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Data-Driven Input-Passivity estimation Using Power Iterations

IFAC-PapersOnLine 2021年第7期54卷 619-624页

作者： Matias I. Müller Anne Koch Frank Allgöwer Cristian R. Rojas Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden Institute for Systems Theory and Automatic Control University of Stuttgart Germany

In this work we develop a non-parametric method to estimate the input-passivity index of an unknown linear and time-invariant (LTI) system from iterative experiments based on the power method from numerical linear algebra. Inspired by the power method for estimating the H∞-norm (or L 2 -gain) from data, we propose an algorithm that time-reverses input-output data in order to emulate measurements of a virtual system whose L 2 -gain matches the passivity index of the original system under study. While the proposed method requires exciting the original system twice, we also introduce an improved sampling scheme where only one experiment per iteration is needed.

关键词： Experiment design nonparametric methods identification for control data-driven learning estimation algorithms

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A neural network for robust estimation and uncertainty intervals evaluation of nonlinear models

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IFAC Proceedings Volumes 1999年第2期32卷 5141-5146页

作者： Ivan Nunes da Silva Lucia Valeria R. de Arruda Wagner Caradori do Amaral University of São Paulo - UNESP Department of Electrical Engineering CP 473 CEP 17033-360 Bauru-SP Brazil Federal Center of Education in Technology CEFET-PR/CEPGEI Curitiba-PR Brazil University of Campinas UNICAMP/FEEC/DCA CP 6101 CEP 13081-970 Campinas-SP Brazil

Artificial neural networks are dynamic systems consisting of highly interconnected and parallel nonlinear processing elements. Systems based on artificial neural networks have high computational rates due to the use of a massive number of simple processing elements and the high degree of connectivity between these elements. The ability of neural networks to realize some complex nonlinear function makes them attractive for system identification. This paper presents a novel approach to solve robust parameter estimation problems for nonlinear model with unknown-but-bounded errors and uncertainties. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the network convergence to the equilibrium points. A solution for the robust estimation problem with unknown-but-bounded error corresponds to an equilibrium point of the network. Simulation results are presented as an illustration of the proposed approach.

关键词： Neural Networks Robust estimation Artificial Intelligence Parameter Identification estimation algorithms

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Parameter estimation for a flotation process tracking simulator

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IFAC Proceedings Volumes 2013年第16期46卷 122-127页

作者： Janne Pietilä Jani Kaartinen Ari-Matti Reinsalo Aalto University Department of Automation and Systems Technology Helsinki Finland

A parameter estimation method for an online flotation process simulator is described. The applications of an online process simulator include soft sensor implementations, process trajectory predictions and advisory feedback to the operator, which have potential to improve the process efficiency and minimize the detrimental effect of disturbances on the process or the environment. The online simulator uses a detailed and dynamic model of an actual industrial flotation process, and therefore accurately corresponds to the process phenomena present at the plant. Parameter estimation is required for the flotation kinetics in order for the simulator to adapt to changes in the process conditions. A relatively generic parameter estimation algorithm is developed and tested with a dual simulator setup. The particular requirements and limitations of adapting an online simulator are discussed, and modifications to well-known estimation algorithms are presented as a possible method of meeting the requirements and overcoming the limitations. The results show that online parameter estimation and simulation model tracking is possible with the chosen method, and point out areas of further development for application when the simulator is used alongside the real process.

关键词： estimation algorithms adaptation process simulators

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State estimation in the Presence of Intermittent Actuator Faults

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IFAC-PapersOnLine 2020年第2期53卷 2403-2408页

作者： Alia R. Strandt Susan C. Schneider Edwin E. Yaz Department of Electrical and Computer Engineering Milwaukee WI 53233 USA

The problem of intermittent, random actuator faults is important in many applications, such as in networked systems, in which there may be intermittent losses of communication between the actuators and the plant. However, state estimation of such systems is rarely addressed, with the majority of the work focusing on fault-tolerant control. In this work, the Kalman filter is modified for state estimation of systems with intermittent actuator faults when the fault rate is known. The proposed estimator is then extended to the case when the actuator fault rate is unknown using the multiple model estimation algorithm. In addition, a sketch of a proof of convergence for this technique is provided. Several simulations involving a DC motor that experiences random actuator faults demonstrate the effectiveness of the proposed techniques.

关键词： Actuator failures Parameter estimation estimation algorithms Stochastic parameters State estimation

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Optimal measurement constellation of the fluoroBancroft localization algorithm for position estimation in tracking confocal microscopy

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IFAC Proceedings Volumes 2010年第18期43卷 435-440页

作者： Zhaolong Shen Sean B. Andersson Department of Mechanical Engineering Boston University Boston MA 02215 USA

The fluoroBancroft algorithm is an analytical approach that converts a collection of fluorescence intensity measurements generated by an isolated sub-diffraction limit source into an estimate with nanometer-scale precision of the source position. Based on this algorithm, we have developed a scheme for tracking single fluorescent particles in a confocal microscope. In this paper, we determine an optimal measurement constellation for the estimation algorithm. The position estimation bias and uncertainty arising from the photon counting statistics are calculated based on the assumption that the natural logarithm of a Poisson random variable with large rate can be approximated as a random variable with a Gaussian distribution. A sufficient condition for an unbiased measurement constellation and the optimal radius of a given constellation geometry with six measurements are then derived. The results are illustrated through numerical simulation.

关键词： estimation algorithms Particle tracking Parameter optimization

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POSITION estimation AND MODELING OF A FLEXIBLE INDUSTRIAL ROBOT

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IFAC Proceedings Volumes 2005年第1期38卷 253-258页

作者： R. Karlsson M. Norrlöf Dept. of Electrical Engineering Linköping University

A sensor fusion technique is presented and it is shown to achieve good estimates of the position for a 3 degrees-of-freedom industrial robot model. By using an accelerometer the estimate of the tool position accuracy can be improved. The computation of the position is formulated as a Bayesian estimation problem and two solutions are proposed. One using the extended Kalman filter and one using the particle filter. Since the aim is to use the positions estimates to improve trajectory tracking with an iterative learning control method, no computational constraints arise. In an extensive simulation study the performance is compared to the Cramer-Rao lower bound. A significant improvement in position accuracy is achieved using the sensor fusion technique.

关键词： Industrial robots estimation extended Kalman filters estimation algorithms

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Maneuver Classification for Road Vehicles with Constrained Filtering Techniques 21st

Maneuver Classification for Road Vehicles with Constrained F...

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21st IFAC World Congress on Automatic Control - Meeting Societal Challenges

作者： Toro, Oliver Becsi, Tamas Aradi, Szilard Kolat, Mate Gaspar, Peter Budapest Univ Technol & Econ Dept Control Transportat & Vehicle Syst Budapest Hungary Hungarian Acad Sci Comp & Automat Res Inst Syst & Control Lab Budapest Hungary

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. Copyright (C) 2020 The Authors.

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

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Backstepping based Visual Servoing for Transport Aircraft Automatic Landing

Backstepping based Visual Servoing for Transport Aircraft Au...

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IEEE Conference on Control Applications (CCA)

作者： Burlion, Laurent de Plinval, Henry Mouyon, Philippe Off Natl Etud & Rech Aerosp French Aerosp Lab F-31055 Toulouse France

ISBN: (纸本)9781479974092

In this paper we propose a vision based flight control law for the automatic landing of an aircraft. Newly defined visual features obtained by a 2D-video camera embed the needed information about the relative pose between the aircraft and the runway. To cope with their dependence on the unknown distance to the ground, a depth estimation algorithm is proposed. With these elements, several nonlinear and adaptive control backstepping laws are derived depending on our knowledge of the runway width. Closed-loop stability proofs are also provided. Simulation results show the effectiveness of the approach.

关键词： Nonlinear control Visual motion estimation algorithms

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Inventory Control for a Manufacturing System under Uncertainty: Adaptive Approach

Inventory Control for a Manufacturing System under Uncertain...

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20th World Congress of the International-Federation-of-Automatic-Control (IFAC)

作者： Azarskov, Valerii N. Zhiteckii, Leonid S. Solovchuk, Klavdia Yu. Sushchenko, Olga A. Lupoi, Roman O. Natl Aviat Univ UA-03058 Kiev Ukraine Int Ctr Inform Technol & Syst Inst Cybernet UA-03680 Kiev Ukraine

This paper deals with controlling the in-process inventories for the manufacturing system of a typical machine building enterprise which includes the machining, the transport, the storage bunker and the assembly line. The decision-making is implemented under uncertainty associated with the absence of exact machining model assuming that machine failures are also possible. To cope with this uncertainty, the adaptive control approach is proposed. Within this approach, a new adaptive reorder policy which makes it possible to improve the performance of the inventory control system is developed. Simulation experiments are conducted to demonstrate the advantage of this policy. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Adaptation estimation algorithms identifiers inventory control production planning and scheduling uncertainty

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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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