraints, where the participating agents cooperate with the aim of jointly minimizing both the workload disparity among them as well as the overall workload transfer. Classical approaches for asymptotic convergence to the global optimum in a distributed fashion typically assume timely exchange of information between neighboring agents of a given multi-agent system. This assumption is not necessarily valid in practical settings due to non-commensurate (heterogeneous) communication and processing delays that might affect transmissions at different times. More specifically, we consider what effect multiple heterogeneous time-varying delays, among the agents can have on the distributed load balancing problem. We show that the distributed load balancing problem under bounded heterogeneous time-varying delays is globally asymptotically stable, but the rate of convergence is affected. Bounds on the convergence rate are provided with respect to an upper bound on the delays. Simulation examples are provided to show the validity and performance of our theoretical results.

关键词： Distributed load balancing time-delays distributed algorithms minimum effort

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Distributed Optimization with Efficient Communication, Event-Triggered Solution Enhancement, and Operation Stopping

arXiv

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arXiv 2025年

作者： Rikos, Apostolos I. Jiang, Wei Charalambous, Themistoklis Johansson, Karl H. Guangzhou China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Clear Water Bay Hong Kong Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo Finland Department of Electrical and Computer Engineering University of Cyprus Nicosia1678 Cyprus Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Finland Division of Decision and Control Systems KTH Royal Institute of Technology StockholmSE-100 44 Sweden Digital Futures United Kingdom

In modern large-scale systems with sensor networks and IoT devices it is essential to collaboratively solve complex problems while utilizing network resources efficiently. In our paper we present three distributed optimization algorithms that exhibit efficient communication among nodes. Our first algorithm presents a simple quantized averaged gradient procedure for distributed optimization, which is shown to converge to a neighborhood of the optimal solution. Our second algorithm incorporates a novel event-triggered refinement mechanism, which refines the utilized quantization level to enhance the precision of the estimated optimal solution. It enables nodes to terminate their operation according to predefined performance guarantees. Our third algorithm is tailored to operate in environments where each message consists of only a few bits. It incorporates a novel event-triggered mechanism for adjusting the quantizer basis and quantization level, allowing nodes to collaboratively decide operation termination based on predefined performance criteria. We analyze the three algorithms and establish their linear convergence. Finally, an application on distributed sensor fusion for target localization is used to demonstrate their favorable performance compared to existing algorithms in the literature. © 2025, CC BY.

关键词： Dynamic programming

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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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Decentralised Iterative Learning control for High Performance Collaborative Tracking Problem with Output Constraints

Decentralised Iterative Learning Control for High Performanc...

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UKACC International Conference on control (control)

作者： Bin Chen Bing Chu Department of Automatic Control and Systems Engineering University of Sheffield Sheffield U.K. School of Electronics and Computer Science University of Southampton Southampton U.K.

ISBN: (数字)9798350374261

ISBN: (纸本)9798350374278

High performance collaborative tracking problem, requiring a group of independent subsystems to generate a global output that can precisely track the desired reference in a repetitive manner, has found lots of applications in practice. However, for such an important control task, existing iterative learning control (ILC) methods have not considered the constraint on each subsystem's output, which leads to potential risk within the control process. This paper proposes a novel optimisation based ILC method to address the high performance collaborative tracking problem with output constraints. The proposed ILC framework can guarantee not only each subsystem's output constraint is always satisfied during the control process, but also the monotonic convergence of a well-defined performance index to a possibly minimum value. To avoid huge computational complexity for large scale systems, we further apply the idea of the alternative direction method of multipliers (ADMM) to implement the proposed ILC frame-work in a decentralised manner, which allows the resulting decentralised methods to be applied to large scale and changing systems. Moreover, the decentralised ILC method proposed in this paper is suitable for non-minimum phase, heterogeneous and/or homogeneous systems, which is appealing in practice. Convergence properties of the proposed ILC algorithms are analysed rigorously, and numerical examples are given to demonstrate the algorithms' effectiveness.

关键词： Collaboration Process control Performance analysis Large-scale systems Task analysis Computational complexity Optimization

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Path Planning of Quadrotors in a Dynamic Environment Using a Multicriteria Multi-Verse Optimizer

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Computers, Materials & Continua 2021年第11期69卷 2159-2180页

作者： Raja Jarray Mujahed Al-Dhaifallah Hegazy Rezk Soufiene Bouallègue Research Laboratory in Automatic Control(LARA) National Engineering School of Tunis(ENIT)University of Tunis El ManarTunis1002Tunisia Department of Systems Engineering King Fahd University of Petroleum&MineralsDhahran31261Saudi Arabia College of Engineering at Wadi Addawaser Prince Sattam Bin Abdulaziz UniversityAl-Kharj11911Saudi Arabia Department of Electrical Engineering Faculty of EngineeringMinia UniversityMinia61517Egypt High Institute of Industrial Systems of Gabès(ISSIG) University of GabèsGabès6011Tunisia

Paths planning of Unmanned Aerial Vehicles(UAVs)in a dynamic environment is considered a challenging task in autonomous flight control *** this work,an efficient method based on a Multi-Objective MultiVerse Optimization(MOMVO)algorithm is proposed and successfully applied to solve the path planning problem of quadrotors with moving *** a path planning task is formulated as a multicriteria optimization problem under operational *** proposed MOMVO-based planning approach aims to lead the drone to traverse the shortest path from the starting point and the target without collision with moving *** vehicle moves to the next position from its current one such that the line joining minimizes the total path length and allows aligning its direction towards the *** choose the best compromise solution among all the non-dominated Pareto ones obtained for compromise objectives,the modified Technique for Order Preference by Similarity to Ideal Solution(TOPSIS)is investigated.A set of homologous metaheuristics such as Multiobjective Salp Swarm Algorithm(MSSA),Multi-Objective Grey Wolf Optimizer(MOGWO),Multi-Objective Particle Swarm Optimization(MOPSO),and Non-Dominated Genetic Algorithm II(NSGAII)is used as a basis for the performance *** results and statistical analyses show the superiority and effectiveness of the proposed MOMVO-based planning *** obtained results are satisfactory and encouraging for future practical implementation of the path planning strategy.

关键词： Quadrotors path planning dynamic obstacles multi-objective optimization global metaheuristics TOPSIS decision-making Friedman statistical tests

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Sneak Circuit Analysis Covering Interface Circuits in Spacecraft Integrated Chips while Considering Faults

Sneak Circuit Analysis Covering Interface Circuits in Spacec...

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System Reliability and Safety engineering (SRSE), International Conference on

作者： Weiming Liu Cen Chen Kaiwen Xiao Mingtao Feng Haonan Zhang Guofu Zhai Department of Electrical Engineering & Automation Harbin Institute of Technology Harbin China Beijing Aerospace Automatic Control Institute Beijing China Harbin Institute of Technology Harbin China

ISBN: (数字)9798350356083

ISBN: (纸本)9798350356090

Sneak circuit analysis is a crucial reliability design process. In the design of spacecraft electronic systems, it is challenging to identify sneak circuits related to integrated chip interface circuits, and there are few targeted researches. In extreme working conditions, the coupling sneak circuits caused by the failure of integrated chips are difficult to identify, affecting the design optimization. The paper proposes a typical interface information specification model for integrated chips to simulate the electrical information characteristics of interfaces. And the typical failure models of intrinsic components inside the chip are determined through multi-physics field analysis. Then, based on the digital model, a sneak circuit diagnosis process based on excitation and fault injection are proposed, and a sneak circuit screening method based on Python virtual prototyping is implemented in the case study. Finally, the design constraint clues for the chip’s external circuit are obtained, which provides a reference for related fault diagnosis and reliability optimization works.

关键词： Space vehicles Semiconductor device modeling Reliability engineering Topology Circuit faults Virtual prototyping Integrated circuit modeling Integrated circuit reliability Circuit analysis Stress

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Magnetoelastic coupling driven magnon gap in a honeycomb antiferromagnet

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Physical Review B 2024年第10期110卷 104419页

作者： Jeongheon Choe Andrey Baydin Gaihua Ye Bowen Ma Cynthia Nnokwe Martin Rodriguez-Vega David Lujan Swati Chaudhary Fuyang Tay Jiaming He Hiroyuki Nojiri Jianshi Zhou Gregory A. Fiete Junichiro Kono Rui He Xiaoqin Li Department of Physics Center of Complex Quantum Systems Center for Dynamics and Control of Materials Department of Electrical and Computer Engineering Smalley–Curl Institute Department of Physics Applied Physics Graduate Program Smalley–Curl Institute Department of Mechanical Engineering Institute for Materials Research Department of Physics and Astronomy Department of Materials Science and NanoEngineering

Cobalt titanate, CoTiO3, is a honeycomb antiferromagnet recently confirmed experimentally to host Dirac magnons, topological spin-orbit excitons, and chiral phonons. Here, we investigate a magnon gap at the zone center which calls for a refined spin Hamiltonian. We propose a microscopic model for the magnon gap and attribute it to a lattice-distortion (phonon)-induced higher-order spin interaction. Strong magnetoelastic coupling in CoTiO3 is also evident in Raman spectra, in which the magnetic order exerts a stronger influence on phonons corresponding to in-plane ionic motions than those with out-of-plane motions. We further examine the evolution of the zone-center magnons in a high magnetic field up to 18.5 T via THz absorption spectroscopy measurements. Based on this field dependence, we propose a spin Hamiltonian that not only agrees with magnon dispersion measured by inelastic neutron scattering but also includes fewer exchange constants and a realistic anisotropy term. Our work highlights the broad implications of magnetoelastic coupling in the study of topologically protected bosonic excitations.

关键词： Magnetism Magnons Topological materials Raman spectroscopy Terahertz spectroscopy

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A Data-Driven Approach to System Invertibility and Input Reconstruction

A Data-Driven Approach to System Invertibility and Input Rec...

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IEEE Conference on Decision and control

作者： Vikas Kumar Mishra Andrea Iannelli Naim Bajcinca Department of Mechanical and Process Engineering RPTU Kaiserslautern Kaiserslautern Germany University of Stuttgart Institute for Systems Theory and Automatic Control Stuttgart Germany

We consider the problems of system invertibility and input reconstruction for linear time-invariant (LTI) systems using only measured data. The two problems are connected in the sense that input reconstruction is possible provided that the system is left invertible. To verify the latter property without model knowledge, we leverage behavioral systems theory and develop two data-driven algorithms: one based on input/state/output data and the other based only on input/output data. We then consider the problem of input reconstruction for both noise-free and noisy data settings. In the case of noisy data, a statistical approach is leveraged to formulate the problem as a maximum likelihood estimation (MLE) problem. The proposed approaches are finally illustrated with numerical examples that show: exact input reconstruction in the noise-free setting; and the better performance of the MLE-based approach compared to the standard least-norm solution.

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Low-Complexity Hardware Architecture of an H.264-Based Video Encoder for FPGAs

Low-Complexity Hardware Architecture of an H.264-Based Video...

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Computer Science, Computer engineering, & Applied Computing (CSCE), Congress in

作者： Azam Tayyebi Darrin Hanna Bryant Jones Department of Electrical and Computer Engineering Oakland University Rochester USA Intrepid Control Systems Troy USA

In this paper, we present an architecture for a scalable, efficient, realtime intra H.264 video encoder implemented on an FPGA. Our architecture was designed to achieve a through-put of up to 2.3 Gbit/s using a parallel and pipelined architecture described in VHDL. All modules in the architecture are optimized to utilize minimum hardware area. A parameterized encoding system and flexible architecture is proposed to provide the ability to achieve different compression ratios ranging from 1.4 to 2 with varying size and power requirements. As a baseline, with no compression, the encoder required hardware resources equivalent to 18K logic gates. This work experimented with compression ratios up to 2 which required an equivalent of 31K logic gates. The encoder performs at frequency ranges of 159–183 MHz.

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