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  • Nonlinearity Compensation Based on Identified NARX Polynomials Models
    arXiv.cs.SY Pub Date : 2020-11-24
    Lucas A. Tavares; Petrus E. O. G. B. Abreu; Luis A. Aguirre

    This paper deals with the compensation of nonlinearities in dynamical systems using nonlinear polynomial autoregressive models with exogenous inputs (NARX). The compensation approach is formulated for static and dynamical contexts, as well as its adaptation to hysteretic systems. In all of these scenarios, identified NARX models are used. The core idea is to rewrite the model as an algebraic polynomial

    更新日期:2020-11-25
  • A Sphere Decoding Algorithm for Multistep Sequential Model Predictive Control
    arXiv.cs.SY Pub Date : 2020-11-24
    Ferdinand Grimm; Zhenbin Zhang; Mehdi Baghdadi

    This paper investigates the combination of two model predictive control concepts, sequential model predictive control and long-horizon model predictive control for power electronics. To achieve sequential model predictive control, the optimization problem is split into two subproblems: The first one summarizes all control goals which linearly depend on the system inputs. Sequential model predictive

    更新日期:2020-11-25
  • A DC-Autotransformer based Multilevel Inverter for Automotive Applications
    arXiv.cs.SY Pub Date : 2020-11-24
    Ferdinand Grimm; John Wood; Mehdi Baghdadi

    This paper proposes a novel multilevel inverter for automotive applications. The topology consists of a modular DCDC converter and a tap selector, where the DC-DC converter provides several DC-output levels and the tap selector produces an AC signal by choosing different DC-output signals from the DC-DC converter. To produce the DC-levels, the DC-DC converter consists of a modular structure where the

    更新日期:2020-11-25
  • Locate the Source of Resonance-Involved Forced Oscillation in Power Systems Based on Mode Shape Analysis
    arXiv.cs.SY Pub Date : 2020-11-24
    Shutang You

    This paper proposed a new method to locate the source of forced oscillation that involves resonance with natural oscillation modes. The new method is based on comparing the oscillation mode shape of the forced oscillation with that of the natural oscillation that the forced oscillation resonating with. The location that has the largest angle difference between the forced oscillation mode and the natural

    更新日期:2020-11-25
  • A Generalizable Model for Fault Detection in Offshore Wind Turbines Based on DeepLearning
    arXiv.cs.SY Pub Date : 2020-11-24
    Soorena Salari; Nasser Sadati

    This paper presents a new deep learning-based model for fault detection in offshore wind turbines. To design a generalizable model for fault detection, we use 5 sensors and a sliding window to exploit the inherent temporal information contained in the raw time-series data obtained from sensors. The proposed model uses the nonlinear relationships among multiple sensor variables and the temporal dependency

    更新日期:2020-11-25
  • A Model-Free Loop-Shaping Method based on Iterative Learning Control
    arXiv.cs.SY Pub Date : 2020-11-24
    Li-Wei Shih; Cheng-Wei Chen

    Many techniques have been developed for the loop-shaping method in control design. While most loop-shaping methods apply a model of the open-loop controlled plant, the resulting performance depends on the accuracy of the dynamical model. This paper aims to develop a model-free loop-shaping technique. The core idea is to convert the model matching problem to a trajectory tracking problem. To achieve

    更新日期:2020-11-25
  • Rethinking solar photovoltaic parameter estimation: global optimality analysis and a simple efficient differential evolution method
    arXiv.cs.SY Pub Date : 2020-11-16
    Shuhua Gao; Cheng Xiang; Yu Ming; Tan Kuan Tak; Tong Heng Lee

    Accurate, fast, and reliable parameter estimation is crucial for modeling, control, and optimization of solar photovoltaic (PV) systems. In this paper, we focus on the two most widely used benchmark datasets and try to answer (i) whether the global minimum in terms of root mean square error (RMSE) has already been reached; and (ii) whether a significantly simpler metaheuristic, in contrast to currently

    更新日期:2020-11-25
  • Simulation-based Optimization of Toll Pricing in Large-Scale Urban Networks using the Network Fundamental Diagram: A Cross-Comparison of Methods
    arXiv.cs.SY Pub Date : 2020-11-24
    Ziyuan Gu; Meead Saberi

    Simulation-based optimization (SO or SBO) has become increasingly important to address challenging transportation network design problems. In this paper, we propose to solve two toll pricing problems with different levels of complexity using the concept of the macroscopic or network fundamental diagram (MFD or NFD), where a large-scale simulation-based dynamic traffic assignment model of Melbourne

    更新日期:2020-11-25
  • A fully distributed event-triggered communication strategy for second-order multi-agent systems consensus
    arXiv.cs.SY Pub Date : 2020-11-24
    Tao Li; Quan Qiu; Chunjiang Zhao

    This paper investigates the communication strategy for second-order multi-agent systems with nonlinear dynamics. To save the scarce resources of communication channels, a novel event-triggered communication mechanism is designed without using continuous signals among the followers. To get rid of the centralized information depending on the spectrum of the Laplacian matrix, a consensus protocol with

    更新日期:2020-11-25
  • Effective Parallelism for Equation and Jacobian Evaluation in Power Flow Calculation
    arXiv.cs.SY Pub Date : 2020-11-24
    Hantao Cui; Fangxing Li; Xin Fang

    This letter investigates parallelism approaches for equations and Jacobian evaluation in power flow calculations. Two levels of parallelism are proposed and analyzed: inter-model parallelism, which evaluates models in parallel, and intra-model parallelism, which evaluates calculations within each model in parallel. Parallelism techniques such as multi-threading and single instruction multiple data

    更新日期:2020-11-25
  • A Data-Driven Automatic Tuning Method for MPC under Uncertainty using Constrained Bayesian Optimization
    arXiv.cs.SY Pub Date : 2020-11-24
    Farshud Sorourifar; Georgios Makrygirgos; Ali Mesbah; Joel A. Paulson

    The closed-loop performance of model predictive controllers (MPCs) is sensitive to the choice of prediction models, controller formulation, and tuning parameters. However, prediction models are typically optimized for prediction accuracy instead of performance, and MPC tuning is typically done manually to satisfy (probabilistic) constraints. In this work, we demonstrate a general approach for automating

    更新日期:2020-11-25
  • Safely Learning Dynamical Systems from Short Trajectories
    arXiv.cs.SY Pub Date : 2020-11-24
    Amir Ali Ahmadi; Abraar Chaudhry; Vikas Sindhwani; Stephen Tu

    A fundamental challenge in learning to control an unknown dynamical system is to reduce model uncertainty by making measurements while maintaining safety. In this work, we formulate a mathematical definition of what it means to safely learn a dynamical system by sequentially deciding where to initialize the next trajectory. In our framework, the state of the system is required to stay within a given

    更新日期:2020-11-25
  • Linear Convergence of Distributed Mirror Descent with Integral Feedback for Strongly Convex Problems
    arXiv.cs.SY Pub Date : 2020-11-24
    Youbang Sun; Shahin Shahrampour

    Distributed optimization often requires finding the minimum of a global objective function written as a sum of local functions. A group of agents work collectively to minimize the global function. We study a continuous-time decentralized mirror descent algorithm that uses purely local gradient information to converge to the global optimal solution. The algorithm enforces consensus among agents using

    更新日期:2020-11-25
  • Symmetry Reduction in Optimal Control of Multiagent Systems on Lie Groups
    arXiv.cs.SY Pub Date : 2020-11-23
    Leonardo Colombo; Dimos V. Dimarogonas

    We study the reduction of degrees of freedom for the equations that determine necessary optimality conditions for extrema in an optimal control problem for a multiagent system by exploiting the physical symmetries of agents, where the kinematics of each agent is given by a left-invariant control system. Reduced optimality conditions are obtained using techniques from variational calculus and Lagrangian

    更新日期:2020-11-25
  • Model Order Reduction for Gas and Energy Networks
    arXiv.cs.SY Pub Date : 2020-11-24
    Christian Himpe; Sara Grundel; Peter Benner

    To counter the volatile nature of renewable energy sources, gas networks take a vital role. But, to ensure fulfillment of contracts under these new circumstances, a vast number of possible scenarios, incorporating uncertain supply and demand, has to be simulated ahead of time. This many-query task can be accelerated by model order reduction, yet, large-scale, nonlinear, parametric, hyperbolic partial

    更新日期:2020-11-25
  • On stability of nonzero set-point for non linear impulsive control systems
    arXiv.cs.SY Pub Date : 2020-11-24
    A. D'Jorge; A. L. Anderson; A. Ferramosca; A. H. González; M. Actis

    The interest in non-linear impulsive systems (NIS) has been growing due to its impact in application problems such as disease treatments (diabetes, HIV, influenza, among many others), where the control action (drug administration) is given by short-duration pulses followed by time periods of null values. Within this framework the concept of equilibrium needs to be extended (redefined) to allows the

    更新日期:2020-11-25
  • Dendritic trafficking: synaptic scaling and structural plasticity
    arXiv.cs.SY Pub Date : 2020-11-24
    Saeed Aljaberi; Timothy O'Leary; Fulvio Forni

    Neuronal circuits internally regulate electrical signaling via a host of homeostatic mechanisms. Two prominent mechanisms, synaptic scaling and structural plasticity, are believed to maintain average activity within an operating range by modifying the strength and spatial extent of network connectivity using negative feedback. However, both mechanisms operate on relatively slow timescales and thus

    更新日期:2020-11-25
  • A Data-Fusion-Assisted Telemetry Layer for Autonomous Optical Networks
    arXiv.cs.SY Pub Date : 2020-11-24
    Xiaomin Liu; Huazhi Lun; Ruoxuan Gao; Meng Cai; Lilin Yi; Weisheng Hu; Qunbi Zhuge

    For further improving the capacity and reliability of optical networks, a closed-loop autonomous architecture is preferred. Considering a large number of optical components in an optical network and many digital signal processing modules in each optical transceiver, massive real-time data can be collected. However, for a traditional monitoring structure, collecting, storing and processing a large size

    更新日期:2020-11-25
  • Policy Optimization for Markovian Jump Linear Quadratic Control: Gradient-Based Methods and Global Convergence
    arXiv.cs.SY Pub Date : 2020-11-24
    Joao Paulo Jansch-Porto; Bin Hu; Geir Dullerud

    Recently, policy optimization for control purposes has received renewed attention due to the increasing interest in reinforcement learning. In this paper, we investigate the global convergence of gradient-based policy optimization methods for quadratic optimal control of discrete-time Markovian jump linear systems (MJLS). First, we study the optimization landscape of direct policy optimization for

    更新日期:2020-11-25
  • Health-Focused Optimal Power Flow
    arXiv.cs.SY Pub Date : 2020-11-24
    Logesh Kumar; Parikshit Pareek; Sivakumar Nadarajan; Souvik Dasgupta; Amit Gupta; Hung D. Nguyen

    In this paper, we propose a novel Health-Focused Optimal Power Flow (HF-OPF) to take into account the equipment health in operational and physical constraints. The health condition index is estimated based on the possible fault characteristics for generators and batteries. The paper addresses the need for understanding the relationship between health condition index and the operational constraints

    更新日期:2020-11-25
  • Stabilizing Queuing Networks with Model Data-Independent Control
    arXiv.cs.SY Pub Date : 2020-11-23
    Qian Xie; Li Jin

    This work studies the stability of multi-class queuing networks under a class of centralized or decentralized model data-independent (MDI) control policies, which only depend on traffic state observation and network topology. Control actions include routing, sequencing, and holding. By constructing piecewise-linear test functions, we derive an easy-to-use criterion to check the stability of a multi-class

    更新日期:2020-11-25
  • Path Design and Resource Management for NOMA enhanced Indoor Intelligent Robots
    arXiv.cs.SY Pub Date : 2020-11-23
    Ruikang Zhong; Xiao Liu; Yuanwei Liu; Yue Chen; Xianbin Wang

    A communication enabled indoor intelligent robots (IRs) service framework is proposed, where non-orthogonal multiple access (NOMA) technique is adopted to enable highly reliable communications. In cooperation with the ultramodern indoor channel model recently proposed by the International Telecommunication Union (ITU), the Lego modeling method is proposed, which can deterministically describe the indoor

    更新日期:2020-11-25
  • Multi-regime analysis for computer vision-based traffic surveillance using a change-point detection algorithm
    arXiv.cs.SY Pub Date : 2020-11-07
    Seungyun Jeong; Keemin Sohn

    As a result of significant advances in deep learning, computer vision technology has been widely adopted in the field of traffic surveillance. Nonetheless, it is difficult to find a universal model that can measure traffic parameters irrespective of ambient conditions such as times of the day, weather, or shadows. These conditions vary recurrently, but the exact points of change are inconsistent and

    更新日期:2020-11-25
  • A framework for constrained static state estimation in unbalanced distribution networks
    arXiv.cs.SY Pub Date : 2020-11-23
    Marta Vanin; Tom Van Acker; Reinhilde D'hulst; Dirk Van Hertem

    State estimation plays a key role in the transition from the passive to the active operation of distribution systems, as it allows to monitor these networks and, successively, to perform control actions. However, designing state estimators for distribution systems carries a significant amount of challenges. This is due to the physical complexity of the networks, e.g., phase unbalance, and limited measurements

    更新日期:2020-11-25
  • Urgency-aware Optimal Routing in Repeated Games through Artificial Currencies
    arXiv.cs.SY Pub Date : 2020-11-23
    Mauro SalazarMaurice; Dario PaccagnanMaurice; Andrea AgazziMaurice; W. P. M. H.Maurice; Heemels

    When people choose routes minimizing their individual delay, the aggregate congestion can be much higher compared to that experienced by a centrally-imposed routing. Yet centralized routing is incompatible with the presence of self-interested agents. How can we reconcile the two? In this paper we address this question within a repeated game framework and propose a fair incentive mechanism based on

    更新日期:2020-11-25
  • Power Market Tool (POMATO) for the Analysis of Zonal Electricity Markets
    arXiv.cs.SY Pub Date : 2020-11-23
    Richard Weinhold; Robert Mieth

    The proposed open-source Power Market Tool (POMATO) aims to enable research on interconnected modern and future electricity markets in the context of the physical transmission system and its secure operation. POMATO has been designed to study capacity allocation and congestion management (CACM) policies of European zonal electricity markets, especially flow-based market coupling (FBMC). For this purpose

    更新日期:2020-11-25
  • A Two-Layer Framework with Battery Temperature Optimal Control and Network Optimal Power Flow
    arXiv.cs.SY Pub Date : 2020-11-23
    Anshuman Singh; Wang Peng; Hung D. Nguyen

    Battery energy storage is an essential component of a microgrid. The working temperature of the battery is an important factor as a high-temperature condition generally increases losses, reduces useful life, and can even lead to fire hazards. Hence, it is indispensable to regulate the temperature profile of the battery modules/packs properly in the battery energy storage during the operation. In view

    更新日期:2020-11-25
  • A hybrid barrier certificate approach to satisfy linear temporal logic specifications
    arXiv.cs.SY Pub Date : 2020-11-23
    Andrea Bisoffi; Dimos V. Dimarogonas

    In this work we formulate the satisfaction of a (syntactically co-safe) linear temporal logic specification on a physical plant through a recent hybrid dynamical systems formalism. In order to solve this problem, we introduce an extension to such a hybrid system framework of the so-called eventuality property, which matches suitably the condition for the satisfaction of such a temporal logic specification

    更新日期:2020-11-25
  • Analysis of Empirical Mode Decomposition-based Load and Renewable Time Series Forecasting
    arXiv.cs.SY Pub Date : 2020-11-23
    Nima Safari; George Price; Chi Yung Chung

    The empirical mode decomposition (EMD) method and its variants have been extensively employed in the load and renewable forecasting literature. Using this multiresolution decomposition, time series (TS) related to the historical load and renewable generation are decomposed into several intrinsic mode functions (IMFs), which are less non-stationary and non-linear. As such, the prediction of the components

    更新日期:2020-11-25
  • Optimal Power Control for DoS Attack over Fading Channel: A Game-Theoretic Approach
    arXiv.cs.SY Pub Date : 2020-11-23
    Jie Wang; Jiahu Qin; Menglin Li; Yang Shi

    In this paper, we investigate remote state estimation against an intelligent denial-of-service (DoS) attack over a vulnerable wireless network whose channel undergoes attenuation and distortion caused by fading. We use the sensor to observe system states and transmit its local state estimates to the remote center. Meanwhile, the attacker injects a jamming signal to destroy the packet accepted by the

    更新日期:2020-11-25
  • Data-Driven Stabilization of Nonlinear Systems with Rational Dynamics
    arXiv.cs.SY Pub Date : 2020-11-23
    Robin Strässer; Julian Berberich; Frank Allgöwer

    In this paper, we present a data-driven controller design method for continuous-time nonlinear systems with rational system dynamics, using no model knowledge but only measured data affected by noise. We first extend recent results on data-driven control for linear time-invariant systems by presenting a purely data-driven representation of unknown nonlinear systems with rational dynamics. By applying

    更新日期:2020-11-25
  • Causality Graph of Vehicular Traffic Flow
    arXiv.cs.SY Pub Date : 2020-11-23
    Sina Molavipour; Germán Bassi; Mladen Čičić; Mikael Skoglund; Karl Henrik Johansson

    In an intelligent transportation system, the effects and relations of traffic flow at different points in a network are valuable features which can be exploited for control system design and traffic forecasting. In this paper, we define the notion of causality based on the directed information, a well-established data-driven measure, to represent the effective connectivity among nodes of a vehicular

    更新日期:2020-11-25
  • KPC: Learning-Based Model Predictive Control with Deterministic Guarantees
    arXiv.cs.SY Pub Date : 2020-11-23
    Emilio T. Maddalena; Paul Scharnhorst; Yuning Jiang; Colin N. Jones

    We propose Kernel Predictive Control (KPC), a learning-based predictive control strategy that enjoys deterministic guarantees of safety. Noise-corrupted samples of the unknown system dynamics are used to learn several models through the formalism of non-parametric kernel regression. By treating each prediction step individually, we dispense with the need of propagating sets through highly non-linear

    更新日期:2020-11-25
  • Indias Rise in Nanoelectronics Research
    arXiv.cs.SY Pub Date : 2020-11-23
    Udayan Ganguly; Sandip Lashkare; Swaroop Ganguly

    Modern semiconductors innovation has a strong relation to scale and skill. While India has a significant demand for semiconductors, it has a daunting challenge to create a semiconductor ecosystem. Yet, India has quietly come a long way. Starting with Centers of Excellence in Nanoelectronics (CENs) initiated in 2006 and broad science and technology funding, India has transformed its nanoelectronics

    更新日期:2020-11-25
  • Restricted Airspace Protection using Multi-UAV Spatio-TemporalMulti-Task Allocation
    arXiv.cs.SY Pub Date : 2020-11-23
    Shridhar Velhal; Suresh Sundaram

    This paper addresses the problem of restricted airspace protection from invaders using the cooperative multi-UAV system. The objective is to detect and capture the invaders cooperatively by a team of homogeneous UAVs (called evaders)before invaders enter the restricted airspace. The problem of restricted airspace protection problem is formulated as a Multi-UAV Spatio-Temporal Multi-Task Allocation

    更新日期:2020-11-25
  • Risk-Sensitive Motion Planning using Entropic Value-at-Risk
    arXiv.cs.SY Pub Date : 2020-11-23
    Anushri Dixit; Mohamadreza Ahmadi; Joel W. Burdick

    We consider the problem of risk-sensitive motion planning in the presence of randomly moving obstacles. To this end, we adopt a model predictive control (MPC) scheme and pose the obstacle avoidance constraint in the MPC problem as a distributionally robust constraint with a KL divergence ambiguity set. This constraint is the dual representation of the Entropic Value-at-Risk (EVaR). Building upon this

    更新日期:2020-11-25
  • Identifying Critical Fleet Sizes Using a Novel Agent-Based Modelling Framework for Autonomous Ride-Sourcing
    arXiv.cs.SY Pub Date : 2020-11-22
    Renos Karamanis; He-in Cheong; Simon Hu; Marc Stettler; Panagiotis Angeloudis

    Ride-sourcing platforms enable an on-demand shared transport service by solving decision problems often related to customer matching, pricing and vehicle routing. These problems have been frequently represented using aggregated mathematical models and solved via algorithmic approaches designed by researchers. The increasing complexity of ride-sourcing environments compromises the accuracy of aggregated

    更新日期:2020-11-25
  • Primal-dual Learning for the Model-free Risk-constrained Linear Quadratic Regulator
    arXiv.cs.SY Pub Date : 2020-11-22
    Feiran Zhao; Keyou You

    Risk-aware control, though with promise to tackle unexpected events, requires a known exact dynamical model. In this work, we propose a model-free framework to learn a risk-aware controller with a focus on the linear system. We formulate it as a discrete-time infinite-horizon LQR problem with a state predictive variance constraint. To solve it, we parameterize the policy with a feedback gain pair and

    更新日期:2020-11-25
  • A Workbench for Testing and Simulation Faults in Three-phase Electric Motors with Intelligent Electronic Device and Microcontrolled System
    arXiv.cs.SY Pub Date : 2020-11-22
    Giovanni Faria; Michel Fernandes Peres; Osmar Moreira da Silva Neto; Jefferson Rodrigo Schuertz; Edson Leonardo dos Santos; Carlos Alexandre Gouvea da Silva

    Electric motors can be damaged or operate improperly from a possible set of failures. Such failures are related to high or very low voltage and current levels, phase loss or blocked rotor. Therefore, it is important to protect these equipments through appropriate mechanisms. Alternatively, a workbench can simulate detectable failures related to the engines, allowing to change parameters, in which maintenance

    更新日期:2020-11-25
  • A Comprehensive Survey on Real-Time Voltage Stability Assessment for Power Systems
    arXiv.cs.SY Pub Date : 2020-11-21
    Gourav Wadhwa; Amandeep Kharb; Satyam Mishra; Mohit Kumar; Shreyansh Srivastav

    Accurate real-time assessment of power systems voltage stability has been an active area of research in the past few decades. In the past decade, after the development of phasor measurement units (PMU), a lot of discussions has been going on phasor measurement techniques for real-time voltage stability. The fundamental idea behind these methods is to find the Thevenin equivalents of the system, and

    更新日期:2020-11-25
  • Comprehensive Assessment of COVID-19 Impact on Saskatchewan Power System Operations
    arXiv.cs.SY Pub Date : 2020-11-21
    Nima Safari; George Price; C. Y. Chung

    This paper presents lessons learned to date during the Coronavirus Disease 2019 (COVID-19) pandemic from the viewpoint of Saskatchewan power system operations. A load estimation approach is developed to identify how the closures affecting businesses, schools, and other non-critical businesses due to COVID-19 changed the electricity consumption. Furthermore, the impacts of COVID-19 containment measures

    更新日期:2020-11-25
  • Integrating Structure, Information Architecture and Control Design for Tensegrity Systems
    arXiv.cs.SY Pub Date : 2020-11-21
    Raman Goyal; Manoranjan Majji; Robert E. Skelton

    A novel unified approach to jointly optimize structural design parameters, actuator and sensor precision and controller parameters is presented in this paper. The joint optimization problem is posed as a covariance control problem, where feasibility is achieved by bounding the covariance of the output as well as that of the control signals. The formulation is used to design a tensegrity system, where

    更新日期:2020-11-25
  • A System for Automatic Rice Disease Detectionfrom Rice Paddy Images Serviced via a Chatbot
    arXiv.cs.SY Pub Date : 2020-11-21
    Pitchayagan Temniranrat; Kantip Kiratiratanapruk; Apichon Kitvimonrat; Wasin Sinthupinyo; Sujin Patarapuwadol

    A rice disease diagnosis LINE Bot System from paddy field images was presented in this paper. An easy-to-use automatic rice disease diagnosis system was necessary to help rice farmers improve yield and quality. We targeted on the images took from the paddy environment without special sample preparation. We used a deep learning neural networks technique to detect rice disease in the images. We purposed

    更新日期:2020-11-25
  • Co-Design of Autonomous Systems: From Hardware Selection to Control Synthesis
    arXiv.cs.SY Pub Date : 2020-11-21
    Gioele Zardini; Andrea Censi; Emilio Frazzoli

    Designing cyber-physical systems is a complex task which requires insights at multiple abstraction levels. The choices of single components are deeply interconnected and need to be jointly studied. In this work, we consider the problem of co-designing the control algorithm as well as the platform around it. In particular, we leverage a monotone theory of co-design to formalize variations of the LQG

    更新日期:2020-11-25
  • Multi Time-scale Imputation aided State Estimation in Distribution System
    arXiv.cs.SY Pub Date : 2020-11-21
    Shweta Dahale; Balasubramaniam Natarajan

    With the transition to a smart grid, we are witnessing a significant growth in sensor deployments and smart metering infrastructure in the distribution system. However, information from these sensors and meters are typically unevenly sampled at different time-scales and are incomplete. It is critical to effectively aggregate these information sources for situational awareness. In order to reconcile

    更新日期:2020-11-25
  • Towards Robust Data-Driven Control Synthesis for Nonlinear Systems with Actuation Uncertainty
    arXiv.cs.SY Pub Date : 2020-11-21
    Andrew J. Taylor; Victor D. Dorobantu; Sarah Dean; Benjamin Recht; Yisong Yue; Aaron D. Ames

    Modern nonlinear control theory seeks to endow systems with properties such as stability and safety, and has been deployed successfully across various domains. Despite this success, model uncertainty remains a significant challenge in ensuring that model-based controllers transfer to real world systems. This paper develops a data-driven approach to robust control synthesis in the presence of model

    更新日期:2020-11-25
  • Learning Control Barrier Functions with High Relative Degree for Safety-Critical Control
    arXiv.cs.SY Pub Date : 2020-11-21
    Chuanzheng Wang; Yinan Li; Yiming Meng; Stephen L. Smith; Jun Liu

    Control barrier functions have shown great success in addressing control problems with safety guarantees. These methods usually find the next safe control input by solving an online quadratic programming problem. However, model uncertainty is a big challenge in synthesizing controllers. This may lead to the generation of unsafe control actions, resulting in severe consequences. In this paper, we develop

    更新日期:2020-11-25
  • Learning-based attacks in Cyber-Physical Systems: Exploration, Detection, and Control Cost trade-offs
    arXiv.cs.SY Pub Date : 2020-11-21
    Anshuka Rangi; Mohammad Javad Khojasteh; Massimo Franceschetti

    We study the problem of learning-based attacks in linear systems, where the communication channel between the controller and the plant can be hijacked by a malicious attacker. We assume the attacker learns the dynamics of the system from observations, then overrides the controller's actuation signal, while mimicking legitimate operation by providing fictitious sensor readings to the controller. On

    更新日期:2020-11-25
  • SymAR: Symmetry Abstractions and Refinement for Accelerating Scenarios with Neural Network Controllers Verification
    arXiv.cs.SY Pub Date : 2020-11-21
    Hussein Sibai; Yangge Li; Sayan Mitra

    We present a Symmetry-based abstraction refinement algorithm SymAR that is directed towards safety verification of large-scale scenarios with complex dynamical systems. The abstraction maps modes with symmetric dynamics to a single abstract mode and refinements recursively split the modes when safety checks fail. We show how symmetry abstractions can be applied effectively to closed-loop control systems

    更新日期:2020-11-25
  • Analysis and Evaluation of Baseline Manipulation in Demand Response Programs
    arXiv.cs.SY Pub Date : 2020-11-20
    Xiaochu Wang; Wenyuan Tang

    The customer baseline is required to assign rebates to participants in baseline-based demand response (DR) programs. The average baseline method has been widely accepted in practice due to its simplicity and reliability. However, the customer's baseline manipulation is little-known in the literature. We start from a customer's perspective and establish a Markov decision process to model the customer's

    更新日期:2020-11-25
  • Cost-Effective Quasi-Parallel Sensing Instrumentation for Industrial Chemical Species Tomography
    arXiv.cs.SY Pub Date : 2020-11-20
    Godwin Enemali; Rui Zhang; Hugh McCann; Chang Liu

    Chemical Species Tomography (CST) has been widely applied for imaging of critical gas-phase parameters in industrial processes. To acquire high-fidelity images, CST is typically implemented by line-of-sight Wavelength Modulation Spectroscopy (WMS) measurements from multiple laser beams. The modulated transmission signal on each laser beam needs to be a) digitised by a high-speed analogue-to-digital

    更新日期:2020-11-25
  • Learning How to Solve Bubble Ball
    arXiv.cs.SY Pub Date : 2020-11-20
    Hotae Lee; Monimoy Bujarbaruah; Francesco Borrelli

    "Bubble Ball" is a game built on a 2D physics engine, where a finite set of objects can modify the motion of a bubble-like ball. The objective is to choose the set and the initial configuration of the objects, in order to get the ball to reach a target flag. The presence of obstacles, friction, contact forces and combinatorial object choices make the game hard to solve. In this paper, we propose a

    更新日期:2020-11-25
  • Landmark and IMU Data Fusion: Systematic Convergence Geometric Nonlinear Observer for SLAM and Velocity Bias
    arXiv.cs.SY Pub Date : 2020-11-20
    Hashim A. Hashim; Abdelrahman E. E. Eltoukhy

    Navigation solutions suitable for cases when both autonomous robot's pose (\textit{i.e}., attitude and position) and its environment are unknown are in great demand. Simultaneous Localization and Mapping (SLAM) fulfills this need by concurrently mapping the environment and observing robot's pose with respect to the map. This work proposes a nonlinear observer for SLAM posed on the manifold of the Lie

    更新日期:2020-11-25
  • Distributed Robust State Estimation for Hybrid AC/DC Distribution Systems using Multi-Source Data
    arXiv.cs.SY Pub Date : 2020-11-20
    Manyun Huang; Junbo Zhao; Zhinong Wei; Marco Pau; Guoqiang Sun

    Hybrid AC/DC distribution systems are becoming a popular means to accommodate the increasing penetration of distributed energy resources and flexible loads. This paper proposes a distributed and robust state estimation (DRSE) method for hybrid AC/DC distribution systems using multiple sources of data. In the proposed distributed implementation framework, a unified robust linear state estimation model

    更新日期:2020-11-25
  • Finite Horizon Discrete Models for Multi-Agent Control Systems with Coupled Dynamics
    arXiv.cs.SY Pub Date : 2020-11-20
    Dimitris Boskos; Dimos V. Dimarogonas

    The goal of this paper is to obtain online abstractions for coupled multi-agent systems in a decentralized manner. A discrete model which captures the motion capabilities of each agent is derived over a bounded time-horizon, by discretizing a corresponding overapproximation of the agent's reachable states. The individual abstractions' composition provides a correct representation of the coupled continuous

    更新日期:2020-11-25
  • Direct Transcription for Dynamic Optimization: A Tutorial with a Case Study on Dual-Patient Ventilation During the COVID-19 Pandemic
    arXiv.cs.SY Pub Date : 2020-11-23
    Eric C. Kerrigan; Yuanbo Nie; Omar Faqir; Caroline H. Kennedy; Steven A. Niederer; Jose A. Solis-Lemus; Peter Vincent; Steven E. Williams

    A variety of optimal control, estimation, system identification and design problems can be formulated as functional optimization problems with differential equality and inequality constraints. Since these problems are infinite-dimensional and often do not have a known analytical solution, one has to resort to numerical methods to compute an approximate solution. This paper uses a unifying notation

    更新日期:2020-11-25
  • The Value of Data in Learning-Based Control for Training Subset Selection
    arXiv.cs.SY Pub Date : 2020-11-20
    Armin Lederer; Alexandre Capone; Thomas Beckers; Jonas Umlauft; Sandra Hirche

    Despite the existence of formal guarantees for learning-based control approaches, the relationship between data and control performance is still poorly understood. In this paper, we present a measure to quantify the value of data within the context of a predefined control task. Our approach is applicable to a wide variety of unknown nonlinear systems that are to be controlled by a generic learning-based

    更新日期:2020-11-25
  • Design Approach for Additive Manufacturing in Spare Part Supply Chains
    arXiv.cs.SY Pub Date : 2020-11-20
    Filipe M. de Brito; Gélson da Cruz Júnior; Enzo M. Frazzon; João P. Basto; Symone G. S. Alcalá

    In the current industrial revolution, additive manufacturing (AM) embodies a promising technology that can enhance the effectiveness, adaptability, and competitiveness of supply chains (SCs). Moreover, it facilitates the development of distributed SCs, thereby enhancing product availability, inventory levels, and lead time. However, the wide adoption of AM in industrial SCs creates various challenges

    更新日期:2020-11-25
  • Learning Hidden Markov Models from Aggregate Observations
    arXiv.cs.SY Pub Date : 2020-11-23
    Rahul Singh; Qinsheng Zhang; Yongxin Chen

    In this paper, we propose an algorithm for estimating the parameters of a time-homogeneous hidden Markov model from aggregate observations. This problem arises when only the population level counts of the number of individuals at each time step are available, from which one seeks to learn the individual hidden Markov model. Our algorithm is built upon expectation-maximization and the recently proposed

    更新日期:2020-11-25
  • MAC for Machine Type Communications in Industrial IoT -- Part II: Scheduling and Numerical Results
    arXiv.cs.SY Pub Date : 2020-11-22
    Jie GaoSherman; Mushu LiSherman; Weihua ZhuangSherman; XueminSherman; Shen; Xu Li

    In the second part of this paper, we develop a centralized packet transmission scheduling scheme to pair with the protocol designed in Part I and complete our medium access control (MAC) design for machine-type communications in the industrial internet of things. For the networking scenario, fine-grained scheduling that attends to each device becomes necessary, given stringent quality of service (QoS)

    更新日期:2020-11-25
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