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 alg...
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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.
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 o...
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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.
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 fe...
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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.
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. Howe...
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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.
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 prec...
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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.
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 ...
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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.
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 uncertaint...
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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.
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 bet...
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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.
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....
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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.
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 mode...
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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.
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