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Autonomous Underground Freight Transport Systems -- The Future of Urban Logistics? arXiv.cs.SY Pub Date : 2024-03-13 Lasse Bienzeisler, Torben Lelke, Bernhard Friedrich
We design a concept for an autonomous underground freight transport system for Hanover, Germany. To evaluate the resulting system changes in overall traffic flows from an environmental perspective, we carried out an agent-based traffic simulation with MATSim. Our simulations indicate comparatively low impacts on network-wide traffic volumes. Local CO2 emissions, on the other hand, could be reduced
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Edge Information Hub: Orchestrating Satellites, UAVs, MEC, Sensing and Communications for 6G Closed-Loop Controls arXiv.cs.SY Pub Date : 2024-03-11 Chengleyang Lei, Wei Feng, Peng Wei, Yunfei Chen, Ning Ge, Shiwen Mao
An increasing number of field robots would be used for mission-critical tasks in remote or post-disaster areas. Due to usually-limited individual abilities, these robots require an edge information hub (EIH), which is capable of not only communications but also sensing and computing. Such EIH could be deployed on a flexibly-dispatched unmanned aerial vehicle (UAV). Different from traditional aerial
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Stochastic Approximation with Delayed Updates: Finite-Time Rates under Markovian Sampling arXiv.cs.SY Pub Date : 2024-02-19 Arman Adibi, Nicolo Dal Fabbro, Luca Schenato, Sanjeev Kulkarni, H. Vincent Poor, George J. Pappas, Hamed Hassani, Aritra Mitra
Motivated by applications in large-scale and multi-agent reinforcement learning, we study the non-asymptotic performance of stochastic approximation (SA) schemes with delayed updates under Markovian sampling. While the effect of delays has been extensively studied for optimization, the manner in which they interact with the underlying Markov process to shape the finite-time performance of SA remains
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Minimal Constraint Violation Probability in Model Predictive Control for Linear Systems arXiv.cs.SY Pub Date : 2024-02-16 Michael Fink, Tim Brüdigam, Dirk Wollherr, Marion Leibold
Handling uncertainty in model predictive control comes with various challenges, especially when considering state constraints under uncertainty. Most methods focus on either the conservative approach of robustly accounting for uncertainty or allowing a small probability of constraint violation. In this work, we propose a linear model predictive control approach that minimizes the probability that linear
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Denoising Diffusion-Based Control of Nonlinear Systems arXiv.cs.SY Pub Date : 2024-02-03 Karthik Elamvazhuthi, Darshan Gadginmath, Fabio Pasqualetti
We propose a novel approach based on Denoising Diffusion Probabilistic Models (DDPMs) to control nonlinear dynamical systems. DDPMs are the state-of-art of generative models that have achieved success in a wide variety of sampling tasks. In our framework, we pose the feedback control problem as a generative task of drawing samples from a target set under control system constraints. The forward process
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Practical Framework for Problem-Based Learning in an Introductory Circuit Analysis Course arXiv.cs.SY Pub Date : 2024-01-29 Sebastian Martin, Salvador Pineda, Juan Perez-Ruiz, Natalia Alguacil, Antonio Ruiz-Gonzalez
Introductory courses on electric circuits at undergraduate level are usually presented in quite abstract terms, with questions and problems quite far from practical problems. This causes the students have difficulties to apply that theory to solve practical technical problems. On the other hand, electric circuits are everywhere in our lives, so we have plenty of real practical problems. Here we compile
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Digital requirements engineering with an INCOSE-derived SysML meta-model arXiv.cs.SY Pub Date : 2024-01-29 James S. Wheaton, Daniel R. Herber
Traditional requirements engineering tools do not readily access the system architecture model defined in SysML and related Profiles, often resulting in duplication of basic system model elements that nevertheless lack the connectivity and expressive detail possible in a SysML-defined model. Without architecture model connectivity, requirements can suffer from imprecision and inconsistent terminology
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Optimal Control of Renewable Energy Communities subject to Network Peak Fees with Model Predictive Control and Reinforcement Learning Algorithms arXiv.cs.SY Pub Date : 2024-01-29 Samy Aittahar, Adrien Bolland, Guillaume Derval, Damien Ernst
We propose in this paper an optimal control framework for renewable energy communities (RECs) equipped with controllable assets. Such RECs allow its members to exchange production surplus through an internal market. The objective is to control their assets in order to minimise the sum of individual electricity bills. These bills account for the electricity exchanged through the REC and with the retailers
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Scalable Reinforcement Learning for Linear-Quadratic Control of Networks arXiv.cs.SY Pub Date : 2024-01-29 Johan Olsson, Runyu Zhang, Emma Tegling, Na Li
Distributed optimal control is known to be challenging and can become intractable even for linear-quadratic regulator problems. In this work, we study a special class of such problems where distributed state feedback controllers can give near-optimal performance. More specifically, we consider networked linear-quadratic controllers with decoupled costs and spatially exponentially decaying dynamics
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Model predictive control of wakes for wind farm power tracking arXiv.cs.SY Pub Date : 2024-01-29 Arnold Sterle, Christian A. Hans, Jörg Raisch
In this paper, a model predictive control scheme for wind farms is presented. Our approach considers wake dynamics including their influence on local wind conditions and allows to track a given power reference. In detail, a Gaussian wake model is used in combination with observation points that carry wind condition information. This allows to estimate the rotor effective wind speeds at downstream turbines
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Leadership Dynamics in Social Multiplex Networks with Mono and Bi-directional Interactions arXiv.cs.SY Pub Date : 2024-01-29 Amirreza Talebi
We explored the dynamics of opinions within a multiplex network, where agents engage in one-way or two-way communication, and the network may have a designated leader. Additionally, we demonstrated that, under specific conditions, opinions tend to converge despite non-positive diagonal elements in transition probability matrices or decomposable layers. Lastly, we contrasted the convergence rates of
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Attentive Convolutional Deep Reinforcement Learning for Optimizing Solar-Storage Systems in Real-Time Electricity Markets arXiv.cs.SY Pub Date : 2024-01-29 Jinhao Li, Changlong Wang, Hao Wang
This paper studies the synergy of solar-battery energy storage system (BESS) and develops a viable strategy for the BESS to unlock its economic potential by serving as a backup to reduce solar curtailments while also participating in the electricity market. We model the real-time bidding of the solar-battery system as two Markov decision processes for the solar farm and the BESS, respectively. We develop
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Deep Reinforcement Learning for Voltage Control and Renewable Accommodation Using Spatial-Temporal Graph Information arXiv.cs.SY Pub Date : 2024-01-29 Jinhao Li, Ruichang Zhang, Hao Wang, Zhi Liu, Hongyang Lai, Yanru Zhang
Renewable energy resources (RERs) have been increasingly integrated into distribution networks (DNs) for decarbonization. However, the variable nature of RERs introduces uncertainties to DNs, frequently resulting in voltage fluctuations that threaten system security and hamper the further adoption of RERs. To incentivize more RER penetration, we propose a deep reinforcement learning (DRL)-based strategy
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Cross-Layer Performance Evaluation of C-V2X arXiv.cs.SY Pub Date : 2024-01-29 Dhruba Sunuwar, Seungmo Kim
As self-driving cars increasingly penetrate our daily lives, vehicle-to-everything (V2X) communications are emerging as one of the key enabler technologies. However, the dynamicity of vehicles (one of whose causes is the mobility of vehicles) often complicates it even further to evaluate the performance of a V2X system. We have been building a system-level simulator dedicated to assessing the performance
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Innovation-triggered Learning for Data-driven Predictive Control: Deterministic and Stochastic Formulations arXiv.cs.SY Pub Date : 2024-01-29 Kaikai Zheng, Dawei Shi, Sandra Hirche, Yang Shi
Data-driven control has attracted lots of attention in recent years, especially for plants that are difficult to model based on first-principle. In particular, a key issue in data-driven approaches is how to make efficient use of data as the abundance of data becomes overwhelming. {To address this issue, this work proposes an innovation-triggered learning framework and a corresponding data-driven controller
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Efficient Data-Driven MPC for Demand Response of Commercial Buildings arXiv.cs.SY Pub Date : 2024-01-28 Marie-Christine Paré, Vasken Dermardiros, Antoine Lesage-Landry
Model predictive control (MPC) has been shown to significantly improve the energy efficiency of buildings while maintaining thermal comfort. Data-driven approaches based on neural networks have been proposed to facilitate system modelling. However, such approaches are generally nonconvex and result in computationally intractable optimization problems. In this work, we design a readily implementable
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Power based adaptive compensator of output oscillations arXiv.cs.SY Pub Date : 2024-01-28 Michael Ruderman
Power-based output feedback compensator for oscillatory systems is proposed. The average input-output power of an oscillatory signal serves as an equivalent control effort, while the unknown oscillation's amplitude and frequency are detected at each half-period. This makes the compensator adaptive and discrete, while the measured oscillatory output is the single available signal in use. The proposed
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Survey of Distributed Algorithms for Resource Allocation over Multi-Agent Systems arXiv.cs.SY Pub Date : 2024-01-28 Mohammadreza Doostmohammadian, Alireza Aghasi, Mohammad Pirani, Ehsan Nekouei, Houman Zarrabi, Reza Keypour, Apostolos I. Rikos, Karl H. Johansson
Resource allocation and scheduling in multi-agent systems present challenges due to complex interactions and decentralization. This survey paper provides a comprehensive analysis of distributed algorithms for addressing the distributed resource allocation (DRA) problem over multi-agent systems. It covers a significant area of research at the intersection of optimization, multi-agent systems, and distributed
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Design of UAV flight state recognition and trajectory prediction system based on trajectory feature construction arXiv.cs.SY Pub Date : 2024-01-28 Xingyu Zhou, Zhuoyong Shi
With the impact of artificial intelligence on the traditional UAV industry, autonomous UAV flight has become a current hot research field. Based on the demand for research on critical technologies for autonomous flying UAVs, this paper addresses the field of flight state recognition and trajectory prediction of UAVs. This paper proposes a method to improve the accuracy of UAV trajectory prediction
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A Parameter Privacy-Preserving Strategy for Mixed-Autonomy Platoon Control arXiv.cs.SY Pub Date : 2024-01-28 Jingyuan Zhou, Kaidi Yang
It has been demonstrated that leading cruise control (LCC) can improve the operation of mixed-autonomy platoons by allowing connected and automated vehicles (CAVs) to make longitudinal control decisions based on the information provided by surrounding vehicles. However, LCC generally requires surrounding human-driven vehicles (HDVs) to share their real-time states, which can be used by adversaries
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Multi-Interval Energy-Reserve Co-Optimization with SoC-Dependent Bids from Battery Storage arXiv.cs.SY Pub Date : 2024-01-27 Cong Chen, Siying Li, Lang Tong
We consider the problem of co-optimized energy-reserve market clearing with state-of-charge (SoC) dependent bids from battery storage participants. While SoC-dependent bidding accurately captures storage's degradation and opportunity costs, such bids result in a non-convex optimization in the market clearing process. More challenging is the regulation reserve capacity clearing, where the SoC-dependent
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Distributed Resilient Interval Observer Synthesis for Nonlinear Discrete-Time Systems arXiv.cs.SY Pub Date : 2024-01-27 Mohammad Khajenejad, Scott Brown, Sonia Martinez
This paper introduces a novel recursive distributed estimation algorithm aimed at synthesizing input and state interval observers for nonlinear bounded-error discrete-time multi-agent systems. The considered systems have sensors and actuators that are susceptible to unknown or adversarial inputs. To solve this problem, we first identify conditions that allow agents to obtain nonlinear bounded-error
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Pulse-Width Modulation Technique With Harmonic Injection in the Modulating Wave and Discontinuous Frequency Modulation for the Carrier Wave for Multilevel Inverters: An Application to the Reduction of Acoustic Noise in Induction Motors arXiv.cs.SY Pub Date : 2024-01-27 Antonio Ruiz-Gonzalez, Juan-Ramon Heredia-Larrubia, Mario J. Meco-Gutierrez, Francisco-M. Perez-Hidalgo
An implementation of a harmonic injection pulse width modulation frequency-modulated triangular carrier (HIPWM-FMTC) control strategy applied to a multilevel power inverter feeding an asynchronous motor is presented. The aim was to justify the reduction in acoustic noise emitted by the machine compared with other strategies in the technical literature. In addition, we checked how the THD at the inverter
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Epidemic Population Games And Perturbed Best Response Dynamics arXiv.cs.SY Pub Date : 2024-01-27 Shinkyu Park, Jair Certorio, Nuno C. Martins, Richard J. La
This paper proposes an approach to mitigate epidemic spread in a population of strategic agents by encouraging safer behaviors through carefully designed rewards. These rewards, which vary according to the state of the epidemic, are ascribed by a dynamic payoff mechanism we seek to design. We use a modified SIRS model to track how the epidemic progresses in response to the population's agents strategic
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An overview of IoT architectures, technologies, and existing open-source projects arXiv.cs.SY Pub Date : 2024-01-27 Tomás Domínguez-Bolaño, Omar Campos, Valentín Barral, Carlos J. Escudero, José A. García-Naya
Today's needs for monitoring and control of different devices in organizations require an Internet of Things (IoT) platform that can integrate heterogeneous elements provided by multiple vendors and using different protocols, data formats and communication technologies. This article provides a comprehensive review of all the architectures, technologies, protocols and data formats most commonly used
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Localization of Dummy Data Injection Attacks in Power Systems Considering Incomplete Topological Information: A Spatio-Temporal Graph Wavelet Convolutional Neural Network Approach arXiv.cs.SY Pub Date : 2024-01-27 Zhaoyang Qu, Yunchang Dong, Yang Li, Siqi Song, Tao Jiang, Min Li, Qiming Wang, Lei Wang, Xiaoyong Bo, Jiye Zang, Qi Xu
The emergence of novel the dummy data injection attack (DDIA) poses a severe threat to the secure and stable operation of power systems. These attacks are particularly perilous due to the minimal Euclidean spatial separation between the injected malicious data and legitimate data, rendering their precise detection challenging using conventional distance-based methods. Furthermore, existing research
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Online Data-Driven Adaptive Control for Unknown Linear Time-Varying Systems arXiv.cs.SY Pub Date : 2024-01-27 Shenyu Liu, Kaiwen Chen, Jaap Eising
This paper proposes a novel online data-driven adaptive control for unknown linear time-varying systems. Initialized with an empirical feedback gain, the algorithm periodically updates this gain based on the data collected over a short time window before each update. Meanwhile, the stability of the closed-loop system is analyzed in detail, which shows that under some mild assumptions, the proposed
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Backscatter Measurements and Models for RF Sensing Applications in Cluttered Environments arXiv.cs.SY Pub Date : 2024-01-26 Dmitry Chizhik, Jinfeng Du, Jakub Sapis, Reinaldo A. Valenzuela, Abhishek Adhikari, Gil Zussman, Manuel A. Almendra, Mauricio Rodriguez, Rodolfo Feick
A statistical backscatter channel model for indoor clutter is developed for indoor RF sensing applications based on measurements. A narrowband 28 GHz sounder used a quazi-monostatic radar arrangement with an omnidirectional transmit antenna illuminating an indoor scene and a spinning horn receive antenna less than 1 m away collecting backscattered power as a function of azimuth. Median average backscatter
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Overview of Sensing Attacks on Autonomous Vehicle Technologies and Impact on Traffic Flow arXiv.cs.SY Pub Date : 2024-01-26 Zihao Li, Sixu Li, Hao Zhang, Yang Zhou, Siyang Xie, Yunlong Zhang
While perception systems in Connected and Autonomous Vehicles (CAVs), which encompass both communication technologies and advanced sensors, promise to significantly reduce human driving errors, they also expose CAVs to various cyberattacks. These include both communication and sensing attacks, which potentially jeopardize not only individual vehicles but also overall traffic safety and efficiency.
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Instantaneous Power Theory Revisited with Classical Mechanics arXiv.cs.SY Pub Date : 2024-01-26 Federico Milano, Georgios Tzounas, Ioannis Dassios
The paper revisits the concepts of instantaneous active and reactive powers and provides a novel definition for basic circuit elements based on quantities utilized in classical mechanics, such as absolute and relative velocity, momentum density, angular momentum and apparent forces. The discussion leverages from recent publications by the authors that interpret the voltage and current as velocities
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Symbolic-numeric algorithm for parameter estimation in discrete-time models with $\exp$ arXiv.cs.SY Pub Date : 2024-01-29 Yosef Berman, Joshua Forrest, Matthew Grote, Alexey Ovchinnikov, Sonia Rueda
Determining unknown parameter values in dynamic models is crucial for accurate analysis of the dynamics across the different scientific disciplines. Discrete-time dynamic models are widely used to model biological processes, but it is often difficult to determine these parameters. In this paper, we propose a robust symbolic-numeric approach for parameter estimation in discrete-time models that involve
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A Novel Geometric Solution for Moving Target Localization through Multistatic Sensing in the ISAC System arXiv.cs.SY Pub Date : 2024-01-29 S. Zhuge, Y. Ma, Z. Lin, Y. Zeng
This paper proposes a novel geometric solution for tracking a moving target through multistatic sensing. In contrast to existing two-step weighted least square (2SWLS) methods which use the bistatic range (BR) and bistatic range rate (BRR) measurements, the proposed method incorporates an additional direction of arrival (DOA) measurement of the target obtained from a communication receiver in an integrated
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Motion-induced error reduction for high-speed dynamic digital fringe projection system arXiv.cs.SY Pub Date : 2024-01-29 Sanghoon Jeon, Hyo-Geon Lee, Jae-Sung Lee, Bo-Min Kang, Byung-Wook Jeon, Jun Young Yoon, Jae-Sang Hyun
In phase-shifting profilometry (PSP), any motion during the acquisition of fringe patterns can introduce errors because it assumes both the object and measurement system are stationary. Therefore, we propose a method to pixel-wise reduce the errors when the measurement system is in motion due to a motorized linear stage. The proposed method introduces motion-induced error reduction algorithm, which
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Decentralized Robust Data-driven Predictive Control for Smoothing Mixed Traffic Flow arXiv.cs.SY Pub Date : 2024-01-29 Xu Shang, Jiawei Wang, Yang Zheng
In a mixed traffic with connected automated vehicles (CAVs) and human-driven vehicles (HDVs) coexisting, data-driven predictive control of CAVs promises system-wide traffic performance improvements. Yet, most existing approaches focus on a centralized setup, which is not computationally scalable while failing to protect data privacy. The robustness against unknown disturbances has not been well addressed
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GarchingSim: An Autonomous Driving Simulator with Photorealistic Scenes and Minimalist Workflow arXiv.cs.SY Pub Date : 2024-01-28 Liguo Zhou, Yinglei Song, Yichao Gao, Zhou Yu, Michael Sodamin, Hongshen Liu, Liang Ma, Lian Liu, Hao Liu, Yang Liu, Haichuan Li, Guang Chen, Alois Knoll
Conducting real road testing for autonomous driving algorithms can be expensive and sometimes impractical, particularly for small startups and research institutes. Thus, simulation becomes an important method for evaluating these algorithms. However, the availability of free and open-source simulators is limited, and the installation and configuration process can be daunting for beginners and interdisciplinary
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Sample Complexity of the Sign-Perturbed Sums Identification Method: Scalar Case arXiv.cs.SY Pub Date : 2024-01-28 Szabolcs Szentpéteri, Balázs Csanád Csáji
Sign-Perturbed Sum (SPS) is a powerful finite-sample system identification algorithm which can construct confidence regions for the true data generating system with exact coverage probabilities, for any finite sample size. SPS was developed in a series of papers and it has a wide range of applications, from general linear systems, even in a closed-loop setup, to nonlinear and nonparametric approaches
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The computation of approximate feedback Stackelberg equilibria in multi-player nonlinear constrained dynamic games arXiv.cs.SY Pub Date : 2024-01-28 Jingqi Li, Somayeh Sojoudi, Claire Tomlin, David Fridovich-Keil
Solving feedback Stackelberg games with nonlinear dynamics and coupled constraints, a common scenario in practice, presents significant challenges. This work introduces an efficient method for computing local feedback Stackelberg policies in multi-player general-sum dynamic games, with continuous state and action spaces. Different from existing (approximate) dynamic programming solutions that are primarily
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Rates of Convergence in the Central Limit Theorem for Markov Chains, with an Application to TD Learning arXiv.cs.SY Pub Date : 2024-01-28 R. Srikant
We prove a non-asymptotic central limit theorem for vector-valued martingale differences using Stein's method, and use Poisson's equation to extend the result to functions of Markov Chains. We then show that these results can be applied to establish a non-asymptotic central limit theorem for Temporal Difference (TD) learning with averaging.
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Data-Driven Strategies for Coping with Incomplete DVL Measurements arXiv.cs.SY Pub Date : 2024-01-28 Nadav Cohen, Itzik Klein
Autonomous underwater vehicles are specialized platforms engineered for deep underwater operations. Critical to their functionality is autonomous navigation, typically relying on an inertial navigation system and a Doppler velocity log. In real-world scenarios, incomplete Doppler velocity log measurements occur, resulting in positioning errors and mission aborts. To cope with such situations, a model
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A Mechatronic System for the Visualisation and Analysis of Orchestral Conducting arXiv.cs.SY Pub Date : 2024-01-28 Courtney Coates, Liao Wu
This paper quantitatively analysed orchestral conducting patterns, and detected variations as a result of extraneous body movement during conducting, in the first experiment of its kind. A novel live conducting system featuring data capture, processing, and analysis was developed. Reliable data of an expert conductor's movements was collected, processed, and used to calculate average trajectories for
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Accelerated Distributed Allocation arXiv.cs.SY Pub Date : 2024-01-28 Mohammadreza Doostmohammadian, Alireza Aghasi
Distributed allocation finds applications in many scenarios including CPU scheduling, distributed energy resource management, and networked coverage control. In this paper, we propose a fast convergent optimization algorithm with a tunable rate using the signum function. The convergence rate of the proposed algorithm can be managed by changing two parameters. We prove convergence over uniformly-connected
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Proto-MPC: An Encoder-Prototype-Decoder Approach for Quadrotor Control in Challenging Winds arXiv.cs.SY Pub Date : 2024-01-27 Yuliang Gu, Sheng Cheng, Naira Hovakimyan
Quadrotors are increasingly used in the evolving field of aerial robotics for their agility and mechanical simplicity. However, inherent uncertainties, such as aerodynamic effects coupled with quadrotors' operation in dynamically changing environments, pose significant challenges for traditional, nominal model-based control designs. We propose a multi-task meta-learning method called Encoder-Prototype-Decoder
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Fault Diagnosis on Induction Motor using Machine Learning and Signal Processing arXiv.cs.SY Pub Date : 2024-01-27 Muhammad Samiullah, Hasan Ali, Shehryar Zahoor, Anas Ali
The detection and identification of induction motor faults using machine learning and signal processing is a valuable approach to avoiding plant disturbances and shutdowns in the context of Industry 4.0. In this work, we present a study on the detection and identification of induction motor faults using machine learning and signal processing with MATLAB Simulink. We developed a model of a three-phase
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Multi-Robot Relative Pose Estimation in SE(2) with Observability Analysis: A Comparison of Extended Kalman Filtering and Robust Pose Graph Optimization arXiv.cs.SY Pub Date : 2024-01-27 Kihoon Shin, Hyunjae Sim, Seungwon Nam, Yonghee Kim, Jae Hu, Kwang-Ki K. Kim
In this paper, we consider multi-robot localization problems with focus on cooperative localization and observability analysis of relative pose estimation. For cooperative localization, there is extra information available to each robot via communication network and message passing. If odometry data of a target robot can be transmitted to the ego-robot then the observability of their relative pose
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Finite-Time Analysis of On-Policy Heterogeneous Federated Reinforcement Learning arXiv.cs.SY Pub Date : 2024-01-27 Chenyu Zhang, Han Wang, Aritra Mitra, James Anderson
Federated reinforcement learning (FRL) has emerged as a promising paradigm for reducing the sample complexity of reinforcement learning tasks by exploiting information from different agents. However, when each agent interacts with a potentially different environment, little to nothing is known theoretically about the non-asymptotic performance of FRL algorithms. The lack of such results can be attributed
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Harnessing Deep Learning of Point Clouds for Inverse Control of 3D Shape Morphing arXiv.cs.SY Pub Date : 2024-01-26 Jue Wang, Dhirodaatto Sarkar, Jiaqi Suo, Alex Chortos
Shape-morphing devices, a crucial branch in soft robotics, hold significant application value in areas like human-machine interfaces, biomimetic robotics, and tools for interacting with biological systems. To achieve three-dimensional (3D) programmable shape morphing (PSM), the deployment of array-based actuators is essential. However, a critical knowledge gap impeding the development of 3D PSM is
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Quantum-Assisted Adaptive Beamforming in UASs Network: Enhancing Airborne Communication via Collaborative UASs for NextG IoT arXiv.cs.SY Pub Date : 2024-01-26 Sudhanshu Arya, Ying Wang
This paper introduces a novel quantum-based method for dynamic beamforming and re-forming in Unmanned Aircraft Systems (UASs), specifically addressing the critical challenges posed by the unavoidable hovering characteristics of UAVs. Hovering creates significant beam path distortions, impacting the reliability and quality of distributed beamforming in airborne networks. To overcome these challenges
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Towards a Theory of Control Architecture: A quantitative framework for layered multi-rate control arXiv.cs.SY Pub Date : 2024-01-26 Nikolai Matni, Aaron D. Ames, John C. Doyle
This paper focuses on the need for a rigorous theory of layered control architectures (LCAs) for complex engineered and natural systems, such as power systems, communication networks, autonomous robotics, bacteria, and human sensorimotor control. All deliver extraordinary capabilities, but they lack a coherent theory of analysis and design, partly due to the diverse domains across which LCAs can be
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Multi-agent Deep Reinforcement Learning for Dynamic Pricing by Fast-charging Electric Vehicle Hubs in ccompetition arXiv.cs.SY Pub Date : 2024-01-25 Diwas Paudel, Tapas K. Das
Fast-charging hubs for electric vehicles will soon become part of the newly built infrastructure for transportation electrification across the world. These hubs are expected to host many DC fast-charging stations and will admit EVs only for charging. Like the gasoline refueling stations, fast-charging hubs in a neighborhood will dynamically vary their prices to compete for the same pool of EV owners
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Maintenance cost assessment for heterogeneous multi-component systems incorporating perfect inspections and waiting time to maintenance arXiv.cs.SY Pub Date : 2024-01-21 Lucía Bautista, Inma T. Castro, Luis Landesa
Most existing research about complex systems maintenance assumes they consist of the same type of components. However, systems can be assembled with heterogeneous components (for example degrading and non-degrading components) that require different maintenance actions. Since industrial systems become more and more complex, more research about the maintenance of systems with heterogeneous components
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Jet space extensions of infinite-dimensional Hamiltonian systems arXiv.cs.SY Pub Date : 2024-01-24 Till Preuster, Manuel Schaller, Bernhard Maschke
We analyze infinite-dimensional Hamiltonian systems corresponding to partial differential equations on one-dimensional spatial domains formulated with formally skew-adjoint Hamiltonian operators and nonlinear Hamiltonian density. In various applications, the Hamiltonian density can depend on spatial derivatives of the state such that these systems can not straightforwardly be formulated as boundary
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Design & Implementation of Automatic Machine Condition Monitoring and Maintenance System in Limited Resource Situations arXiv.cs.SY Pub Date : 2024-01-22 Abu Hanif Md. Ripon, Muhammad Ahsan Ullah, Arindam Kumar Paul, Md. Mortaza Morshed
In the era of the fourth industrial revolution, it is essential to automate fault detection and diagnosis of machineries so that a warning system can be developed that will help to take an appropriate action before any catastrophic damage. Some machines health monitoring systems are used globally but they are expensive and need trained personnel to operate and analyse. Predictive maintenance and occupational
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A Dynamic Capacitance Matching (DCM)-based Current Response Algorithm for Signal Line RC Network arXiv.cs.SY Pub Date : 2024-01-16 Zhoujie Wu, Cai Luo, Zhong Guan
This paper proposes a dynamic capacitance matching (DCM)-based RC current response algorithm for calculating the current waveform of a signal line without performing SPICE simulation. Specifically, unlike previous method such as CCS model, driver linear representation, waveform functional fitting or equivalent load capacitance, our algorithm does not rely on fixed reduced model of both standard cell
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CycLight: learning traffic signal cooperation with a cycle-level strategy arXiv.cs.SY Pub Date : 2024-01-16 Gengyue Han, Xiaohan Liu, Xianyue Peng, Hao Wang, Yu Han
This study introduces CycLight, a novel cycle-level deep reinforcement learning (RL) approach for network-level adaptive traffic signal control (NATSC) systems. Unlike most traditional RL-based traffic controllers that focus on step-by-step decision making, CycLight adopts a cycle-level strategy, optimizing cycle length and splits simultaneously using Parameterized Deep Q-Networks (PDQN) algorithm
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Identification of Additive Continuous-time Systems in Open and Closed-loop arXiv.cs.SY Pub Date : 2024-01-02 Rodrigo A. González, Koen Classens, Cristian R. Rojas, James S. Welsh, Tom Oomen
When identifying electrical, mechanical, or biological systems, parametric continuous-time identification methods can lead to interpretable and parsimonious models when the model structure aligns with the physical properties of the system. Traditional linear system identification may not consider the most parsimonious model when relying solely on unfactored transfer functions, which typically result
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Identification of Secondary Resonances of Nonlinear Systems using Phase-Locked Loop Testing arXiv.cs.SY Pub Date : 2024-01-02 Tong Zhou, Gaetan Kerschen
One unique feature of nonlinear dynamical systems is the existence of superharmonic and subharmonic resonances in addition to primary resonances. In this study, an effective vibration testing methodology is introduced for the experimental identification of these secondary resonances. The proposed method relies on phase-locked loop control combined with adaptive filters for online Fourier decomposition
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A Stochastic-MILP dispatch optimization model for Concentrated Solar Thermal under uncertainty arXiv.cs.SY Pub Date : 2024-01-02 Navid Mohammadzadeh, Huy Truong-Ba, Michael E. Cholette, Theodore A. Steinberg, Giampaolo Manzolini
Concentrated Solar Thermal (CST) offers a promising solution for large-scale solar energy utilization as Thermal Energy Storage (TES) enables electricity generation independently of daily solar fluctuations, shifting to high-priced electricity intervals. The development of dispatch planning tools is mandatory to account for uncertainties associated with solar irradiation and electricity price forecasts
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Quadratic Time-Frequency Analysis of Vibration Signals for Diagnosing Bearing Faults arXiv.cs.SY Pub Date : 2024-01-02 Mohammad Al-Sa'd, Tuomas Jalonen, Serkan Kiranyaz, Moncef Gabbouj
Diagnosis of bearing faults is paramount to reducing maintenance costs and operational breakdowns. Bearing faults are primary contributors to machine vibrations, and analyzing their signal morphology offers insights into their health status. Unfortunately, existing approaches are optimized for controlled environments, neglecting realistic conditions such as time-varying rotational speeds and the vibration's
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A Minimal Control Family of Dynamical Syetem for Universal Approximation arXiv.cs.SY Pub Date : 2023-12-20 Yifei Duan, Yongqiang Cai
The universal approximation property (UAP) of neural networks is a fundamental characteristic of deep learning. It is widely recognized that a composition of linear functions and non-linear functions, such as the rectified linear unit (ReLU) activation function, can approximate continuous functions on compact domains. In this paper, we extend this efficacy to the scenario of dynamical systems with
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Safety verification of Neural-Network-based controllers: a set invariance approach arXiv.cs.SY Pub Date : 2023-12-18 Louis Jouret, Adnane Saoud, Sorin Olaru
This paper presents a novel approach to ensure the safety of continuous-time linear dynamical systems controlled by a neural network (NN) based state-feedback. Our method capitalizes on the use of continuous piece-wise affine (PWA) activation functions (e.g. ReLU) which render the NN a PWA continuous function. By computing the affine regions of the latter and applying Nagumo's theorem, a subset of