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Simple-Structured Quaternion Estimator Separating Inertial and Magnetic Sensor Effects

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2019年第6期55卷 2698-2706页

作者： Suh, Young Soo Univ Ulsan Dept Elect Engn Ulsan 44610 South Korea

This paper presents a new filter estimating quaternion using inertial and magnetic sensors. Using a reference coordinate system multiplicative quaternion error representation and a constrained structure filter gain, the proposed filter has a separation property, where the magnetic sensor output does not affect pitch and roll angle estimation. Furthermore, the proposed filter gain can be computed just from five scalar equations. Through simulation, the separation property of the proposed filter is verified.

关键词： Quaternions Magnetic sensors estimation Magnetic separation Accelerometers Kalman filters Attitude estimation algorithms inertial sensors Kalman filters

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Modeling and identification of uncertain-input systems

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AUTOMATICA 2019年 105卷 130-141页

作者： Risuleo, Riccardo Sven Bottegal, Giulio Hjalmarsson, Hakan KTH Royal Inst Technol ACCESS Linnaeus Ctr Stockholm Sweden Eindhoven Univ Technol Dept Elect Engn Eindhoven Netherlands

We present a new class of models, called uncertain-input models, that allows us to treat system identification problems in which a linear system is subject to a partially unknown input signal. To encode prior information about the input or the linear system, we use Gaussian-process models. We estimate the model from data using the empirical Bayes approach: the hyperparameters that characterize the Gaussian-process models are estimated from the marginal likelihood of the data. We propose an iterative algorithm to find the hyperparameters that relies on the EM method and results in decoupled update steps. Because in the uncertain-input setting neither the marginal likelihood nor the posterior distribution of the unknowns is tractable, we develop an approximation approach based on variational Bayes. As part of the contribution of the paper, we show that this model structure encompasses many classical problems in system identification such as Hammerstein models, blind system identification, and cascaded linear systems. This connection allows us to build a systematic procedure that applies effectively to all the aforementioned problems, as shown in the numerical simulations presented in the paper. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： System identification estimation algorithms Nonlinear models Nonparametric identification Gaussian processes

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Non-linear control of speed and air path in a diesel engine with turbocharger and exhaust gas recirculation

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REVISTA IBEROAMERICANA DE AUTOMATICA E INFORMATICA INDUSTRIAL 2019年第4期16卷 403-414页

作者： Luis Mendoza-Soto, Jose Alvarez-Icaza, Luis Rodriguez Cortes, H. Univ Nacl Autonoma Mexico Fac Ingn Av Univ 3000 Mexico City 04510 DF Mexico Univ Nacl Autonoma Mexico Inst Ingn Ciudad De Mexico 04510 Mexico Inst Politecn Nacl Ctr Invest & Estudios Avanzados Av Inst Politecn Nacl 2508 Ciudad De Mexico 07360 Mexico

A non-linear control to regulate the speed and the supply of air flow in an internal combustion Diesel engine with exhaust gas recirculation system is proposed. The control scheme uses a static feedback of the states to linearize the motor-turbocharger system avoiding linearization by dynamic state feedback. The control scheme used is complemented by an estimator for the load torque of the engine based on the Immersion and Invariance technique. The stability analysis allows to conclude asymptotic stability when the control scheme uses the estimated load torque. Through a series of numerical simulations the properties of the proposed control scheme are evaluated.

关键词： Diesel engines estimation algorithms Feedback control Speed control Exhaust gas recirculation

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SVD-based factored-form Cubature Kalman Filtering for continuous-time stochastic systems with discrete measurements

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AUTOMATICA 2020年 120卷 109110-109110页

作者： Kulikova, Maria, V Kulikov, Gennady Yu Univ Lisbon Inst Super Tecn CEMAT Av Rovisco Pais 1 P-1049001 Lisbon Portugal

In this paper, a singular value decomposition (SVD) approach is developed for implementing the cubature Kalman filter. The discussed estimator is one of the most popular and widely used method for solving nonlinear Bayesian filtering problem in practice. To improve its numerical stability (with respect to roundoff errors) and practical reliability of computations, the SVD-based methodology recently proposed for the classical Kalman filter is generalized on the nonlinear filtering problem. More precisely, we suggest the SVD-based solution for the continuous-discrete cubature Kalman filter and design two estimators: (i) the filter based on the traditionally used Euler-Maruyama discretization scheme;(ii) the estimator based on advanced Ito-Taylor expansion for discretizing the underlying stochastic differential equations. Both estimators are formulated in terms of SVD factors of the filter error covariance matrix and belong to the class of stable factored-form (square-root) algorithms. The new methods are tested on a radar tracking problem. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Stochastic systems estimation algorithms Kalman filters Nonlinear filters

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Sparse linear regression from perturbed data

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AUTOMATICA 2020年 122卷 109284-109284页

作者： Fosson, Sophie M. Cerone, Vito Regruto, Diego Politecn Torino Dipartimento Automat & Informat Corso Duca Abruzzi 24 I-10129 Turin Italy

The problem of sparse linear regression is relevant in the context of linear system identification from large datasets. When data are collected from real-world experiments, measurements are always affected by perturbations or low-precision representations. However, the problem of sparse linear regression from fully-perturbed data is scarcely studied in the literature, due to its mathematical complexity. In this paper, we show that, by assuming bounded perturbations, this problem can be tackled by solving low-complex l(2) and l(1) minimization problems. Both theoretical guarantees and numerical results are illustrated. (c) 2020 Elsevier Ltd. All rights reserved.

关键词： Linear systems Compressed sensing Perturbed data estimation algorithms Non-convex optimization System identification

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Mixed-Initiative Control Synthesis: Estimating an Unknown Task Based on Human Control Input ⁎

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IFAC-PapersOnLine 2020年第5期53卷 119-125页

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

In this paper we consider a mobile platform controlled by two entities; an autonomous agent and a human user. The human aims for the mobile platform to complete a task, which we will denote as the human task, and will impose a control input accordingly, while not being aware of any other tasks the system should or must execute. The autonomous agent will in turn plan its control input taking in consideration all safety requirements which must be met, some task which should be completed as much as possible (denoted as the robot task), as well as what it believes the human task is based on previous human control input. A framework for the autonomous agent and a mixed initiative controller are designed to guarantee the satisfaction of the safety requirements while both the human and robot tasks are violated as little as possible. The framework includes an estimation algorithm of the human task which will improve with each cycle, eventually converging to a task which is similar to the actual human task. Hence, the autonomous agent will eventually be able to find the optimal plan considering all tasks and the human will have no need to interfere again. The process is illustrated with a simulated example.

关键词： Temporal Logic Human-Machine Interface Adaptive Control estimation algorithms Formal Methods

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Maneuver Classification for Road Vehicles with Constrained Filtering Techniques ⁎

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IFAC-PapersOnLine 2020年第2期53卷 15495-15500页

作者： Olivér Törö Tamás Bécsi Szilárd Aradi Máté Kolat Péter Gáspár Department of Control for Transportation and Vehicle Systems Budapest University of Technology and Economics Systems and Control Laboratory Computer and Automation Research Institute Hungarian Academy of Sciences

Environment perception and situation awareness are keystones for autonomous road vehicles. The problem of maneuver classification for road vehicles in the context of multi-model state estimation under model uncertainty is addressed in this paper. The conventional approach is to define different motion models that match the desired type of movements. In this work we used a single motion model as a starting point and applied constraints to construct such filters that are fine tuned for the predefined maneuvers. The estimation is carried out in the interacting multiple model framework, where the elemental filters are constrained Kalman filters. To capture the characteristics of the considered maneuvers linear equality and non-equality state constraints were used. The performance of the proposed method is demonstrated in a simulation environment participating an observer and a maneuvering vehicle.

关键词： Manoeuvring target Road traffic Constraints estimation algorithms Simulation Filtering

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Dynamic model for a water distribution network: application to leak diagnosis and quality monitoring

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IFAC-PapersOnLine 2020年第2期53卷 16679-16684页

作者： J.A. Delgado-Aguiñaga F.I. Becerra-López L. Torres G. Besançon V. Puig M. R. Jiménez Magaña Universidad del Valle de México CIIDETEC Universidad de Guadalajara CUCEI Instituto de Ingeniería UNAM Univ. Grenoble Alpes CNRS Grenoble INP GIPSA-lab Institut de Robòtica i Informàtica Industrial CSIC-UPC Facultad de Estudios Superiores Aragón UNAM

This paper presents a model based on the rigid water column (RWC) theory to describe the flow and the decay of chlorine in water distribution networks (WDNs), which can be used for developing tools to diagnose leaks and estimate chlorine concentrations. The model includes the continuity equation for each node of the network such that i) the relation of the flow rates entering and leaving the nodes is explicit, and ii) the computation of pressures and flow rates can be simultaneously done. The chlorine decay in each node and in each pipeline section of the WDN is predicted from the computed flow rates by using the third order accurate Warming-Kutler-Lomax (WKL) method. At the end of this paper, it is shown that the chlorine decay rate is well predicted by using the WKL method according to a comparison with simulations results obtained by using the EPANET-MSX software. Furthermore, it is shown that several single leak-diagnosis scenarios can be successfully solved by using an improved sensitivity matrix method together with the proposed model.

关键词： Fault diagnosis Water pollution Networks Pipelines estimation algorithms

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Identification of Heterogeneous Parameters in an Intracellular Reaction Network from Population Snapshot Measurements through Sensitivity Analysis and Neural Network 8th

Identification of Heterogeneous Parameters in an Intracellul...

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8th Conference on Foundations of Systems Biology in Engineering (FOBSE)

作者： Lee, Dongheon Jayaraman, Arul Kwon, Joseph S. Texas A&M Univ Artie McFerrin Dept Chem Engn College Stn TX 77840 USA Texas A&M Univ Texas A&M Energy Inst College Stn TX 77840 USA Texas A&M Univ Dept Biomed Engn College Stn TX 77840 USA

Cells in a clonal cell-population exhibit a significant degree of heterogeneity in their responses to an external stimulus. In order to model a heterogeneous intracellular process, the individual-based population model (IBPM) has been developed in the past. Specifically, the IBPM approach can represent the heterogeneous dynamics in a cell population with a system of differential equations, whose model parameters follow probability density functions (PDF) instead of being constants. Therefore, in order to accurately predict the heterogeneous cellular dynamics, it is important to infer the PDFs of the model parameters from available experimental measurements. In this study, we propose a methodology to estimate the PDFs of the model parameters from population snapshot measurements obtained from flow cytometry. First, the PDFs of the model parameters are assumed to be normal so that a finite dimensional vector will be inferred from the measurements instead of inferring PDFs. Second, the sensitivity analysis is performed to identify which PDFs of the model parameters are identifiable and should be estimated from the available measurements. Next, in order to reduce the excessive number of evaluations of the IBPM during the PDF estimation process, an NNM is developed so that the output PDFs can be computed for given parameter PDFs. Lastly, the NNM is used to estimate the PDFs of the model parameters by minimizing the difference between the measured and predicted PDFs of the output. To show the effectiveness of the proposed methodology, the PDFs of parameters of a TNF alpha signaling model were estimated from in silico measurements. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： estimation algorithms Stochastic systems Sensitivity analysis Identifiability

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A Kalman-filtering derivation of simultaneous input and state estimation

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AUTOMATICA 2019年 108卷 108478-000页

作者： Bitmead, Robert R. Hovd, Morten Abooshahab, Mohammad Ali Univ Calif San Diego Dept Mech & Aerosp Engn 9500 Gilman DrMS-0411 La Jolla CA 92093 USA Norwegian Univ Sci & Technol Dept Engn Cybernet N-7491 Trondheim Norway

Simultaneous input and state estimation algorithms are studied as particular limits of Kalman filtering problems. This admits interpretation of the algorithm properties and critical analysis of their claims to being partly model-free and to providing unbiased estimates. A disturbance model, white noise of unbounded variance, is provided and the bias feature is shown to be a geometric projection property rather than probabilistic in nature. As a consequence of this analysis, the algorithm is connected, in the stationary case, to Algebraic Riccati equation computations for the gains, estimate covariances and filter frequency response. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： State estimation Input estimation Kalman filters estimation algorithms

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