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Measurement Noise (measurement + noise)
Selected AbstractsSystem identification applied to long-span cable-supported bridges using seismic recordsEARTHQUAKE ENGINEERING AND STRUCTURAL DYNAMICS, Issue 3 2008Dionysius M. Siringoringo Abstract This paper presents the application of system identification (SI) to long-span cable-supported bridges using seismic records. The SI method is based on the System Realization using Information Matrix (SRIM) that utilizes correlations between base motions and bridge accelerations to identify coefficient matrices of a state-space model. Numerical simulations using a benchmark cable-stayed bridge demonstrate the advantages of this method in dealing with multiple-input multiple-output (MIMO) data from relatively short seismic records. Important issues related to the effects of sensor arrangement, measurement noise, input inclusion, and the types of input with respect to identification results are also investigated. The method is applied to identify modal parameters of the Yokohama Bay Bridge, Rainbow Bridge, and Tsurumi Fairway Bridge using the records from the 2004 Chuetsu-Niigata earthquake. Comparison of modal parameters with the results of ambient vibration tests, forced vibration tests, and analytical models are presented together with discussions regarding the effects of earthquake excitation amplitude on global and local structural modes. Copyright © 2007 John Wiley & Sons, Ltd. [source] On-line identification of non-linear hysteretic structural systems using a variable trace approachEARTHQUAKE ENGINEERING AND STRUCTURAL DYNAMICS, Issue 9 2001Jeng-Wen Lin Abstract In this paper, an adaptive on-line parametric identification algorithm based on the variable trace approach is presented for the identification of non-linear hysteretic structures. At each time step, this recursive least-square-based algorithm upgrades the diagonal elements of the adaptation gain matrix by comparing the values of estimated parameters between two consecutive time steps. Such an approach will enforce a smooth convergence of the parameter values, a fast tracking of the parameter changes and will remain adaptive as time progresses. The effectiveness and efficiency of the proposed algorithm is shown by considering the effects of excitation amplitude, of the measurement units, of larger sampling time interval and of measurement noise. The cases of exact-, under-, over-parameterization of the structural model have been analysed. The proposed algorithm is also quite effective in identifying time-varying structural parameters to simulate cumulative damage in structural systems. Copyright © 2001 John Wiley & Sons, Ltd. [source] Stochastic Cost Optimization of Multistrategy DNAPL Site RemediationGROUND WATER MONITORING & REMEDIATION, Issue 3 2010Jack Parker This paper investigates numerical optimization of dense nonaqueous phase liquid (DNAPL) site remediation design considering effects of prediction and measurement uncertainty. Results are presented for a hypothetical problem involving remediation using thermal source reduction (TSR) and bioremediation with electron donor (ED) injection. Pump-and-treat is utilized as a backup measure if compliance criteria are not met. Remediation system design variables are optimized to minimize expected net present value (ENPV) cost. Adaptive criteria are assumed for real-time control of TSR and ED duration. Source zone dissolved concentration data enabled more reliable and lower cost operation of TSR than soil concentration data, but using both soil and dissolved data improved results sufficiently to more than offset the additional cost. Decisions to terminate remediation and monitoring or to initiate pump-and-treat are complicated by measurement noise. Simultaneous optimization of monitoring frequency, averaging period, and lookback periods to confirm decisions, in addition to remediation design variables, reduced ENPV cost. Results indicate that remediation design under conditions of uncertainty is affected by subtle interactions and tradeoffs between design variables, compliance rules, site characteristics, and uncertainty in model predictions and monitoring data. Optimized designs yielded cost savings of up to approximately 50% compared with a nonoptimized design based on common engineering practices. Significant improvements in accuracy and reductions in cost were achieved by recalibrating the model to data collected during remediation and re-optimizing design variables. Repeating this process periodically is advisable to minimize total costs and maximize reliability. [source] An EM-like reconstruction method for diffuse optical tomographyINTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING, Issue 9 2010*Article first published online: 28 JUN 2010, Caifang Wang Abstract Diffuse optical tomography (DOT) is an optical imaging modality which provides the spatial distributions of optical parameters inside an object. The forward model of DOT is described by the diffusion approximation of radiative transfer equation, while the DOT is to reconstruct the optical parameters from boundary measurements. In this paper, an EM-like iterative reconstruction method specifically for the steady state DOT problem is developed. Previous iterative reconstruction methods are mostly based on the assumption that the measurement noise is Gaussian, and are of least-squares type. In this paper, with the assumption that the boundary measurements have independent and identical Poisson distributions, the inverse problem of DOT is solved by maximizing a log-likelihood functional with inequality constraints, and then an EM-like reconstruction algorithm is developed according to the Kuhn,Tucker condition. The proposed algorithm is a variant of the well-known EM algorithm. The performance of the proposed algorithm is tested with three-dimensional numerical simulation. Copyright © 2010 John Wiley & Sons, Ltd. [source] An improved perturbation method for stochastic finite element model updatingINTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING, Issue 13 2008X. G. Hua Abstract In this paper, an improved perturbation method is developed for the statistical identification of structural parameters by using the measured modal parameters with randomness. On the basis of the first-order perturbation method and sensitivity-based finite element (FE) model updating, two recursive systems of equations are derived for estimating the first two moments of random structural parameters from the statistics of the measured modal parameters. Regularization technique is introduced to alleviate the ill-conditioning in solving the equations. The numerical studies of stochastic FE model updating of a truss bridge are presented to verify the improved perturbation method under three different types of uncertainties, namely natural randomness, measurement noise, and the combination of the two. The results obtained using the perturbation method are in good agreement with, although less accurate than, those obtained using the Monte Carlo simulation (MCS) method. It is also revealed that neglecting the correlation of the measured modal parameters may result in an unreliable estimation of the covariance matrix of updating parameters. The statistically updated FE model enables structural design and analysis, damage detection, condition assessment, and evaluation in the framework of probability and statistics. Copyright © 2007 John Wiley & Sons, Ltd. [source] Matching a system behavior with a known set of models: A quadratic optimization-based adaptive solutionINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 9 2009Moisés Bonilla Abstract The matching process between a time-domain external behavior of a lumped single-input single-output dynamical system and a known set of linear continuous time-invariant models is tackled in this paper. The proposed online solution is based on an adaptive structure detector, which in finite time locates in the known set of models the one corresponding to the observed external behavior; the detector results from the solution of a constrained quadratic optimization problem. The problem is expressed in terms of the time-domain activity of a family of discriminating filters and is solved via a normalized gradient algorithm, which avoids mismatching due to the presence of structural zeros in the filters and can take into account band-limited high-frequency measurement noise. A failure detection problem concerning a simulated servomechanism is included in order to illustrate the proposed solution. Copyright © 2008 John Wiley & Sons, Ltd. [source] Extending the Frisch scheme for errors-in-variables identification to correlated output noiseINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 1 2008Torsten Söderström Abstract Several estimation methods have been proposed for identifying errors-in-variables systems, where both input and output measurements are corrupted by noise. One of the promising approaches is the so-called Frisch scheme. In its standard form, it is designed to handle white measurement noise on the input and output sides. As the output noise comprises both effects of measurement errors and of process disturbances, it is much more realistic to allow correlated output noise. It is described in the paper how the Frisch scheme can be extended to such cases. Copyright © 2007 John Wiley & Sons, Ltd. [source] Online trained support vector machines-based generalized predictive control of non-linear systemsINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 10 2006S. Iplikci Abstract In this work, an online support vector machines (SVM) training method (Neural Comput. 2003; 15: 2683,2703), referred to as the accurate online support vector regression (AOSVR) algorithm, is embedded in the previously proposed support vector machines-based generalized predictive control (SVM-Based GPC) architecture (Support vector machines based generalized predictive control, under review), thereby obtaining a powerful scheme for controlling non-linear systems adaptively. Starting with an initially empty SVM model of the unknown plant, the proposed online SVM-based GPC method performs the modelling and control tasks simultaneously. At each iteration, if the SVM model is not accurate enough to represent the plant dynamics at the current operating point, it is updated with the training data formed by persistently exciting random input signal applied to the plant, otherwise, if the model is accepted as accurate, a generalized predictive control signal based on the obtained SVM model is applied to the plant. After a short transient time, the model can satisfactorily reflect the behaviour of the plant in the whole phase space or operation region. The incremental algorithm of AOSVR enables the SVM model to learn the new training data pair, while the decremental algorithm allows the SVM model to forget the oldest training point. Thus, the SVM model can adapt the changes in the plant and also in the operating conditions. The simulation results on non-linear systems have revealed that the proposed method provides an excellent control quality. Furthermore, it maintains its performance when a measurement noise is added to the output of the underlying system. Copyright © 2006 John Wiley & Sons, Ltd. [source] Simultaneous input and parameter estimation with input observers and set-membership parameter bounding: theory and an automotive applicationINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 5 2006I. Kolmanovsky Abstract The paper addresses an on-line, simultaneous input and parameter estimation problem for a first-order system affected by measurement noise. This problem is motivated by practical applications in the area of engine control. Our approach combines an input observer for the unknown input with a set-membership algorithm to estimate the parameter. The set-membership algorithm takes advantage of a priori available information such as (i) known bounds on the unknown input, measurement noise and time rate of change of the unknown input; (ii) the form of the input observer in which the unknown parameter affects only the observer output; and (iii) the input observer error bounds for the case when the parameter is known exactly. The asymptotic properties of the algorithm as the observer gain increases are delineated. It is shown that for accurate estimation the unknown input needs to approach the known bounds a sufficient number of times (these time instants need not be known). Powertrain control applications are discussed and a simulation example based on application to engine control is reported. A generalization of the basic ideas to higher order systems is also elaborated. Copyright © 2006 John Wiley & Sons, Ltd. [source] Guaranteed recursive non-linear state bounding using interval analysisINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 3 2002Michel Kieffer Abstract The problem considered here is state estimation in the presence of unknown but bounded state perturbations and measurement noise. In this context, most available results are for linear models, and the purpose of the present paper is to deal with the non-linear case. Based on interval analysis and the notion of set inversion, a new state estimator is presented, which evaluates a set estimate guaranteed to contain all values of the state that are consistent with the available observations, given the perturbation and noise bounds and a set containing the initial value of the state. To the best of our knowledge, it is the first estimator for which this claim can be made. The precision of the set estimate can be improved, at the cost of more computation. Theoretical properties of the estimator are studied, and computer implementation receives special attention. A simple illustrative example is treated. Copyright © 2002 John Wiley & Sons, Ltd. [source] Adaptive recurrent neural network control of biological wastewater treatmentINTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 2 2005Ieroham S. Baruch Three adaptive neural network control structures to regulate a biological wastewater treatment process are introduced: indirect, inverse model, and direct adaptive neural control. The objective is to keep the concentration of the recycled biomass proportional to the influent flow rate in the presence of periodically acting disturbances, process parameter variations, and measurement noise. This is achieved by the so-called Jordan Canonical Recurrent Trainable Neural Network, which is a completely parallel and parametric neural structure, permitting the use of the obtained parameters, during the learning phase, directly for control system design. Comparative simulation results confirmed the applicability of the proposed control schemes. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 173,193, 2005. [source] New RF extrinsic resistances extraction procedure for deep-submicron MOS transistorsINTERNATIONAL JOURNAL OF NUMERICAL MODELLING: ELECTRONIC NETWORKS, DEVICES AND FIELDS, Issue 2 2010J. C. Tinoco Abstract The modeling of MOS transistors used for RF applications needs the definition of a lumped equivalent circuit where the intrinsic device and series extrinsic resistances are properly evaluated. The model accuracy depends on the extraction precision of each intrinsic lumped element. In order to determine the intrinsic device behavior, it is necessary to first remove the series extrinsic resistances. For this reason their extraction becomes critical for the modeling of MOS transistors in RF circuit design. Several extraction methods have been proposed; nevertheless, the measurement noise strongly affects the obtained results. The method proposed by Bracale and co-workers is the most robust extraction procedure against measurement noise, but fails to predict correctly the series extrinsic resistances for deep-submicron devices. For those reasons, we deeply analyze the method proposed by Bracale in order to understand and then overcome its limitations. Based on those analyses, a robust extraction method for deep-submicron devices is proposed. Copyright © 2009 John Wiley & Sons, Ltd. [source] Minimal data rate stabilization of nonlinear systems over networks with large delaysINTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 10 2010C. De Persis Abstract Control systems over networks with a finite data rate can be conveniently modeled as hybrid (impulsive) systems. For the class of nonlinear systems in feedfoward form, we design a hybrid controller, which guarantees stability, in spite of the measurement noise due to the quantization, and of an arbitrarily large delay, which affects the communication channel. The rate at which feedback packets are transmitted from the sensors to the actuators is shown to be arbitrarily close to the infimal one. Copyright © 2009 John Wiley & Sons, Ltd. [source] A new non-linear sliding-mode torque and flux control method for an induction machine incorporating a sliding-mode flux observerINTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 5 2004Fang Chen Abstract In this paper a novel sliding-mode control algorithm, based on the differential geometry state-co-ordinates transformation method, is proposed to control motor torque directly. Non-linear feedback linearization theory is employed to decouple the control of rotor flux magnitude and motor torque. The advantages of this method are: (1) The rotor flux and the generated torque can be accurately controlled. (2) Robustness with respect to matched and mismatched uncertainties is obtained. Additionally, a varying continuous control term is proposed. As a result, chattering is eliminated without sacrificing robustness and precision. The control strategy is based on all motor states being available. In practice the rotor fluxes are not usually measurable, and a sliding-mode observer is derived to estimate the rotor flux. The observer is designed to possess invariant dynamic modes which can be assigned independently to achieve the desired performance. Furthermore, it can be shown that the observer is robust against model uncertainties and measurement noise. Simulation and practical results are presented to confirm the characteristics of the proposed control law and rotor flux observer. Copyright © 2004 John Wiley & Sons, Ltd. [source] Inverse filtering and deconvolutionINTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 2 2001Ali Saberi Abstract This paper studies the so-called inverse filtering and deconvolution problem from different angles. To start with, both exact and almost deconvolution problems are formulated, and the necessary and sufficient conditions for their solvability are investigated. Exact and almost deconvolution problems seek filters that can estimate the unknown inputs of the given plant or system either exactly or almostly whatever may be the unintended or disturbance inputs such as measurement noise, external disturbances, and model uncertainties that act on the system. As such they require strong solvability conditions. To alleviate this, several optimal and suboptimal deconvolution problems are formulated and studied. These problems seek filters that can estimate the unknown inputs of the given system either exactly, almostly or optimally in the absence of unintended (disturbance) inputs, and on the other hand, in the presence of unintended (disturbance) inputs, they seek that the influence of such disturbances on the estimation error be as small as possible in a certain norm (H2 or H,) sense. Both continuous- and discrete-time systems are considered. For discrete-time systems, the counter parts of all the above problems when an ,,-step delay in estimation is present are introduced and studied. Next, we focus on the exact and almost deconvolution but this time when the uncertainties in plant dynamics can be structurally modeled by a ,-block as a feedback element to the nominally known plant dynamics. This is done either in the presence or absence of external disturbances. Copyright © 2001 John Wiley & Sons, Ltd. [source] Improved myelin water quantification using spatially regularized non-negative least squares algorithmJOURNAL OF MAGNETIC RESONANCE IMAGING, Issue 1 2009Dosik Hwang PhD Abstract Purpose To improve the myelin water quantification in the brain in the presence of measurement noise and to increase the visibility of small focal lesions in myelin-water-fraction (MWF) maps. Materials and Methods A spatially regularized non-negative least squares (srNNLS) algorithm was developed for robust myelin water quantification in the brain. The regularization for the conventional NNLS algorithm was expanded into the spatial domain in addition to the spectral domain. Synthetic data simulations were performed to study the effectiveness of this new algorithm. Experimental free-induction-decay measurements were obtained using a multi-gradient-echo pulse sequence and MWF maps were estimated using the srNNLS algorithm. The results were compared with other conventional methods. Results A substantial decrease in MWF variability was observed in both simulations and experimental data when the srNNLS algorithm was applied. As a result, false lesions in the MWF maps disappeared and the visibility of small focal lesions improved greatly. On average, the contrast-to-noise ratio for focal lesions was improved by a factor of 2. Conclusion The MWF variability due to the measurement noise can be substantially reduced and the detection of small focal lesions can be improved by using the srNNLS algorithm. J. Magn. Reson. Imaging 2009;30:203,208. © 2009 Wiley-Liss, Inc. [source] Cosmic flows on 100 h,1 Mpc scales: standardized minimum variance bulk flow, shear and octupole momentsMONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY, Issue 4 2010Hume A. Feldman ABSTRACT The low-order moments, such as the bulk flow and shear, of the large-scale peculiar velocity field are sensitive probes of the matter density fluctuations on very large scales. In practice, however, peculiar velocity surveys are usually sparse and noisy, which can lead to the aliasing of small-scale power into what is meant to be a probe of the largest scales. Previously, we developed an optimal ,minimum variance' (MV) weighting scheme, designed to overcome this problem by minimizing the difference between the measured bulk flow (BF) and that which would be measured by an ideal survey. Here we extend this MV analysis to include the shear and octupole moments, which are designed to have almost no correlations between them so that they are virtually orthogonal. We apply this MV analysis to a compilation of all major peculiar velocity surveys, consisting of 4536 measurements. Our estimate of the BF on scales of ,100 h,1 Mpc has a magnitude of |v| = 416 ± 78 km s ,1 towards Galactic l= 282°± 11° and b= 6°± 6°. This result is in disagreement with , cold dark matter with Wilkinson Microwave Anisotropy Probe 5 (WMAP5) cosmological parameters at a high confidence level, but is in good agreement with our previous MV result without an orthogonality constraint, showing that the shear and octupole moments did not contaminate the previous BF measurement. The shear and octupole moments are consistent with WMAP5 power spectrum, although the measurement noise is larger for these moments than for the BF. The relatively low shear moments suggest that the sources responsible for the BF are at large distances. [source] Spectrum separation resolves partial-volume effect of MRSI as demonstrated on brain tumor scansNMR IN BIOMEDICINE, Issue 10 2008Yuzhuo Su Abstract Magnetic resonance spectroscopic imaging (MRSI) is currently used clinically in conjunction with anatomical MRI to assess the presence and extent of brain tumors and to evaluate treatment response. Unfortunately, the clinical utility of MRSI is limited by significant variability of in vivo spectra. Spectral profiles show increased variability because of partial coverage of large voxel volumes, infiltration of normal brain tissue by tumors, innate tumor heterogeneity, and measurement noise. We address these problems directly by quantifying the abundance (i.e. volume fraction) within a voxel for each tissue type instead of the conventional estimation of metabolite concentrations from spectral resonance peaks. This ,spectrum separation' method uses the non-negative matrix factorization algorithm, which simultaneously decomposes the observed spectra of multiple voxels into abundance distributions and constituent spectra. The accuracy of the estimated abundances is validated on phantom data. The presented results on 20 clinical cases of brain tumor show reduced cross-subject variability. This is reflected in improved discrimination between high-grade and low-grade gliomas, which demonstrates the physiological relevance of the extracted spectra. These results show that the proposed spectral analysis method can improve the effectiveness of MRSI as a diagnostic tool. Copyright © 2008 John Wiley & Sons, Ltd. [source] Enhancing Controller Performance via Dynamic Data ReconciliationTHE CANADIAN JOURNAL OF CHEMICAL ENGINEERING, Issue 3 2005Shuanghua Bai Abstract Measured values of process variables are subject to measurement noise. The presence of measurement noise can result in detuned controllers in order to prevent excessive adjustments of manipulated variables. Digital filters, such as exponentially weighted moving average (EWMA) and moving average (MA) filters, are commonly used to attenuate measurement noise before controllers. In this article, we present another approach, a dynamic data reconciliation (DDR) filter. This filter employs discrete dynamic models that can be phenomenological or empirical, as constraints in reconciling noisy measurements. Simulation results for a storage tank and a distillation column under PI control demonstrate that the DDR filter can significantly reduce propagation of measurement noise inside control loops. It has better performance than the EWMA and MA filters, so that the overall performance of the control system is enhanced. Les valeurs mesurées des variables de procédé sont affectées par les bruits de mesure. La présence de bruit de mesure force de régler à la baisse les régulateurs afin de prévenir des mouvements excessifs des variables manipulées. Des filtres numériques, tels que les filtres à moyenne mobile pondérée exponentiellement (EWMA) et les filtres à moyenne mobile (MA), sont communément utilisés pour atténuer le bruit de mesure avant les régulateurs. On présente dans cet article une autre approche, soit un filtre dynamique de réconciliation de données (DDR). Ce filtre emploie des modèles dynamiques discrets qui peuvent être phénoménologiques ou empiriques comme contraintes pour réconcilier les mesures bruitées. Les résultats de simulation pour un réservoir de stockage et une colonne à distiller utilisant un régulateur PI montrent que le filtre DDR peut réduire de manière significative la propagation du bruit de mesure dans les boucles de régulation. Sa performance est meilleure que celles des filtres EWMA ou MA, et par conséquent la performance globale du système de commande s'en trouve accrue. [source] Chattering reduction of sliding mode control by low-pass filtering the control signalASIAN JOURNAL OF CONTROL, Issue 3 2010Ming-Lei Tseng Abstract The conventional approach to reducing control signal chattering in sliding mode control is to use the boundary layer design. However, when there is high-level measurement noise, the boundary layer design becomes ineffective in chattering reduction. This paper, therefore, proposes a new design for chattering reduction by low-pass filtering the control signal. The new design is non-trivial since it requires estimation of the sliding variable via a disturbance estimator. The new sliding mode control has the same performance as the boundary layer design in noise-free environments, and outperforms the boundary layer design in noisy environments. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [source] Reduced-order robust adaptive control design of uncertain SISO linear systemsINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 7 2008Qingrong Zhao Abstract In this paper, a stability and robustness preserving adaptive controller order-reduction method is developed for a class of uncertain linear systems affected by system and measurement noises. In this method, we immediately start the integrator backstepping procedure of the controller design without first stabilizing a filtered dynamics of the output. This relieves us from generating the reference trajectory for the filtered dynamics of the output and thus reducing the controller order by n, n being the dimension of the system state. The stability of the filtered dynamics is indirectly proved via an existing state signal. The trade-off for this order reduction is that the worst-case estimate for the expanded state vector has to be chosen as a suboptimal choice rather than the optimal choice. It is shown that the resulting reduced-order adaptive controller preserves the stability and robustness properties of the full-order adaptive controller in disturbance attenuation, boundedness of closed-loop signals, and output tracking. The proposed order-reduction scheme is also applied to a class of single-input single-output linear systems with partly measured disturbances. Two examples are presented to illustrate the performance of the reduced-order controller in this paper. Copyright © 2007 John Wiley & Sons, Ltd. [source] Adaptive robust force control for vehicle active suspensionsINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 2 2004Supavut Chantranuwathana Abstract In this paper, the modular adaptive robust control (MARC) technique is applied to design the force loop controller of an electro-hydraulic active suspension system. A key advantage of this modular design approach lies in the fact that the adaptation algorithm can be designed for explicit estimation convergence. The effect of parameter adaptation on force tracking performance can be compensated and thus it is possible to guaranteed certain control performance. Experimental results from a quarter-car active suspension test rig show that when realistic external disturbances and measurement noises exist, the modular design achieves a better estimate than the non-modular ARC design. The improved estimation was found to result in control signals with slightly lower magnitude while maintaining similar tracking performance. Copyright © 2004 John Wiley & Sons, Ltd. [source] A comparative study on a novel model-based PID tuning and control mechanism for nonlinear systemsINTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 13 2010S. Iplikci Abstract This work presents a novel predictive model-based proportional integral derivative (PID) tuning and control approach for unknown nonlinear systems. For this purpose, an NARX model of the plant to be controlled is obtained and then it used for both PID tuning and correction of the control action. In this study, for comparison, neural networks (NNs) and support vector machines (SVMs) have been used for modeling. The proposed structure has been tested on two highly nonlinear systems via simulations by comparing control and convergence performances of SVM- and NN-Based PID controllers. The simulation results have shown that when used in the proposed scheme, both NN and SVM approaches provide rapid parameter convergence and considerably high control performance by yielding very small transient- and steady-state tracking errors. Moreover, they can maintain their control performances under noisy conditions, while convergence properties are deteriorated to some extent due to the measurement noises. Copyright © 2009 John Wiley & Sons, Ltd. [source] A robust fault detection and isolation filter design under sensitivity constraint: An LMI approachINTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 15 2008Saverio Armeni Abstract This paper deals with the design of a residual generator (RG) for linear time-invariant systems subject to simultaneous different faults, disturbances and measurement noises. The objective is to design an RG filter that maximizes the transmission from a potential fault to a related residual, while minimizing the ones from nuisances (disturbances, measurement noises and other faults). The isolation of each fault is carried out by designing a bank of RG filters, each one insensitive, as much as possible, to nuisances and capable of detecting the occurrence of its related fault. The design is carried out through ,, filtering techniques under an eigenstructure assignment constraint. Under mild assumptions, the RG filter can be obtained by solving a ,-parameterized linear matrix inequality optimization problem. A comparison with existing fault detection and isolation (FDI) methods is considered in order to exhibit the relative merits of the proposed method. Copyright © 2008 John Wiley & Sons, Ltd. [source] Robustness of time-scale learning of robot motions to uncertainty in acquired knowledgeJOURNAL OF FIELD ROBOTICS (FORMERLY JOURNAL OF ROBOTIC SYSTEMS), Issue 10 2001C.C. Cheah A disadvantage of present iterative learning control algorithms is that they are generally applicable only in cases where a certain task is performed over and over again. Consequently, if knowledge or control inputs acquired from learning a task can be used on similar tasks, learning will be more efficient. Recently, several methods for constructing the control input of a new motion based on the control inputs acquired from previous learning of similar tasks have been proposed. However, these methods assumed that the perfect control inputs could be obtained from the previous learning. In practice, the control inputs could never be obtained exactly from learning in the presence of certain uncertainties such as disturbance and measurement noises. In addition, it is also not known for sure how the basic motion patterns should be chosen for learning. In this article, the robustness problem of the time-scale learning control to uncertainty in the acquired learning control inputs is formulated and solved. From the analysis, certain new insights such as its implication to choices of basic motion patterns for time-scale learning will be discussed. Simulation results of a 3-link robot are presented to illustrate the analysis. © 2001 John Wiley & Sons, Inc. [source] | |||||||||||||||||||||||||||||||