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Wireless Communication and Control Co-Design for system Identification

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第5期23卷 4114-4126页

作者： Han, Zidong Li, Xiaoyang Zhou, Ziqin Huang, Kaibin Gong, Yi Zhang, Qinyu Harbin Inst Technol Guangdong Prov Key Lab Aerosp Commun & Networking Shenzhen 518055 Peoples R China Shenzhen Res Inst Big Data Shenzhen 518172 Peoples R China Peng Cheng Lab Shenzhen 518066 Peoples R China Pazhou Lab Huangpu Guangzhou 510700 Guangdong Peoples R China Southern Univ Sci & Technol Dept Elect & Elect Engn Shenzhen 518055 Peoples R China Univ Hong Kong Dept Elect & Elect Engn Hong Kong Peoples R China

The unprecedented growth of industrial Internet of Things applications requires the evolution of wireless networked control system (WNCS). WNCSs are becoming the fundamental infrastructure technologies for critical wireless control applications due to the main benefits of the reduced deployment and maintenance cost, as well as the enhanced flexibility and safety. However, independent designs between communication and control without considering their tight interaction in conventional WNCS lead to poor overall system performance and efficiency. Co-designs are expected to achieve the target control performance while improving the wireless resource efficiency. In this paper, by considering how to allocate wireless resource while guaranteeing control performance, a co-design framework is established based on the finite-time wireless system identification (WSI) - a fundamental problem in systems theory and intelligent control. To this end, two design problems are investigated aiming at maximizing the communication throughput or minimizing the power consumption while guaranteeing the WSI performance. In the former design, the joint optimization of power and channel allocations leads to a non-convex integer combinatorial problem, which is iteratively solved by optimizing the power allocation via Lagrangian method and obtaining the optimal channel allocation via Hungarian algorithm. The minimum number of data samples for guaranteeing the WSI accuracy under confidence level is further derived by exploiting the relationship between WSI accuracy and the number of state sampling processes, which leads to the maximum throughput with respect to both the communication and control processes. In the latter design for energy-efficient WSI, by exploiting the relationship between the power consumption and channel allocation given the WSI performance requirement, the optimization problem can be simplified and solved by Hungarian algorithm. Simulations are conducted to verify the perform

关键词： Wireless communication Process control Throughput Wireless sensor networks Resource management Power demand Performance evaluation Industrial Internet of Things wireless networked control system wireless system identification wireless communication and control co-design linear dynamical system

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A partial Pade-via-Lanczos method for reduced-order modeling

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linear ALGEBRA AND ITS APPLICATIONS 2001年第1期332卷 139-164页

作者： Bai, ZJ Freund, RW Bell Labs Lucent Technol Murray Hill NJ 07974 USA Univ Calif Davis Dept Comp Sci Davis CA 95616 USA

The classical Lanczos process can be used to efficiently generate Fade approximants of the transfer function of a given single-input single-output time-invariant linear dynamical system. Unfortunately, in general, the resulting reduced-order models based on Pade approximation do not preserve the stability, and possibly passivity, of the original linear dynamical system. In this paper, we describe the use of partial Pade approximation for reduced-order modeling. Partial Pade approximants have a number of prescribed poles and zeros, while the remaining degrees of freedom are used to match the Taylor expansion of the original transfer function in as many leading coefficients as possible. We present an algorithm for computing partial Pade approximants via suitable rank-1 updates of the tridiagonal matrices generated by the Lanczos process. Numerical results for two circuit examples are reported. (C) 2001 Elsevier Science Inc. All rights reserved.

关键词： Lanczos algorithm linear dynamical system transfer function stability passivity partial Pade approximation VLSI circuit simulation

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Piecewise linear dynamical Model for Action Clustering from Real-World Deployments of Inertial Body Sensors

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IEEE TRANSACTIONS ON AFFECTIVE COMPUTING 2016年第3期7卷 231-242页

作者： Gong, Jiaqi Asare, Philip Qi, Yanjun Lach, John Univ Virginia UVA Ctr Wireless Hlth Dept Elect & Comp Engn Charlottesville VA 22904 USA Bucknell Univ Dept Elect & Comp Engn Lewisburg PA 17837 USA Univ Virginia UVA Ctr Wireless Hlth Dept Comp Sci Charlottesville VA 22904 USA

Human motion has been reported as having great relevance to various disease, disorder, injuries and emotional state. Therefore, motion assessment using inertial body sensor networks (BSNs) is gaining popularity as an outcome measure in clinical study and neuroscience research. The efficacy of motion assessment heavily relies on the accurate temporal clustering of human motion into actions on various time scales. However, two human factors in real-world deployments of inertial BSNs make such motion assessment challenging: mounting errors (where sensor displacement and orientation do not match what is assumed by processing algorithms) and insecure mounting (where sensors are loosely worn causing them to shake during operations). In order to enhance the robustness of human actions clutsering from real-world BSN data, this work leverages dynamical systems modeling with the considerations of human factors. By proposing a computational body-model framework called the piecewise linear dynamical model (PLDM), we derive a robust method to segment time series data of inertial BSNs in real-world deployment with human factors into motion primitives and actions. We test the proposed method on three different inertial BSN datasets, extract actions on different temporal scales and recognize the actions into clusters. The experimental results demonstrate the effectiveness of our approach.

关键词： Time series clustering human motion analysis inertial body sensors linear dynamical system

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Identification of a dynamical system with input nonlinearity

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IEE PROCEEDINGS-CONTROL THEORY AND APPLICATIONS 1999年第1期146卷 41-51页

作者： Sun, L Liu, W Sano, A Keio Univ Dept Elect Engn Kohoku Ku Yokohama 2238522 Japan Xian Jiaotong Univ Dept Automat Control Engn Xian 710049 Shaanxi Peoples R China

A novel identification approach based on an input-holding scheme is proposed for a class of nonlinear system which consists of a nonlinear element followed by a linear dynamical system. By only using an observed system output where the test input signal is held during a multiple of the output sampling interval, the parameters of the transfer function of the linear part can be identified first. Then the intermediate input between the nonlinear part and the linear part is reconstructed by using the estimated parameters of the linear part. Finally, the nonlinear element is identified by using the test signal and the reconstructed intermediate input. It is clarified that the consistency of the parameter estimates of the linear part is assured under some specified assumptions. The effectiveness of the proposed algorithm is examined in identification of a Hammerstein system which includes an unknown continuous and/or discontinuous nonlinearity such as a backlash.

关键词： observed system output dynamical system input nonlinearity output sampling interval nonlinear dynamical systems continuous nonlinearity linear dynamical system transfer function parameters Simulation, modelling and identification Hammerstein system identification discontinuous nonlinearity input-holding scheme transfer functions parameter estimation backlash reconstructed intermediate input

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Clinical time series prediction: Toward a hierarchical dynamical system framework

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2015年第1期65卷 5-18页

作者： Liu, Zitao Hauskrecht, Milos Univ Pittsburgh Dept Comp Sci Pittsburgh PA 15260 USA

Objective: Developing machine learning and data mining algorithms for building temporal models of clinical time series is important for understanding of the patient condition, the dynamics of a disease, effect of various patient management interventions and clinical decision making. In this work, we propose and develop a novel hierarchical framework for modeling clinical time series data of varied length and with irregularly sampled observations. Materials and methods: Our hierarchical dynamical system framework for modeling clinical time series combines advantages of the two temporal modeling approaches: the linear dynamical system and the Gaussian process. We model the irregularly sampled clinical time series by using multiple Gaussian process sequences in the lower level of our hierarchical framework and capture the transitions between Gaussian processes by utilizing the linear dynamical system. The experiments are conducted on the complete blood count (CBC) panel data of 1000 post-surgical cardiac patients during their hospitalization. Our framework is evaluated and compared to multiple baseline approaches in terms of the mean absolute prediction error and the absolute percentage error. Results: We tested our framework by first learning the time series model from data for the patients in the training set, and then using it to predict future time series values for the patients in the test set. We show that our model outperforms multiple existing models in terms of its predictive accuracy. Our method achieved a 3.13% average prediction accuracy improvement on ten CBC lab time series when it was compared against the best performing baseline. A 5.25% average accuracy improvement was observed when only short-term predictions were considered. Conclusion: A new hierarchical dynamical system framework that lets us model irregularly sampled time series data is a promising new direction for modeling clinical time series and for improving their predictive performance. (C)

关键词： Gaussian processes linear dynamical system Hierarchical framework Clinical time series prediction

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Second order global uniformly convergent numerical method for a coupled system of singularly perturbed initial value problems

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APPLIED MATHEMATICS AND COMPUTATION 2012年第8期219卷 3740-3753页

作者： Rao, S. Chandra Sekhara Kumar, Sunil Indian Inst Technol Delhi Dept Math New Delhi 110016 India Indian Inst Sci Supercomp Educ & Res Ctr Bangalore 560012 Karnataka India

We consider a coupled system of singularly perturbed first order ordinary differential equations. The equations have small positive parameters of different magnitudes associated with them. A decomposition of the exact solution into smooth and singular components is constructed that is useful for the analysis of the numerical method. We apply the HODIE (High Order Difference approximation with Identity Expansions) technique to construct a second order finite difference scheme and combine this with standard backward Euler difference scheme in a special way on a piecewise-uniform Shishkin mesh to solve the system numerically. It is proved that the method is second order (up to a logarithmic factor) convergent in the maximum norm uniformly in both perturbation parameters. Numerical experiments are conducted to support the theoretical results. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Singular perturbation Coupled system Uniform convergence High order linear dynamical system

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Krylov-subspace methods for reduced-order modeling in circuit simulation

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JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2000年第1-2期123卷 395-421页

作者： Freund, RW Bell Labs Lucent Technol Murray Hill NJ 07974 USA

The simulation of electronic circuits involves the numerical solution of very large-scale, sparse, in general nonlinear, systems of differential-algebraic equations. Often, the size of these systems can be reduced considerably by replacing the equations corresponding to linear subcircuits by approximate models of much smaller state-space dimension. In this paper, we describe the use of Krylov-subspace methods for generating such reduced-order models of linear subcircuits. Particular emphasis is on reduced-order modeling techniques that preserve the passivity of linear RLC subcircuits. (C) 2000 Elsevier Science B.V. All rights reserved.

关键词： Lanczos algorithm Arnoldi process linear dynamical system VLSI interconnect transfer function Pade approximation stability passivity positive real function

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Exact Asymptotics for linear Quadratic Adaptive Control

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JOURNAL OF MACHINE LEARNING RESEARCH 2021年第1期22卷 1-112页

作者： Wang, Feicheng Janson, Lucas Harvard Univ Dept Stat Cambridge MA 02138 USA

Recent progress in reinforcement learning has led to remarkable performance in a range of applications, but its deployment in high-stakes settings remains quite rare. One reason is a limited understanding of the behavior of reinforcement algorithms, both in terms of their regret and their ability to learn the underlying system dynamics existing work is focused almost exclusively on characterizing rates, with little attention paid to the constants multiplying those rates that can be critically important in practice. To start to address this challenge, we study perhaps the simplest non-bandit reinforcement learning problem: linear quadratic adaptive control (LQAC) . By carefully combining recent finite-sample performance bounds for the LQAC problem with a particular (less-recent) martingale central limit theorem, we are able to derive asymptotically- exact expressions for the regret, estimation error, and prediction error of a rate-optimal stepwise-updating LQAC algorithm. In simulations on both stable and unstable systems, we find that our asymptotic theory also describes the algorithm's finite-sample behavior remarkably well.

关键词： reinforcement learning adaptive control linear dynamical system system identification safety uncertainty quantification exact asymptotics

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Robust Bayesian Analysis of Early-Stage Parkinson's Disease Progression Using DaTscan Images

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2021年第2期40卷 549-561页

作者： Zhou, Yuan Tinaz, Sule Tagare, Hemant D. Yale Univ Dept Radiol & Biomed Imaging New Haven CT 06520 USA Yale Univ Dept Neurol New Haven CT 06520 USA Yale Univ Dept Biomed Engn New Haven CT 06520 USA Yale Univ Dept Stat & Data Sci New Haven CT 06520 USA

This paper proposes a mixture of linear dynamical systems model for quantifying the heterogeneous progress of Parkinson's disease from DaTscan Images. The model is fitted to longitudinal DaTscans from the Parkinson's Progression Marker Initiative. Fitting is accomplished using robust Bayesian inference with collapsed Gibbs sampling. Bayesian inference reveals three image-based progression subtypes which differ in progression speeds as well as progression trajectories. The model reveals characteristic spatial progression patterns in the brain, each pattern associated with a time constant. These patterns can serve as disease progression markers. The subtypes also have different progression rates of clinical symptoms measured by MDS-UPDRS Part III scores.

关键词： Parkinson's disease disease progression model DaTscans linear dynamical system centrosymmetric matrix t-distribution

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A quiver approach to studying orbit spaces of linear systems

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systemS & CONTROL LETTERS 2014年 73卷 1-5页

作者： Stupariu, Mihai-Sorin Univ Bucharest Fac Math & Comp Sci Bucharest 010014 Romania

Orbit spaces associated to linear actions are of particular interest in control theory. Their geometrical properties can be naturally investigated by using the representations of quivers as an abstract framework. The aim of the paper is to bring into attention an application of this approach and to show how the use of quivers makes it easy handling concepts arising in control theory. Specifically, the natural duality between controllable and observable systems, as well as the construction of compactifications for the associated orbit spaces is interpreted in terms of opposite quivers. (C) 2014 Elsevier B.V. All rights reserved.

关键词： linear dynamical system Quiver representation Stability Compactification

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