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Stochastic phase-cohesiveness of discrete-time kuramoto oscillators in a frequency-dependent tree network

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arXiv 2019年

作者： Jafarian, Matin Mamduhi, Mohammad H. Johansson, Karl H. Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

This paper presents the notion of stochastic phasecohesiveness based on the concept of recurrent Markov chains and studies the conditions under which a discrete-time stochastic Kuramoto model is phase-cohesive. It is assumed that the exogenous frequencies of the oscillators are combined with random variables representing uncertainties. A bidirectional tree network is considered such that each oscillator is coupled to its neighbors with a coupling law which depends on its own noisy exogenous frequency. In addition, an undirected tree network is studied. For both cases, a sufficient condition for the common coupling strength (κ) and a necessary condition for the sampling-period are derived such that the stochastic phase-cohesiveness is achieved. The analysis is performed within the stochastic systems framework and validated by means of numerical simulations. Copyright © 2019, The Authors. All rights reserved.

关键词： Stochastic systems

Analysis of a nonlinear opinion dynamics model with biased assimilation

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arXiv 2019年

作者： Xia, Weiguo Ye, Mengbin Liu, Ji Cao, Ming Sun, Xi-Ming School of Control Science and Engineering Dalian University of Technology China Faculty of Science and Engineering ENTEG University of Groningen Netherlands Department of Electrical and Computer Engineering Stony Brook University United States

—This paper analyzes a nonlinear opinion dynamics model which generalizes the DeGroot model by introducing a bias parameter for each individual. The original DeGroot model is recovered when the bias parameter is equal to zero. The magnitude of this parameter reflects an individual’s degree of bias when assimilating new opinions, and depending on the magnitude, an individual is said to have weak, intermediate, and strong bias. The opinions of the individuals lie between 0 and 1. It is shown that for strongly connected networks, the equilibria with all elements equal identically to the extreme value 0 or 1 is locally exponentially stable, while the equilibrium with all elements equal to the neutral consensus value of 21 is unstable. Regions of attraction for the extreme consensus equilibria are given. For the equilibrium consisting of both extreme values 0 and 1, which corresponds to opinion polarization according to the model, it is shown that the equilibrium is unstable for all strongly connected networks if individuals all have weak bias, becomes locally exponentially stable for complete and two-island networks if individuals all have strong bias, and its stability heavily depends on the network topology when individuals have intermediate bias. Analysis on star graphs and simulations show that additional equilibria may exist where individuals form clusters. Copyright © 2019, The Authors. All rights reserved.

关键词： Topology

Robust Trajectory Tracking control for Underactuated Autonomous Underwater Vehicles

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arXiv 2019年

作者： Heshmati-alamdari, Shahab Nikou, Alexandros Dimarogonas, Dimos V. Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

— Motion control of underwater robotic vehicles is a demanding task with great challenges imposed by external disturbances, model uncertainties and constraints of the operating workspace. Thus, robust motion control is still an open issue for the underwater robotics community. In that sense, this paper addresses the tracking control problem of 3D trajectories for underactuated underwater robotic vehicles operating in a constrained workspace including obstacles. In particular, a robust Nonlinear Model Predictive control (NMPC) scheme is presented for the case of underactuated Autonomous Underwater Vehicles (AUVs) (i.e., vehicles actuated only in surge, heave and yaw). The purpose of the controller is to steer the underactuated AUV to a desired trajectory with guaranteed input and state constraints inside a partially known and dynamic environment where the knowledge of the operating workspace is constantly updated on–line via the vehicle’s on–board sensors. In particular, by considering a ball which covers the volume of the system, obstacle avoidance with any of the detected obstacles is guaranteed, despite the model dynamic uncertainties and the presence of external disturbances representing ocean currents and waves. The proposed feedback control law consists of two parts: an online law which is the outcome of a Finite Horizon Optimal control Problem (FHOCP) solved for the nominal dynamics;and a state feedback law which is tuned off-line and guarantees that the real trajectories remain bounded in a hyper-tube centered along the nominal trajectories for all times. Finally, a simulation study verifies the performance and efficiency of the proposed approach. Copyright © 2019, The Authors. All rights reserved.

关键词： Ocean currents

Structural Decomposition and its Use in Qualitative Model-based Diagnosis of Complex Systems : Plenary Talk

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Structural Decomposition and its Use in Qualitative Model-ba...

International Symposium on Intelligent Systems and Informatics (SISY)

作者： Katalin M. Hangos A. I. Pózna Systems and Control Laboratory Institute for Computer Science and Control HAS Budapest Hungary Department of Electrical Engineering and Information Systems University of Pannonia Veszprém Hungary

ISBN: (数字)9781728121437

ISBN: (纸本)9781728121444

Decomposition offers the potential to reduce the complexity of model-based optimization, prediction, control and diagnosis by accounting for the structure and sparsity of the describing model. Motivated by this fact, a rich and powerful collection of decomposition methods are available for model based diagnosis of large-scale complex dynamic systems, too. At the same time, one usually does not have enough information about a large-scale complex dynamic system to construct its precise enough model, so a kind of qualitative dynamic model is often used for the diagnosis. Two structural decomposition based qualitative diagnostic methods are presented in this lecture, together with their component-driven system decomposition techniques.

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Saliency Detection using the Combination of Boundary Aware Color-map and Seam-map

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Saliency Detection using the Combination of Boundary Aware C...

International Conference on computer, Communication, Chemical, Material and Electronic engineering (IC4ME2)

作者： Aminul Islam Sk. Md. Masudul Ahsan Joo Kooi Tan Department of Computer Science and Engineering Khulna University of Engineering & Technology Khulna Bangladesh Department of Control Engineering Kyushu Institute of Technology Kitakyushu-shi Fukuoka Japan

ISBN: (数字)9781728130606

ISBN: (纸本)9781728130613

Salient region of an image is usually detected by using contrast and boundary priors. Along with those cues the use of seam importance map has shown promising output previously. In this study, better result is found by further exploiting the seam-map using spatial distance and color information in combination with boundary prior. Color and seam maps are also down-weighted using average spatial distance to other regions. Moreover, passing the superpixelized version of the input image into seam and color map generation procedure has improved the output. Experimental results based on MSRA 1k dataset are presented with ten state of the art methods. F-beta measures are presented along with precision recall curves to better understand the outcome. The performance comparison with compared researches proofs superiority of the proposed method.

关键词： Image color analysis Saliency detection Visualization computer vision Image segmentation Object detection

Randomised benchmarking for characterizing and forecasting correlated processes

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arXiv 2023年

作者： Zhang, Xinfang Wu, Zhihao White, Gregory A.L. Xiang, Zhongcheng Hu, Shun Peng, Zhihui Liu, Yong Zheng, Dongning Fu, Xiang Huang, Anqi Poletti, Dario Modi, Kavan Wu, Junjie Deng, Mingtang Guo, Chu Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China School of Physics and Astronomy Monash University VIC3800 Australia Dahlem Center for Complex Quantum Systems Freie Universität Berlin Berlin14195 Germany Institute of Physics Chinese Academy of Sciences Beijing100190 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha410081 China Science Mathematics and Technology Cluster and Engineering Product Development Pillar Singapore University of Technology and Design 8 Somapah Road 487372 Singapore Centre for Quantum Technologies National University of Singapore 117543 Singapore MajuLab CNRS-UNS-NUS-NTU International Joint Research Unit UMI 3654 Singapore Quantum for NSW Sydney2000 Australia

The development of fault-tolerant quantum processors relies on the ability to control noise. A particularly insidious form of noise is temporally correlated or non-Markovian noise. By combining randomized benchmarking with supervised machine learning algorithms, we develop a method to learn the details of temporally correlated noise. In particular, we can learn the time-independent evolution operator of system plus bath and this leads to (i) the ability to characterize the degree of non-Markovianity of the dynamics and (ii) the ability to predict the dynamics of the system even beyond the times we have used to train our model. We exemplify this by implementing our method on a superconducting quantum processor. Our experimental results show a drastic change between the Markovian and non-Markovian regimes for the learning accuracies. Copyright © 2023, The Authors. All rights reserved.

关键词： Benchmarking

Application of the Dynamic Iterative Learning control to the Heteroplanar Active Magnetic Bearing

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IFAC-PapersOnLine 2020年第2期53卷 1511-1516页

作者： Lukasz Hladowski Arkadiusz Mystkowski Krzysztof Galkowski Eric Rogers Bing Chu Institute of Control and Computation Engineering University of Zielona Góra ul. Podgórna 50 65-246 Zielona Góra Poland Faculty of Electrical Engineering Bialystok University of Technology ul.Wiejska 45c 15-351 Bialystok Poland School of Electronics and Computer Science University of Southampton Southampton SO17 1BJ UK

Heteroplanar active magnetic bearings have numerous applications, where one example is a high-temperature gas-cooled reactors. Rotor imbalance, however, may cause problems for critical parts of the system in the form of repetitive periodic vibrations. This is known problem and periodic component extraction is widely used in active magnetic bearing unbalance control laws. More recently, iterative learning control has been considered as an alternative and this paper gives new results on this approach. In particular, a new control law in the 2D systems setting is developed and the results of a simulation based study using the model of a test rig are given, where such a study is an essential step prior to experimental validation.

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A Non-Stop Double Buffering Mechanism for Dataflow Architecture

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Journal of computer science & Technology 2018年第1期33卷 145-157页

作者： Xu Tan Xiao-Wei Shen Xiao-Chun Ye Da Wang Dong-Rui Fan Lunkai Zhang Wen-Ming Li Zhi-Min Zhang Zhi-Min Tang State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing 100049 China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214125 China Department of Computer Science The University of Chicago Chicago IL 60637 U.S.A.

Double buffering is an effective mechanism to hide the latency of data transfers between on-chip and off-chip memory. However, in dataflow architecture, the swapping of two buffers during the execution of many tiles decreases the performance because of repetitive filling and draining of the dataflow accelerator. In this work, we propose a non-stop double buffering mechanism for dataflow architecture. The proposed non-stop mechanism assigns tiles to the processing element array without stopping the execution of processing elements through optimizing control logic in dataflow architecture. Moreover, we propose a work-flow program to cooperate with the non-stop double buffering mechanism. After optimizations both on control logic and on work-flow program, the filling and draining of the array needs to be done only once across the execution of all tiles belonging to the same dataflow graph. Experimental results show that the proposed double buffering mechanism for dataftow architecture achieves a 16.2% average efficiency improvement over that without the optimization.

关键词： non-stop double buffering dataflow architecture high-performance computing

Verification of AA-Diagnosability in Probabilistic Finite Automata is PSPACE-Hard

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Verification of AA-Diagnosability in Probabilistic Finite Au...

IEEE Annual Conference on Decision and control

作者： Christoforos Keroglou Christoforos N. Hadjicostis Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus

ISBN: (数字)9781728113982

ISBN: (纸本)9781728113999

In this paper we consider the complexity of verifying the property of AA-diagnosability in probabilistic finite automata and establish that AA-diagnosability is, in general, a PSPACE-hard problem. In deterministic and nondeterministic finite automata, the property of diagnosability captures our ability to determine, based on our observation of the activity in a given finite automaton, the occurrence of any fault event, at least if we wait for at most a finite number of events (following the occurrence of the unobservable fault event). In stochastic settings where the underlying system is a probabilistic finite automaton under partial observation, there is not a prevalent notion of diagnosability and many variations have been proposed, including A-diagnosability and AA-diagnosability. Earlier work has shown that the verification of A-diagnosability (also referred to as strong stochastic diagnosability) for a given probabilistic finite automaton is a PSPACE-hard problem. In this paper, we establish that the verification of AA-diagnosability (also referred to as stochastic diagnosability) is a PSPACE-hard problem.

关键词： Automata Hidden Markov models Probabilistic logic Markov processes Complexity theory Fault diagnosis