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arXiv 2022年

作者： Ju, Yue Mu, Biqiang Ljung, Lennart Chen, Tianshi The School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen518172 China Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and System Science Chinese Academy of Sciences Beijing100190 China The Division of Automatic Control Department of Electrical Engineering Linköping University LinköpingSE-58183 Sweden

Regularized techniques, also named as kernel-based techniques, are the major advances in system identification in the last decade. Although many promising results have been achieved, their theoretical analysis is far from complete and there are still many key problems to be solved. One of them is the asymptotic theory, which is about convergence properties of the model estimators as the sample size goes to infinity. The existing related results for regularized system identification are about the almost sure convergence of various hyper-parameter estimators. A common problem of those results is that they do not contain information on the factors that affect the convergence properties of those hyper-parameter estimators, e.g., the regression matrix. In this paper, we tackle problems of this kind for the regularized finite impulse response model estimation with the empirical Bayes (EB) hyper-parameter estimator and filtered white noise input. In order to expose and find those factors, we study the convergence in distribution of the EB hyper-parameter estimator, and the asymptotic distribution of its corresponding model estimator. For illustration, we run Monte Carlo simulations to show the efficacy of our obtained theoretical results. Copyright © 2022, The Authors. All rights reserved.

关键词： Monte Carlo methods

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Tutorial on Asymptotic Properties of Regularized Least Squares Estimator for Finite Impulse Response Model

arXiv

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arXiv 2021年

作者： Ju, Yue Chen, Tianshi Mu, Biqiang Ljung, Lennart The School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen518172 China Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and System Science Chinese Academy of Sciences Beijing100190 China The Division of Automatic Control Department of Electrical Engineering Linköping University LinköpingSE-58183 Sweden

In this paper, we give a tutorial on asymptotic properties of the Least Square (LS) and Regularized Least Squares (RLS) estimators for the finite impulse response model with filtered white noise inputs. We provide three perspectives: the almost sure convergence, the convergence in distribution and the boundedness in probability. On one hand, these properties deepen our understanding of the LS and RLS estimators. On the other hand, we can use them as tools to investigate asymptotic properties of other estimators, such as various hyper-parameter estimators. © 2021, CC BY.

关键词： Probability distributions

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Annual business and news:: Beginning the 31st year of the Journal of Guidance, control, and Dynamics

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JOURNAL OF GUIDANCE control AND DYNAMICS 2008年第1期31卷 1-1页

作者： Schmidt, George T. Aeronautics and Astronautics Massachusetts Institute of Technology (MIT) AIAA Institute of Electrical and Electronics Engineers Russian Federation Academy of Navigation and Motion Control Department of Mechanical and Aerospace Engineering and Engineering Mechanics University of Missouri-Rolla(UMR) Lockheed Electronics Company Center for Space Research University of Texas Austin United States Wright Laboratory Eglin Air Force Base UMR AIAA Guidance Navigation and Control Technical Committee American Automatic Control Council Lockheed Martin Aeronautics Company Palmdale CA United States C4ISR and Unmanned Air Vehicle (UAV) Programs for Air Vehicle Sciences and Systems Accreditation Board Engineering and Technology Inc./Aeronautical National Technical Committee on Guidance Navigation and Control Department of Mechanical and Aerospace Engineering University at Buffalo State University of New York (SUNY) AIAA Technical Committee on GN and C Air Vehicles Directorate Air Force Research Laboratory (AFRL) Wright-Patterson Air Force Base Space Access and Hypersonic Vehicle Guidance and Control Group Control Science Center of Excellence AFRL AIAA Technical Committee on Guidance Navigation and Control Department of Mechanical and Aeronautical Engineering University of California (UC) Davis United States Boeing Company Modernized Flight Control System for the Apache Longbow AIAA Institute of Electrical and Electronics Engineers AHS Jet Propulsion Laboratory California Institute of Technology Boeing Company Flight Sciences and Advanced Design Group Guidance Navigation and Control (GN and C) Systems Engineer Distributed Space Systems NASA Goddard Space Flight Center (GSFC) AIAA GN and C Technical Committee Department of Aerospace Engineering and Engineering Mechanics University of Texas Austin United States Institute of Navigation American Astronautical Society AIAA Guidance Navigation (GN and C) and Control Technical Committee Department of Aerospace Engineering Iowa State University Iowa

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An error analysis of GPS compass

An error analysis of GPS compass

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SICE Annual Conference

作者： Chansik Park Ilsun Kim Gyu-In Jee Jang Gyu Lee Application System Development Team Network Division Samsung Electronics Seoul South Korea Multimedia Laboratory. Corporate Technical Operations Samsung Electronics Company Limited Suwon South Korea Department of Electronics Engineering Konkuk University Seoul South Korea Automatic Control Research Center School of Electrical Engineering Seoul National University Seoul South Korea

ISBN: (纸本)0780342089

In this paper, analytical error characteristics of GPS compass which determines heading and elevation of the vehicle using carrier phase measurements from two antennas is given. The derived result gives clear analysis of error statistics on attitude determination using GPS carrier phase measurement. It shows the accuracy of measured attitude relies on satellite geometry, performance of receiver, baseline length, baseline configuration and nominal attitude of vehicle. It is also shown that to obtain more precise heading, longer baseline along the vehicle is preferred and the accuracy of heading is always better than that of elevation since the vertical measurement uncertainty caused by geometry of satellites affects only on that of elevation. These results can be applied to basic directions for attitude determination: to what direction should the baseline be located to minimize the error, which satellites should be selected to minimize the error. These must be important and useful pre-information in attitude determination problem. An application to real data shows that the predicted error by the proposed method coincides with that of experiments.

关键词： Error analysis Global Positioning system Vehicles Position measurement Satellites Phase measurement Geometry Antenna measurements Length measurement Measurement uncertainty

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A Hierarchical Architecture for the Coordination of an Ensemble of Steam Generators

arXiv

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arXiv 2020年

作者： Spinelli, Stefano Longoni, Elia Farina, Marcello Petzke, Felix Streif, Stefan Ballarino, Andrea Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Italy Istituto di Sistemi e Tecnologie Industriali Intelligenti per il Manifatturiero Avanzato Consiglio Nazionale delle Ricerche Italy Department of Electrical Engineering and Information Technology Technische Universita't Chemnitz Automatic Control and System Dynamics Lab Germany

This work presents a hierarchical architecture for the optimal management of an ensemble of steam generators, which needs to jointly sustain a common load. The coordination of independent subsystems is provided by a multi—layer control scheme. A high—level optimizer computes the optimal shares of production to be allocated to single generators. At medium level, a robust tube—based model predictive control (MPC) is proposed to track the time—varying demand of the ensemble using a centralized, but aggregated model, whose order does not scale with the number of subsystems. At low level, decentralized controllers are in place to stabilize the internal boiler pressure. The control architecture enables the dynamic modification of the ensemble configuration and plug and play operations. Simulation results are reported to demonstrate the potentialities of the proposed approach. Copyright © 2020, The Authors. All rights reserved.

关键词： Model predictive control

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A new force profile signal for a convex solution of muscle force estimation from electromyographic signals*

A new force profile signal for a convex solution of muscle f...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Mehdi Shirzadi Arezoo Mirshamsi Alp Eşrefoğlu Monica Rojas-Martínez Hamid Reza Marateb Miquel Angel Mañanas Taian Vieira Department of Automatic Control (ESAII) Biomedical Engineering Research Centre (CREB) Universitat Politèncicna de Catalunya (UPC) Barcelona Spain Biomedical Engineering Department Engineering Faculty University of Isfahan Isfahan Iran Laboratory of Applied Biology & Research Unit in Applied Neurophysiology (LABNeuro) – Université Libre de Bruxelles Brussels Belgium CIBER of Bioengineering Biomaterilas and Nanomedicine (CIBER-BBN) Madrid Spain Department of Electronics and Telecommunication Laboratory for Engineering of the Neuromuscular System (LISiN) PolitoBIOMed Lab – Politecnico di Torino Turin Italy

High-Density Surface Electromyography (HD-sEMG) is a non-invasive technique for measuring the electrical activity of a muscle with multiple, closely spaced electrodes. Estimation of muscle force is one of the applications of HD-sEMG. Usually, validating different EMG-Force models entails simple movements limited to laboratory settings. The validity of these models in more ecological conditions, requesting force production over a wide frequency band, remains unknown. In this study, we, therefore, compare the results of force prediction using four different types of input force profiles that can be representative of daily life activities, and we investigate whether the crest factor of these different input signals affects force prediction. For predicting the force from sEMG signals, we used our real-time and convex methods. HD-sEMG signals were recorded with 144 channels from the biceps brachii, brachioradialis, and triceps (long, lateral, and medial head) muscles of 24 healthy subjects during random signal, random phase, Schroeder phase, and minimum crest factor (crestmin) signal. The correlation and coefficient of determination (R 2 ) between measured and predicted forces were calculated for the different force feedback profiles. The crestmin signal showed significantly better results based on statistical tests (P-value < 0.05), with correlation and R 2 equal to 0.92±0.03 and 0.86±0.05, respectively. The results demonstrate that the crest factor of input signals is a crucial parameter that can impact the performance of EMG-Force models and must be considered during *** Relevance— This study demonstrates that lower crest factor multisine force profiles result in improved fitness for force prediction and can be used as an alternative to random signals.

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Observability and Identifiability Analyses of Process Models for Agricultural Anaerobic Digestion Plants

Observability and Identifiability Analyses of Process Models...

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International Conference on Process control (PC)

作者： Simon Hellmann Arne-Jens Hempel Stefan Streif Sören Weinrich Biochemical Conversion Department DBFZ Deutsches Biomasseforschungszentrum gGmbH Leipzig Germany Saxon University of Cooperative Education Glauchau Germany Chair of Automatic Control and System Dynamics Chemnitz University of Technology Chemnitz Germany Faculty of Energy. Building Services. Environmental Engineering Munster University of Applied Sciences Münster Germany

Dynamic operation of anaerobic digestion plants requires advanced process monitoring and control. Different simplifications of the Anaerobic Digestion Model No. 1 (ADM1) have been proposed recently, which appear promising for model-based process automation and state estimation. As a fundamental requirement, observability and identifiability of these models are analyzed in this work, which was pursued through algebraic and geometric analysis. Manual algebraic assessment was successful for small models such as the ADM1-R4 and simplified versions of the ADM1-R3, which were derived in this context. However, for larger model classes the algebraic approach showed to be insufficient. By contrast, the geometric approach, implemented in the STRIKE GOLDD toolbox, allowed to show observability for more complex models (including ADM1-R4 and ADM1-R3), employing two independent algorithms. The present study lays the groundwork for state observer design, parameter estimation and advanced control resting upon ADM1-based models.

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Supplementary material for CDC submission no. 1461

arXiv

引用

arXiv 2020年

作者： Ju, Yue Chen, Tianshi Mu, Biqiang Ljung, Lennart Chinese University of Hong Kong Shenzhen518172 Hong Kong School of Science and Engineering Shenzhen Research Institute of Big Data Chinese University of Hong Kong Shenzhen518172 Hong Kong Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and System Science Chinese Academy of Sciences Beijing100190 China Division of Automatic Control Department of Electrical Engineering Linköping University LinköpingSE-58183 Sweden

In this paper, we study the influence of ill-conditioned regression matrix on two hyper-parameter estimation methods for the kernel-based regularization method: the empirical Bayes (EB) and the Stein’s unbiased risk estimator (SURE). First, we consider the convergence rate of the cost functions of EB and SURE, and we find that they have the same convergence rate but the influence of the ill-conditioned regression matrix on the scale factor are different: the scale factor for SURE contains one more factor cond(ΦTΦ) than that of EB, where Φ is the regression matrix and cond(·) denotes the condition number of a matrix. This finding indicates that when Φ is ill-conditioned, i.e., cond(ΦTΦ) is large, the cost function of SURE converges slower than that of EB. Then we consider the convergence rate of the optimal hyper-parameters of EB and SURE, and we find that they are both asymptotically normally distributed and have the same convergence rate, but the influence of the ill-conditioned regression matrix on the scale factor are different. In particular, for the ridge regression case, we show that the optimal hyper-parameter of SURE converges slower than that of EB with a factor of 1/n2, as cond(ΦTΦ) goes to ∞, where n is the FIR model order. Copyright © 2020, The Authors. All rights reserved.

关键词： Cost functions

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Data-Driven Modelling and Model Predictive control Design of a Nonlinear Auger-Based Pyrolysis Reactor

SSRN

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SSRN 2024年

作者： Adiprabowo, Arya Bhaskara Muharto, Bambang Prabowo, Wargiantoro Saputro, Frendy Rian Anggoro, Trisno Masfuri, Imron Sugeng, Dhani Avianto Rosyadi, Erlan Lang, Zi-Qiang KST BJ Habibie Energy Building 620-625 Indonesia Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS1 3JD United Kingdom Department of Electrical Engineering Mathematics and Computer Science University of Twente Enschede7522 NB Netherlands Vehicle System Engineering Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia Kuala Lumpur54100 Malaysia Department of Chemical and Environmental Engineering Malaysia Japan International Institute of Technology Universiti Teknologi Malaysia Kuala Lumpur54100 Malaysia

Pyrolysis is a prominent technique to harvest energy while addressing the global waste problem. Product yield relies heavily on precise reactor temperature control in a pyrolysis process;thus, a representative mathematical model is necessary to design and evaluate the control system. Hence, this research aims to create a data-driven model of a 1 kg/h Auger-based pyrolysis (APR) plant and design a robust controller accordingly. The system is excited by using a pseudo-random binary signal (PRBS) to trigger process response and is recorded for 2 hours. Various models with different orders, such as Auto Regressive Exogeneous (ARX), Auto Regressive Moving Average Exogeneous (ARMAX), Box-Jenkins (BJ), and Output Error (OE) were created from the obtained data. The models were examined using best-fit percentage, final prediction error (FPE) and mean square error (MSE) criteria and validated using cross-correlation and auto-correlation functions. Two widely used and purpose-fit controllers, Proportional-Integral-Derivate (PID) and Model Predictive control (MPC), with output constraints, were designed and tuned for this application. Several operational scenarios were investigated, such as setpoint tracking and disturbance rejection. The best-fit models were selected and validated for TIC-3, TIC-4 and TIC-5, with 96.22%, 97.4% and 84.83% fitness criteria, respectively. Both controllers successfully satisfied all the tests with less than 2% steady-state error. However, MPC surpassed PID controllers in all aspects, such as 5-7 times faster settling time, negligible overshoot, and reduced integral absolute error (IAE) and integral square error (ISE) up to 44.75% and 5.45%, respectively. Nevertheless, MPC with input and output constraints performance pales compared to other controllers despite some adjustments with massive ISE and settling time above 3000 seconds. As a result, a recommendation to change the input actuator will be beneficial to unleash the benefit of MPC fully. © 2

关键词： Model predictive control

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Enhanced GMM-based Filtering with Measurement Update Ordering and Innovation-based Pruning

Enhanced GMM-based Filtering with Measurement Update Orderin...

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International Conference on Information Fusion

作者： Xi Li Le Yang Lyudmila Mihaylova Fucheng Guo Min Zhang School of Electronic Science National University of Defense Technology Changsha China Department of ECE University of Canterbury Christchurch New Zealand Department of Automatic Control & System Engineering University of Sheffield Sheffield UK

The Gaussian mixture model (GMM) has been extensively investigated in nonlinear/non-Gaussian filtering problems. This paper presents two enhancements for GMM-based nonlinear filtering techniques, namely, the adaptive ordering of the measurement update and normalized innovation square (NlS)-based mixture component management The first technique selects the order of measurement update by maximizing the marginal measurement likelihood to improve performance. The second approach takes the filtering history of a mixture component into account and prunes those components with NIS larger than a threshold to eliminate their impact on the filtering posterior. The advantage of the proposed enhancements is illustrated via simulations that consider source tracking using the time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements received at two unmanned aerial vehicles (UAVs). A GMM-cubature quadrature Kalman filter (CQKF) is implemented and its performances with different measurement update and mixture component management strategies are compared. The superior performance obtained via the use of the two proposed techniques is demonstrated.

关键词： Time measurement Covariance matrices Noise measurement Frequency measurement Weight measurement Merging Kalman filters

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