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Event-triggered distributed estimation with decaying communication rate

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arXiv 2021年

作者： He, Xingkang Xing, Yu Wu, Junfeng Johansson, Karl H. The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology StockholmSE-100 44 Sweden Guangdong Province Shenzhen China College of Control Science and Engineering Zhejiang University Zhejiang Province Hangzhou China

We study distributed estimation of a high-dimensional static parameter vector through a group of sensors whose communication network is modeled by a fixed directed graph. Different from existing time-triggered communication schemes, an event-triggered asynchronous scheme is investigated in order to reduce communication while preserving estimation convergence. A distributed estimation algorithm with a single step size is first proposed based on an event-triggered communication scheme with a time-dependent decaying threshold. With the event-triggered scheme, each sensor sends its estimate to neighbor sensors only when the difference between the current estimate and the last sent-out estimate is larger than the triggering threshold. Different sensors can have different step sizes and triggering thresholds, enabling the parameter estimation process to be conducted in a fully distributed way. We prove that the proposed algorithm has mean-square and almost-sure convergence respectively, under an integrated condition of sensor network topology and sensor measurement matrices. The condition is satisfied if the topology is a balanced digraph containing a spanning tree and the system is collectively observable. The collective observability is the possibly mildest condition, since it is a spatially and temporally collective condition of all sensors and allows sensor measurement matrices to be time-varying, stochastic, and non-stationary. Moreover, we provide estimates for the convergence rates, which are related to the step size as well as the triggering threshold. Furthermore, as an essential metric of sensor communication intensity in the event-triggered distributed algorithms, the communication rate is proved to decay to zero with a certain speed almost surely as time goes to infinity. In addition, we show that it is feasible to tune the threshold and the step size such that requirements of algorithm convergence and communication rate decay are satisfied simultaneously. We

关键词： Directed graphs

Optimal convergence rates of totally asynchronous optimization

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Optimal convergence rates of totally asynchronous optimizati...

IEEE Conference on decision and control

作者： Xuyang Wu Sindri Magnússon Hamid Reza Feyzmahdavian Mikael Johansson Division of Decision and Control Systems School of EECS KTH Royal Institute of Technology Stockholm Sweden Department of Computer and System Science Stockholm University Stockholm Sweden ABB Cooperate Research Västerås Sweden

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

Asynchronous optimization algorithms are at the core of modern machine learning and resource allocation systems. However, most convergence results consider bounded information delays and several important algorithms lack guarantees when they operate under total asynchrony. In this paper, we derive explicit convergence rates for the proximal incremental aggregated gradient (PIAG) and the asynchronous block-coordinate descent (Async-BCD) methods under a specific model of total asynchrony, and show that the derived rates are order-optimal. The convergence bounds provide an insightful understanding of how the growth rate of the delays deteriorates the convergence times of the algorithms. Our theoretical findings are demonstrated by a numerical example.

关键词： Machine learning algorithms Computational modeling Machine learning Numerical models Delays Resource management Indexes

Stochastic Stability of Discrete-time Phase-coupled Oscillators over Uncertain and Random Networks

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arXiv 2021年

作者： Jafarian, Matin Mamduhi, Mohammad H. Johansson, Karl H. The Delft Center for Systems and Control Delft University of Technology Netherlands The Automatic Control Laboratory ETH Zürich Switzerland The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Digital Futures Sweden

This article studies stochastic relative phase stability, i.e., stochastic phase-cohesiveness, of discrete-time phase-coupled oscillators. Stochastic phase-cohesiveness in two types of networks is studied. First, we consider oscillators coupled with 2π-periodic odd functions over underlying undirected graphs subject to both multiplicative and additive stochastic uncertainties. We prove stochastic phase-cohesiveness of the network with respect to two specific, namely in-phase and anti-phase, sets by deriving sufficient coupling conditions. We show the dependency of these conditions on the size of the mean values of additive and multiplicative uncertainties, as well as the sign of the mean values of multiplicative uncertainties. Furthermore, we discuss the results under a relaxation of the odd property of the coupling function. Second, we study an uncertain network in which the multiplicative uncertainties are governed by the Bernoulli process representing the well-known Erdös-Rényi network. We assume constant exogenous frequencies and derive sufficient conditions for achieving both stochastic phase-cohesive and phase-locked solutions, i.e., stochastic phase-cohesiveness with respect to the origin. For the latter case, where identical exogenous frequencies are assumed, we prove that any positive probability of connectivity leads to phase-locking. Thorough analyses are provided, and insights obtained from stochastic analysis are discussed, along with numerical simulations to validate the analytical results. © 2021, CC BY.

关键词： Stochastic systems

Towards probabilistic intrusion detection in supervisory control of discrete event systems 1

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IFAC-PapersOnLine 2020年第2期53卷 1776-1782页

作者： Rômulo Meira-Góes Christoforos Keroglou Stéphane Lafortune Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor USA Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

In control systems, sensor deception is a class of attacks where an attacker manipulates sensor readings to cause damage to the system. Our work investigates quantitative measurements to detect this class of attacks in the context of stochastic supervisory control. We introduce the notion of ϵ -safe systems, which is a first step to generalize qualitative intrusion detection conditions to quantitative intrusion detection conditions. We provide sufficient and necessary conditions to verify if a system is ϵ -safe. Moreover, we provide an algorithm that verifies these conditions, which implies that the problem is decidable.

关键词： Supervisory control automata Discrete event modeling simulation Intrusion detection Security

Learning Goal-based Movement via Motivational-based Models in Cognitive Mobile Robots

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arXiv 2023年

作者： Berto, Letícia Costa, Paula Simões, Alexandre Gudwin, Ricardo Colombini, Esther Laboratory of Robotics and Cognitive Systems Institute of Computing University of Campinas Brazil Dept. of Computer Engineering and Industrial Automation School of Electrical and Computer Engineering University of Campinas Brazil Dept. of Control and Automation Engineering Institute of Science and Technology São Paulo State University Sorocaba Brazil University of Campinas Brazil

Humans have needs motivating their behavior according to intensity and context. However, we also create preferences associated with each action’s perceived pleasure, which is susceptible to changes over time. This makes decision-making more complex, requiring learning to balance needs and preferences according to the context. To understand how this process works and enable the development of robots with a motivational-based learning model, we computationally model a motivation theory proposed by Hull. In this model, the agent (an abstraction of a mobile robot) is motivated to keep itself in a state of homeostasis. We added hedonic dimensions to see how preferences affect decision-making, and we employed reinforcement learning to train our motivated-based agents. We run three agents with energy decay rates representing different metabolisms in two different environments to see the impact on their strategy, movement, and behavior. The results show that the agent learned better strategies in the environment that enables choices more adequate according to its metabolism. The use of pleasure in the motivational mechanism significantly impacted behavior learning, mainly for slow metabolism agents. When survival is at risk, the agent ignores pleasure and equilibrium, hinting at how to behave in harsh scenarios. Copyright © 2023, The Authors. All rights reserved.

关键词： Metabolism

Data-driven control of T-Product-based Dynamical systems

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arXiv 2025年

作者： He, Ziqin Mei, Yidan Mei, Shenghan Mao, Xin Dong, Anqi Wang, Ren Chen, Can Department of Mathematics University of North Carolina at Chapel Hill Chapel HillNC27599 United States School of Data Science and Society University of North Carolina at Chapel Hill Chapel HillNC27599 United States Division of Decision and Control Systems Department of Mathematics KTH Royal Institute of Technology StockholmSE-100 44 Sweden Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States School of Data Science and Society the Department of Mathematics University of North Carolina at Chapel Hill Chapel HillNC27599 United States

Data-driven control is a powerful tool that enables the design and implementation of control strategies directly from data without explicitly identifying the underlying system dynamics. While various data-driven control techniques, such as stabilization, linear quadratic regulation, and model predictive control, have been extensively developed, these methods are not inherently suited for multi-linear dynamical systems, where the states are represented as higher-order tensors. In this article, we propose a novel framework for data-driven control of T-product-based dynamical systems (TPDSs), where the system evolution is governed by the T-product between a third-order dynamic tensor and a third-order state tensor. In particular, we offer necessary and sufficient conditions to determine the data informativity for system identification, stabilization by state feedback, and T-product quadratic regulation of TPDSs with detailed complexity analyses. Finally, we validate our framework through numerical *** Codes 37N35, 93-08, 93A15 © 2025, CC BY.

关键词： Tensors

Results on sparsity and estimation accuracy in Orthogonal Matching Pursuit with application to optimal input design

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Automatica 2025年 179卷

作者： Javad Parsa Cristian R. Rojas Håkan Hjalmarsson Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm 10044 Sweden Centre for Advanced Bio Production KTH Royal Institute of Technology Stockholm 10044 Sweden

Appropriate excitation conditions are essential for reliably identifying sparse models. In regression problems, these conditions are often characterized by properties of the regressor matrix, with mutual coherence, the maximum correlation between regressors, playing a central role in enabling sparse recovery. However, obtaining sparse estimates is rarely the sole objective, as estimation accuracy is also important. When a model is used in an application, e.g. control design, acceptable performance with high probability can be (approximately) ensured by requiring the confidence ellipsoid to be contained in a certain ellipsoid which depends on the performance specifications in the application. However, it is well known that experiments fulfilling such requirements using minimal excitation energy (least-costly experiments) tend to generate highly correlated regressors, in conflict with the requirements for sparsity. Adhering to this setting and focusing on the popular orthogonal matching pursuit algorithm (OMP), we derive conditions for simultaneously ensuring sparsity and having the confidence ellipsoid contained in a pre-specified ellipsoid. We extend this result to a recently proposed two stage sparse estimation method where a linear transformation is used in a pre-processing step before OMP to reduce mutual coherence. A final contribution is to show that our theoretical results are of importance in optimal input design for sparse models. Specifically, we show that the choice of hyperparameters in a recently proposed input design method can be guided by our contributions and we show explicitly how this can be done in this two-stage method.

关键词： System identification Input design Sparse estimation Mutual coherence

On Robustness Metrics for Learning STL Tasks

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On Robustness Metrics for Learning STL Tasks

American control Conference

作者： Peter Varnai Dimos V. Dimarogonas Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden

ISBN: (数字)9781538682661

ISBN: (纸本)9781538682678

Signal temporal logic (STL) is a powerful tool for describing complex behaviors for dynamical systems. Among many approaches, the control problem for systems under STL task constraints is well suited for learning-based solutions, because STL is equipped with robustness metrics that quantify the satisfaction of task specifications and thus serve as useful rewards. In this work, we examine existing and potential robustness metrics specifically from the perspective of how they can aid such learning algorithms. We show that various desirable properties restrict the form of potential metrics, and introduce a new one based on the results. The effectiveness of this new robustness metric for accelerating the learning procedure is demonstrated through an insightful case study.

关键词： learning algorithms DYNAMICAL systems Robustness Perspective

State Estimation for Linear systems with Quadratic Outputs

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arXiv 2023年

作者： Berkane, Soulaimane Theodosis, Dionysis Hamel, Tarek Dimarogonas, Dimos V. Département d’informatique et d’ingénierie Université du Québec en Outaouis GatineauQC Canada theDepartment of Electrical Engineering Lakehead University ON Canada the Dynamic Systems and Simulation Laboratory Technical University of Crete Greece the Divi-sion of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden I3S-CNRS University Côte d’Azur Institut universitaire de France France

This letter deals with the problem of state estimation for a class of systems involving linear dynamics with multiple quadratic output measurements. We propose a systematic approach to immerse the original system into a linear time-varying (LTV) system of a higher dimension. The methodology extends the original system by incorporating a minimum number of auxiliary states, ensuring that the resulting extended system exhibits both linear dynamics and linear output. Consequently, any Kalman-type observer can showcase global state estimation, provided the system is uniformly observable. © 2023, CC BY.

关键词： Time varying systems