Showing papers on "System identification published in 2001"

Stochastic System Identification for Operational Modal Analysis: A Review

Abstract: This paper reviews stochastic system identification methods that have been used to estimate the modal parameters of vibrating structures in operational conditions. It is found that many classical input-output methods havean output-only counterpart. For instance, the Complex Mode Indication Function (CMIF) can be applied both to Frequency Response Functions and output power and cross spectra. The Polyreference Time Domain (PTD) method applied to impulse responses is similar to the Instrumental Variable (IV) method applied to output covariances. The Eigensystem Realization Algorithm (ERA) is equivalent to stochastic subspace identification.

Dynamical model development and parameter identification for an anaerobic wastewater treatment process

Abstract: This paper deals with the development and the parameter identification of an anaerobic digestion process model. A two-step (acidogenesis-methanization) mass-balance model has been considered. The model incorporates electrochemical equilibria in order to include the alkalinity, which has to play a central role in the related monitoring and control strategy of a treatment plant. The identification is based on a set of dynamical experiments designed to cover a wide spectrum of operating conditions that are likely to take place in the practical operation of the plant. A step by step identification procedure to estimate the model parameters is presented. The results of 70 days of experiments in a 1-m(3) fermenter are then used to validate the model. (C) 2001 John Wiley & Sons, Inc.

Global identifiability of nonlinear models of biological systems

Abstract: A prerequisite for well-posedness of parameter estimation of biological and physiological systems is a priori global identifiability, a property which concerns uniqueness of the solution for the unknown model parameters. Assessing a priori global identifiability is particularly difficult for nonlinear dynamic models. Various approaches have been proposed in the literature but no solution exists in the general case. Here, the authors present a new algorithm for testing global identifiability of nonlinear dynamic models, based on differential algebra. The characteristic set associated to the dynamic equations is calculated in an efficient way and computer algebra techniques are used to solve the resulting set of nonlinear algebraic equations. The algorithm is capable of handling many features arising in biological system models, including zero initial conditions and time-varying parameters. Examples of usage of the algorithm for analyzing a priori global identifiability of nonlinear models of biological and physiological systems are presented.

A feedback control approach for guaranteeing relative delays in Web servers

Abstract: The paper presents the design, implementation, and evaluation of an adaptive architecture to provide relative delay guarantees for different service classes on Web servers under HTTP 1.1. The first contribution of the paper is the architecture based on a feedback control loop that enforces desired relative delays among classes via dynamic connection scheduling and process reallocation. The second contribution is our use of feedback control theory to design the feedback loop with proven performance guarantees. In contrast with ad hoc approaches that often rely on laborious tuning and design iterations, our control theory approach enables us to systematically design an adaptive Web server with established analytical methods. The design methodology includes using system identification to establish a dynamic model, and using the Root Locus method to design a feedback controller to satisfy performance specifications of a Web server. The adaptive architecture has been implemented by modifying an Apache Web server. Experimental results demonstrate that our adaptive server achieves robust relative delay guarantees even when workload varies significantly. Properties of our adaptive Web server include guaranteed stability, and satisfactory efficiency and accuracy in achieving the desired relative delay differentiation.

Model-based predictive diagnostics for primary and secondary batteries

Abstract: The model-based effort described here is focused on predictive diagnostics for primary and secondary batteries. However, this novel approach can also be applied to other electrochemical energy sources such as fuel cells. This method is based on accurate parametric modeling of the transport mechanisms within the battery. This system knowledge was used for the careful development of electrochemical and thermal models. These models have been used to extract new features to be used in conjunction with several traditional measured parameters to assess the condition of the battery. The resulting output and any usable information available about the battery is then evaluated using hybrid automated reasoning schemes consisting of neural network and decision theoretic methods. The focus of this paper is on the model identification and data fusion of the monitored and virtual sensor data. The methodology and analysis presented in this paper is applicable to mechanical systems where multiple sensor types are used for diagnostic assessment.

Using control theory to achieve service level objectives in performance management

Abstract: A widely used approach to achieving service level objectives for a software system (e.g., an email server) is to add a controller that manipulates the target system's tuning parameters. We describe a methodology for designing such controllers for software systems that builds on classical control theory. The classical approach proceeds in two steps: system identification and controller design. In system identification, we construct mathematical models of the target system. Traditionally, this has been based on a first-principles approach, using detailed knowledge of the target system. Such models can be complex and difficult to build, validate, use, and maintain. In our methodology, a statistical (ARMA) model is fit to historical measurements of the target being controlled. These models are easier to obtain and use and allow us to apply control-theoretic design techniques to a larger class of systems. When applied to a Lotus Notes groupware server, we obtain model fits with R/sup 2/ no lower than 75% and as high as 98%. In controller design, an analysis of the models leads to a controller that will achieve the service level objectives. We report on an analysis of a closed-loop system using an integral control law with Lotus Notes as the target. The objective is to maintain a reference queue length. Using root-locus analysis from control theory, we are able to predict the occurrence (or absence) of controller-induced oscillations in the system's response. Such oscillations are undesirable since they increase variability, thereby resulting in a failure to meet the service level objective. We implement this controller for a real Lotus Notes system, and observe a remarkable correspondence between the behavior of the real system and the predictions of the analysis. This indicates that the control theoretic analysis is sufficient to select controller parameters that meet the desired goals, and the need for simulations is reduced.

Identification and control of mechanical systems

Abstract: The control of vibrating systems is a significant issue in the design of aircraft, spacecraft, bridges and high-rise buildings. This 2001 book discusses the control of vibrating systems, integrating structural dynamics, vibration analysis, modern control and system identification. Integrating these subjects is an important feature in that engineers will need only one book, rather than several texts or courses, to solve vibration control problems. The book begins with a review of basic mathematics needed to understand subsequent material. Chapters then cover more recent and valuable developments in aerospace control and identification theory, including virtual passive control, observer and state-space identification, and data-based controller synthesis. Many practical issues and applications are addressed, with examples showing how various methods are applied to real systems. Some methods show the close integration of system identification and control theory from the state-space perspective, rather than from the traditional input-output model perspective of adaptive control. This text will be useful for advanced undergraduate and beginning graduate students in aerospace, mechanical and civil engineering, as well as for practising engineers.

Bayesian spectral density approach for modal updating using ambient data

Abstract: The problem of identification of the modal parameters of a structural model using measured ambient response time histories is addressed. A Bayesian spectral density approach (BSDA) for modal updating is presented which uses the statistical properties of a spectral density estimator to obtain not only the optimal values of the updated modal parameters but also their associated uncertainties by calculating the posterior joint probability distribution of these parameters. Calculation of the uncertainties of the identified modal parameters is very important if one plans to proceed with the updating of a theoretical finite element model based on modal estimates. It is found that the updated PDF of the modal parameters can be well approximated by a Gaussian distribution centred at the optimal parameters at which the posterior PDF is maximized. Examples using simulated data are presented to illustrate the proposed method. Copyright © 2001 John Wiley & Sons, Ltd.

Some new results on system identification with dynamic neural networks

Abstract: Nonlinear system online identification via dynamic neural networks is studied in this paper. The main contribution of the paper is that the passivity approach is applied to access several new stable properties of neuro identification. The conditions for passivity, stability, asymptotic stability, and input-to-state stability are established in certain senses. We conclude that the gradient descent algorithm for weight adjustment is stable in an L/sub /spl infin// sense and robust to any bounded uncertainties.

Adaptive observer backstepping control using neural networks

Abstract: This paper extends the application of neurocontrol approaches to a new class of nonlinear systems diffeomorphic to output feedback nonlinear systems with unmeasured states. A neural-based adaptive observer is introduced for state estimation as well as system identification using only output measurements during online operation. System identification is achieved via the online approximation of a priori unknown functions. The controller is designed using the backstepping control design procedure. Leakage terms in the adaptive laws and nonlinear damping terms in the backstepping controller are introduced to prevent instability from arising due to the inherent approximation error. A primary benefit of the online function approximation is the reduction of approximation errors, which allows reduction of both the observer and controller gains. A semi-global stability analysis for the proposed approach is provided and the feasibility is investigated by an illustrative simulation example.

Frequency domain blind MIMO system identification based on second- and higher order statistics

Abstract: We present a novel frequency-domain framework for the identification of a multiple-input multiple-output (MIMO) system driven by white, mutually independent, unobservable inputs. The system frequency response is obtained based on singular value decomposition (SVD) of a matrix constructed based on the power-spectrum and slices of polyspectra of the system output. By appropriately selecting the polyspectra slices, we can create a set of such matrices, each of which could independently yield the solution, of they could all be combined in a joint diagonalization scheme to yield a solution with improved statistical performance. The freedom to select the polyspectra slices allows us to bypass the frequency-dependent permutation ambiguity that is usually associated with frequency domain SVD, while at the same time allows us compute and cancel the phase ambiguity. An asymptotic consistency analysis of the system magnitude response estimate is performed.

Adaptive Control of Nonsmooth Dynamic Systems

Abstract: 1. New Models and Identification Methods for Backlash and Gear Play.- 2. Adaptive Dead Zone Inverses for Possibly Nonlinear Control Systems.- 3. Deadzone Compensation in Motion Control Systems Using Augmented Multilayer Neural Networks.- 4. On-line Fault Detection, Diagnosis, Isolation and Accommodation of Dynamical Systems with Actuator Failures.- 5. Adaptive Control of Systems with Actuator Failures.- 6. Multi-mode System Identification.- 7. On Feedback Control of Processes with 'Hard' Nonlinearities.- 8. Adaptive Friction Compensation for Servo Mechanisms.- 9. Relaxed Controls and a Class of Active Material Actuator Models.- 10. Robust Adaptive Control of Nonlinear Systems with Dynamic Backlash-like Hysteresis.- 11. Adaptive Control of a Class of Time-delay Systems in the Presence of Saturation.- 12. Adaptive Control for Systems with Input Constraints - A Survey.- 13. Robust Adaptive Control of Input Rate Constrained Discrete Time Systems.- 14. Adaptive Control of Linear Systems with Poles in the Closed LHP with Constrained Inputs.- 15. Adaptive Control with Input Saturation Constraints.- 16. Adaptive Control of Linear Systems with Unknown Time Delay.

Application of ARMAV models to the identification and damage detection of mechanical and civil engineering structures

Abstract: In this paper, the application of auto-regressive moving average vector models to system identification and damage detection is investigated. These parametric models have already been applied for the analysis of multiple input-output systems under ambient excitation. Their main advantage consists in the capability of extracting modal parameters from the recorded time signals, without the requirement of excitation measurement. The excitation is supposed to be a stationary Gaussian white noise. The method also allows the estimation of modal parameter uncertainties. On the basis of these uncertainties, a statistically based damage detection scheme is performed and it becomes possible to assess whether changes of modal parameters are caused by, e.g. some damage or simply by estimation inaccuracies. The paper reports first an example of identification and damage detection applied to a simulated system under random excitation. The `Steel-Quake' benchmark proposed in the framework of COST Action F3 `Structural Dynamics' is also analysed. This structure was defined by the Joint Research Centre in Ispra (Italy) to test steel building performance during earthquakes. The proposed method gives an excellent identification of frequencies and mode shapes, while damping ratios are estimated with less accuracy.

Testing parameters in structural equation modeling: Every "one" matters.

Abstract: A problem with standard errors estimated by many structural equation modeling programs is described. In such programs, a parameter's standard error is sensitive to how the model is identified (i.e., how scale is set). Alternative but equivalent ways to identify a model may yield different standard errors, and hence different Z tests for a parameter, even though the identifications produce the same overall model fit. This lack of invariance due to model identification creates the possibility that different analysts may reach different conclusions about a parameter's significance level even though they test equivalent models on the same data. The authors suggest that parameters be tested for statistical significance through the likelihood ratio test, which is invariant to the identification choice.

Output-Only Subspace-Based Structural Identification: From Theory to Industrial Testing Practice

Abstract: We address the problem of structural model identification during normal operating conditions and thus with uncontrolled, unmeasured, and nonstationary excitation. We advocate the use of output-only and covariance-driven subspace-based stochastic identification methods. We explain how to handle nonsimultaneously measured data from multiple sensor setups, and how robustness with respect to nonstationary excitation can be achieved. Experimental results obtained for three real application examples are shown.

A wavelet-based approach for model and parameter identification of non-linear systems

Abstract: A procedure is presented for identifying the mechanical parameters of zero-memory non-linear discrete structural systems. The procedure allows both the parameter estimation of a priori known dynamical models as well as the identification of classes of suitable non-linear models based on input–output data. The method relies on a wavelet-based discretization of the non-linear governing differential equation of motion. Orthogonal Daubechies scaling functions are used in the analysis. The scaling functions localization properties permit the tracking of fast variations of the state of the dynamical system which may be associated with unmodeled dynamics of measurement noise. The method is based on the knowledge of measured state variables and excitations and applies to single and multi-degree-of-freedom systems under either free or forced vibrations.

Identification of stable models in subspace identification by using regularization

Abstract: In subspace identification methods, the system matrices are usually estimated by least squares, based on estimated Kalman filter state sequences and the observed inputs and outputs. For a finite number of data points, the estimated system matrix is not guaranteed to be stable, even when the true linear system is known to be stable. In this paper, stability is imposed by using regularization. The regularization term used here is the trace of a matrix which involves the dynamical system matrix and a positive (semi) definite weighting matrix. The amount of regularization can be determined from a generalized eigenvalue problem. The data augmentation method of Chui and Maciejowski (1996) is obtained by using specific choices for the weighting matrix in the regularization term.

Fractional state variable filter for system identification by fractional model

Abstract: This article deals with modeling and identification of fractional systems in the time domain. Fractional state-space representation is defined, and a stability condition for fractional systems given. A new identification method for fractional systems is then proposed. The method is based on the generalization to fractional orders of classical methods based on State Variable Filters (SVF). A particular case of fractional SVF, fractional Poisson filters, is studied. Parameter estimation is then performed, through the conventional least squares method, and then through the instrumental variable method which permits unbiased parameter estimation. Monte Carlo simulations are then performed, using various noise levels, to compare the identification performance of these two methods, and of a prediction error method based on a fractional ARX model.