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This paper presents a novel technique for upscaling multiphase fluid flow in complex porous materials that combines asymptotic homogenization approach with hydrodynamicand thermodynamic bounds. Computational asymptotic homogenization has been widely utilised in solid mechanics as a method for analysing multiscale expansion and convergence coefficients in heterogeneous systems. Computations are performed over several volumes by increasing the size until convergence of the material parameters under different load scenarios is achieved. It works by simplifying the problem with a homogenization method and is ideally suited for estimating the representative elementary volume of microporous material by expanding algorithms. The validity of the method to include complex multiphase hydrodynamic processes and their interaction with the matrix structure of porous media lacks a sound theoretical foundation. To overcome this problem, a variational thermodynamic approach is used. Upper and lower bounds of entropy production are proposed to provide effective material properties with uncertainties. This allows multiple possibilities to address dynamics via thermodynamically linked processes. This work utilizes volume of fluid approach to model multiphase porous media flow in models based on micro-computerized tomography x-ray data of Bentheimer sandstone and Savonnieres carbonate. It is found that the representative elementary volume sizes obtained by the conventional asymptotic homogenization methods do not satisfy thermodynamic bounds which consistently require larger representative elementary volume sizes. For the Savonnieres carbonate the entropic bounds have not converged fully questioning the reliability of the effective properties obtained from the classical method. [ABSTRACT FROM AUTHOR]

The method of simultaneous identification and optimization aims at satisfying the conditions of optimality while providing accurate predictions of the process outputs. The model parameters are updated in a run-to-run procedure as to account for changes in operating points and to correct for errors in the predicted gradients of the cost-function and constraints. To make this parameter updating step more robust, we propose a parameter identification objective that includes a ratio of the sum of squared errors to a parametric gradient sensitivity function. This results in an identified set of parameters which provide larger sensitivities for the subsequent gradient correction step thus leading to faster convergence to the optimum. Moreover, worst-case uncertainty bounds on the model outputs are utilized to enforce an adequate model fitting. This is especially valuable when identifying dynamic metabolic models with many parameters. The resulting improvements are illustrated using two simulated cell culture processes. [ABSTRACT FROM AUTHOR]

Bulk chemical composition is a fundamental property of a planetary material, rock or regolith, and can be used to constrain the properties and history of a material, and by extension its parent body, including its potential for habitability. Here, we investigate how uncertainties in bulk major element analyses can affect inferences derived from those analyses, including rock classification by total-alkalis–silica (TAS); Chemical Index of Alteration (CIA); a tectonic discriminant for magma genesis; and the inferred mantle pressure and temperature of a basaltic magma’s origin. Uncertainties for actual spacecraft instruments (Mars Exploration Rover and Mars Science Laboratory (MER/MSL), Alpha Proton X-Ray Spectroscopy (APXS), and Mars Science Laboratory: Laser-Induced Breakdown Spectroscopy (MSL LIBS)) and a suggested uncertainty level for analyses on Venus (Venus Exploration Targets (VExT) Workshop) are higher than those of standard Earth-based analyses (e.g., by inductively coupled plasma optical emission spectrometry (ICPOES)). We propagate the uncertainties from each analysis type to the derived parameters, both implicitly and via boot-strap (Monte Carlo) methods. Our calculations show that the uncertainties of APXS and VExT are greater than those for ICPOES, but they still allow useful inferences about rock type and history. Our results show that the uncertainties of MSL LIBS analyses are significantly larger than the other techniques, and can provide only limited constraints on rock types or histories. Any instruments chosen for future mission must have uncertainties of the chemical analyses small enough to meet the mission’s overall scientific objectives.

The controller design for a polynomial system with uncertainties and some/all un‐measurable states is a hard issue for researchers. Furthermore, if there is no information for the upper bounds of uncertainties, the design work will be more challenging. In order to overcome these difficulties, this study proposes a novel approach based on the unknown input method and disturbance observer to synthesise an observer‐based controller for the mentioned system such that the states are estimated and stability of the system is achieved asymptotically. First, based on the unknown input method, let the uncertainties of the polynomial system be regarded as the disturbance, and then a new observer is proposed to estimate the disturbance and states simultaneously. Next, the observer‐based controller is synthesised with suitable parameters which are computed from the conditions in the main theorems. Those parameters are obtained by using Matlab tool to solve a set of polynomial linear matrix inequalities which are derived in the main theorems too. With the aid of Lyapunov theory and sum‐of‐square technique, the above two main theorems are derived. It is noted that this study allows the uncertainty bounds are unknown and some/all states are un‐measurable. Finally, two examples are presented to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Recently, a subspace fitting approach has been proposed for vibration-based finite element model updating. The approach makes use of subspace-based system identification, where the extended observability matrix is estimated from vibration measurements. Finite element model updating is performed by correlating the model-based observability matrix with the estimated one, by using a single set of experimental data. Hence, the updated finite element model only reflects this single test case. However, estimates from vibration measurements are inherently exposed to uncertainty due to unknown excitation, measurement noise and finite data length. In this paper, a covariance estimation procedure for the updated model parameters is proposed, which propagates the data-related covariance to the updated model parameters by considering a first-order sensitivity analysis. In particular, this propagation is performed through each iteration step of the updating minimization problem, by taking into account the covariance between the updated parameters and the data-related quantities. Simulated vibration signals are used to demonstrate the accuracy and practicability of the derived expressions. Furthermore, an application is shown on experimental data of a beam. [ABSTRACT FROM AUTHOR]

A critical step in the control design of industrial processes is the Control Configuration Selection (CCS), where each actuator is associated with a set of measurements which will be used in the calculation of the control action. A possible solution to the CCS problem is given by the gramian-based Interaction Measures (IMs), which are derived from nominal process models. This paper introduces the derivation of uncertainty bounds for a gramian-based IM using models with uncertainty described in multiplicative form. An alternative to this model-based approach is presented, where uncertainty bounds are estimated from a tailored experiment. In addition, a procedure for robust CCS is introduced. This procedure integrates the calculated uncertainty bounds in the design of the control configuration. [ABSTRACT FROM AUTHOR]

• The sensor fault detection and isolation problems are studied for the accelerometer system in drilling tools. • Process disturbances without prior assumptions are estimated as time-varying zonotopic sets. • A residual generator with the Zonotopic Kalman Filter is proposed to deal with uncertainties, and the bounds of parametric uncertainties and measurement noises are obtained by calibration. • A fault isolation method is developed by fault directions. Sensor faults of the accelerometer system impact the efficiency and security of the drilling process, so the sensor fault detection and isolation are significant. However, there are insufficient relevant studies on the accelerometer system affected by time-varying disturbances with unknown bounds. This paper proposes a residual generator based on the Zonotopic Kalman Filter with the process disturbance estimation, and the bounds of parametric uncertainties and measurement noises are obtained by calibration. Then, a fault detection test is realized by time-varying residual bounds regarded as the combinations of residuals and dynamic thresholds. Moreover, a fault isolation decision is developed by analyzing fault directions. Finally, simulation and experiment results demonstrate the effectiveness of the proposed method. The proposed method estimates time-varying process disturbances and performs better than existing methods. This paper provides a reference for the reliable application of the accelerometer system. [ABSTRACT FROM AUTHOR]

Highlights: [•] The S101 Bridge, a prestressed concrete bridge, was damaged in a large scale progressive damage test. [•] Ambient vibration measurements during damage test provide a benchmark for monitoring algorithms. [•] Two algorithms are evaluated that take the intrinsic uncertainty in the data into account. [•] An identification and a detection approach are considered, both subspace-based. [•] The structural changes during the progressive damage test can be clearly detected. [Copyright &y& Elsevier]

The aim of this paper is to solve optical character recognition (OCR) problem using the interval type-2 neural fuzzy system (IT2NFS) with stable learning mechanism and uncertainty bounds operations for computation speedup and implementation on digital signal processors (DSPs). Differ from most of the interval type-2 fuzzy neural networks, the type-reduction of IT2NFS is embedded in the network structure by using uncertainty bounds method such that the time-consuming Karnik-Mendel (KM) algorithm can be avoided. The simultaneous perturbation stochastic approximation (SPSA) algorithm provides the gradient free property which is suitable for training IT2NFS. The classification of 26 English letters on the image under the conditions of rotation, scale, and displacement is described to illustrate the proposed OCR system. The experimental results demonstrate the feasibility of the designed OCR system based on a fixed-point TMS320DM6437 DSP from Texas Instruments. [ABSTRACT FROM AUTHOR]

Abstract: Stochastic Subspace Identification methods have been extensively used for the modal analysis of mechanical, civil or aeronautical structures for the last ten years. So-called stabilization diagrams are used, where modal parameters are estimated at successive model orders, leading to a graphical procedure where the physical modes of the system are extracted and separated from spurious modes. Recently an uncertainty computation scheme has been derived for allowing the computation of uncertainty bounds for modal parameters at some given model order. In this paper, two problems are addressed. Firstly, a fast computation scheme is proposed reducing the computational burden of the uncertainty computation scheme by an order of magnitude in the model order compared to a direct implementation. Secondly, a new algorithm is proposed to derive efficiently the uncertainty bounds for the estimated modes at all model orders in the stabilization diagram. It is shown that this new algorithm is both computationally and memory efficient, reducing the computational burden by two orders of magnitude in the model order. [Copyright &y& Elsevier]

Abstract: In operational modal analysis, the modal parameters (natural frequencies, damping ratios and mode shapes), obtained with stochastic subspace identification from ambient vibration measurements of structures, are subject to statistical uncertainty. It is hence necessary to evaluate the uncertainty bounds of the obtained results, which can be done by a first-order perturbation analysis. To obtain vibration measurements at many coordinates of a structure with only a few sensors, it is common practice to use multiple sensor setups for the measurements. Recently, a multi-setup subspace identification algorithm has been proposed that merges the data from different setups prior to the identification step to obtain one set of global modal parameters, taking the possibly different ambient excitation characteristics between the measurements into account. In this paper, an algorithm is proposed that efficiently estimates the covariances on modal parameters obtained from this multi-setup subspace identification. The new algorithm is validated on multi-setup ambient vibration data of the Z24 Bridge, benchmark of the COST F3 European network. [Copyright &y& Elsevier]

SUMMARY This paper discusses the use of H-infinity approximation for the online adaptation of PID controller parameters. For a frequency loop-shaping control objective, it is possible to adapt the PID parameters directly with linear model estimation algorithms. Standard least squares algorithms are common solutions for this problem, but their estimates exhibit a well-known strong dependence on the properties of the excitation. This drawback becomes more pronounced for systems where the modeling mismatch is large, as is frequently the case in PID control. In an alternative formulation of the estimation problem, we use a filter-bank to decompose the error signal to different components and minimize approximately the H-infinity norm of the sensitivity-weighted error operator. This approach results in a more consistent estimate of the optimal PID parameters, at the expense of higher excitation requirements. It also allows for the computation of a 'health indicator' to describe the confidence in the estimated parameters. The practical implication of this observation is that PIDs can be tuned more reliably, even in cases of large mismatch between the target and the feasible loop shapes. It also suggests a general theme where a min-max optimization of an operator error provides an advantage over signal error optimization. The key aspects of the algorithm are illustrated by numerical examples. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper proposes an interval type-2 Takagi-Sugeno-Kang fuzzy neural system (IT2TFNS) to develop an on-line adaptive controller using stable simultaneous perturbation stochastic approximatio (SPSA) algorithm. The proposed IT2TFNS realizes an interval type-2 TSK fuzzy logic system formed by the neural network structure. Differ from the most of interval type-2 fuzzy systems, the type-reduction of the proposed IT2TFNS is embedded in the network by using uncertainty bounds method such that the time-consuming Karnik-Mendel (KM) algorithm is replaced. The proposed stable SPSA algorithm provides the gradient free property and faster convergence. However, the stable SPSA algorithm inherently has the problem for on-line adaptive control. Hence, in order to achieve the on-line result, we utilize the sliding surface to develop a new on-line adaptive control scheme. In addition, the corresponding stable learning is derived by Lyapunov theorem which guarantees the convergence and stability of the closed-loop systems. Simulation and comparison results are shown to demonstrate the performance and effectiveness of our approach. [ABSTRACT FROM AUTHOR]

This paper presents a type-2 fuzzy robust regulator for the nonlinear and uncertain systems. In this paper, first, state equations of the nonlinear system is linearized around operating point and then a fuzzy system is designed based on state vector of the linearized system and knowledge about the control system. The designed fuzzy system uses control signals to regulate outputs through measurements obtained from system state vector and basic fuzzy rules of the human knowledge. In this paper, to immediate use of the regulator, type-2 fuzzy system is designed based on uncertainty bounds technique. Finally, simulations are implemented for an individual inverted pendulum model in two cases: certain and uncertain equations of the system. In both cases, the simulation results show that proposed regulator has a better performance in convergence speed and robustness compare to type-1 fuzzy regulator. [ABSTRACT FROM AUTHOR]

One major drawback of the PolyMAX estimator, a polyreference least-squares frequency-domain estimator, is the lack of confidence bounds on the estimated modal parameters. This paper proposes a fast two-step approach to determine the variances on the estimated polynomial parameters and next on the resonance frequencies and damping ratios. The approach is based on the linearization of the sensitivity of the modal parameters to the noise variance. This noise variance (explicitly or as the coherence function) is assumed to be known a priori. The approach is tested on simulations and real-life measurements. [Copyright &y& Elsevier]

The least-squares complex frequency-domain (LSCF) estimator—commercially known as the PolyMAX estimator—nowadays is used intensively in various modal analysis applications. The main advantages are the very clear stabilization diagrams and even more important, the speed. In this paper it is shown that confidence intervals of the modal parameter estimates can be derived without major additional calculations, if the frequency response functions are uncorrelated and noise information (e.g. the coherence function) is available. This approach is also applied to the iterative quadratic maximum likelihood (IQML) estimator, indicating strong analogies to the maximum likelihood (ML) estimator. The algorithm is evaluated by means of Monte Carlo simulations. [Copyright &y& Elsevier]

Abstract: The modal parameters of a structure that are estimated from ambient vibration measurements are always subject to bias and variance errors. In this paper, it is discussed how part of the bias errors can be removed and how the variance errors can be estimated from a single ambient vibration test. The bias removal procedure makes use of a stabilization diagram. The variance estimation procedure uses the first-order sensitivity of the modal parameter estimates to perturbations of the measured output-only data. This methodology, that is generally applicable, is illustrated here for the reference-based covariance-driven stochastic subspace identification algorithm. Both simulated and measured vibration data are used to demonstrate the accuracy and practicability of the derived expressions. [Copyright &y& Elsevier]

Identification of parametric transfer-function models from noisy input/output observations is an important task in many engineering applications. Aside from the parametric model, the estimation algorithm used should also provide accurate confidence bounds. In addition, it is important to know whether the proposed estimation algorithm has the lowest possible uncertainty within the class of consistent estimators. This paper handles these issues for the frequency-domain Gaussian maximum-likelihood estimator of rational transfer-function models within an errors-in-variables framework. [ABSTRACT FROM AUTHOR]

In (operational) modal analysis the modal parameters of a structure are identified from the response of that structure to (unmeasurable operational) perturbations. A key issue that remains to be solved is the calculation of uncertainty bounds on the estimated modal parameters. The present paper fills this gap. The theory is illustrated by means of a simulation and a real measurement example (operational modal analysis of a bridge). [Copyright &y& Elsevier]

The dynamic responses of a recurrent-fuzzy-neural-network (RFNN) sliding-mode controlled motor-toggle servomechanism are described. The servomechanism is a toggle mechanism actuated by a permanent magnet (PM) synchronous servo motor. First, a total sliding-mode control system, which is insensitive to uncertainties including parameter variations and external disturbance in the whole control process, is introduced. In the baseline model design a computed torque controller is designed to cancel the nonlinearity of the nominal plant. In the curbing controller design an additional controller is designed using a new sliding surface to ensure the sliding motion through the entire state trajectory. Therefore, in the total sliding-mode control system the controlled system has a total sliding motion without a reaching phase. Then, to overcome the two main problems with sliding-mode control, a RFNN sliding-mode control system is investigated to control the motor-toggle servomechanism. In the RFNN sliding-mode control system a RFNN bound observer is utilized to adjust the uncertainty bounds in real time. To guarantee the convergence of tracking error, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the RFNN. Simulated and experimental results due to periodic sinusoidal command show that the dynamic behaviors of the proposed control systems are robust with regard to uncertainties [ABSTRACT FROM PUBLISHER]

This paper discusses experimental robot identification based on a statistical framework. It presents a new approach toward the design of optimal robot excitation trajectories, and formulates the maximum-likelihood estimation of dynamic robot model parameters. The differences between the new design approach and the existing approaches lie in the parameterization of the excitation trajectory and in the optimization criterion. The excitation trajectory for each joint is a finite Fourier series. This approach guarantees periodic excitation which is advantageous because it allows: 1) time-domain data averaging; 2) estimation of the characteristics of the measurement noise, which is valuable in the case of maximum-likelihood parameter estimation. In addition, the use of finite Fourier series allows calculation of the joint velocities and acceleration in an analytic way from the measured position response, and allows specification of the bandwidth of the excitation trajectories. The optimization criterion is the uncertainty on the estimated parameters or a lower bound for it, instead of the often used condition of the parameter estimation problem. Simulations show that this criterion yields parameter estimates with smaller uncertainty bounds than trajectories optimized according to the classical criterion. Experiments on an industrial robot show that the presented trajectory design and maximum-likelihood parameter estimation approaches complement each other to make a practicable robot identification technique which yields accurate robot models. [ABSTRACT FROM PUBLISHER]

In this paper, the robust nonlinear controller for an uncertain robot system is developed and characterized with a unified method. Based on deterministic approach, the control structure consists of two parts: In the first part, the primary control law is synthesized to precompensate for the nominal system; and in the second part the adaptive version of robust controllers are utilized to postcompensate for the system uncertainties. The uncertainties assumed in this papar are bounded by higher-order polynomials in the Euclidean norms of system states without knowledge of bounding coefficients. Using the Lyapunov stability theory, we can guarantee that all possible responses of the closed-loop system are at least uniformly and ultimately bounded. The tracking properties of the control algorithms are verified through numerical simulations, and the results show that the proposed controllers are proven to be robust enough for any higher-order system uncertainty. [ABSTRACT FROM AUTHOR]