Unknown Parameters (unknown + parameter)

Distribution by Scientific Domains
Distribution within Engineering

Selected Abstracts

Combining information from different sources to estimate a common effect and use of multiple measurements for ecological assessment

C. R. Rao
A statistical analysis for combining information from different sources to estimate an unknown parameter is outlined. A new index is then proposed as a measure of the quality of the site. Copyright 2004 John Wiley & Sons, Ltd. [source]

Solutions of linear and semilinear distributed parameter equations with a fractional Brownian motion

T. E. Duncan
Abstract In this paper, some linear and semilinear distributed parameter equations (equations in a Hilbert space) with a (cylindrical) fractional Brownian motion are considered. Solutions and sample path properties of these solutions are given for the stochastic distributed parameter equations. The fractional Brownian motions are indexed by the Hurst parameter H,,,(0, 1). For H,=, the process is Brownian motion. Solutions of these linear and semilinear equations are given for each H,,,(0, 1) with the assumptions differing for the cases H,,,(0, ) and H,,,(, 1). For the linear equations, the solutions are mild solutions and limiting Gaussian measures are characterized. For the semilinear equations, the solutions are either mild or weak. The weak solutions are obtained by transforming the measure of the associated linear equation by a Radon,Nikodym derivative (likelihood function). An application to identification is given by obtaining a strongly consistent family of estimators for an unknown parameter in a linear equation with distributed noise or boundary noise. Copyright 2008 John Wiley & Sons, Ltd. [source]

Dual high-gain-based adaptive output-feedback control for a class of nonlinear systems,

P. Krishnamurthy
Abstract We propose an adaptive output-feedback controller for a general class of nonlinear triangular (strict-feedback-like) systems. The design is based on our recent results on a new high-gain control design approach utilizing a dual high-gain observer and controller architecture with a dynamic scaling. The technique provides strong robustness properties and allows the system class to contain unknown functions dependent on all states and involving unknown parameters (with no magnitude bounds required). Unlike our earlier result on this problem where a time-varying design of the high-gain scaling parameter was utilized, the technique proposed here achieves an autonomous dynamic controller by introducing a novel design of the observer, the scaling parameter, and the adaptation parameter. This provides a time-invariant dynamic output-feedback globally asymptotically stabilizing solution for the benchmark open problem proposed in our earlier work with no magnitude bounds or sign information on the unknown parameter being necessary. Copyright 2007 John Wiley & Sons, Ltd. [source]

Simultaneous input and parameter estimation with input observers and set-membership parameter bounding: theory and an automotive application

I. 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]

Composite adaptive and input observer-based approaches to the cylinder flow estimation in spark ignition automotive engines

A. Stotsky
Abstract The performance of air charge estimation algorithms in spark ignition automotive engines can be enhanced using advanced estimation techniques available in the controls literature. This paper illustrates two approaches of this kind that can improve the cylinder flow estimation for gasoline engines without external exhaust gas recirculation (EGR). The first approach is based on an input observer, while the second approach relies on an adaptive estimator. Assuming that the cylinder flow is nominally estimated via a speed-density calculation, and that the uncertainty is additive to the volumetric efficiency, the straightforward application of an input observer provides an easy to implement algorithm that corrects the nominal air flow estimate. The experimental results that we report in the paper point to a sufficiently good transient behaviour of the estimator. The signal quality may deteriorate, however, for extremely fast transients. This motivates the development of an adaptive estimator that relies mostly on the feedforward speed-density calculation during transients, while during engine operation close to steady-state conditions, it relies mostly on the adaptation. In our derivation of the adaptive estimator, the uncertainty is modelled as an unknown parameter multiplying the intake manifold temperature. We use the tracking error between the measured and modelled intake manifold pressure together with an appropriately defined prediction error estimate to develop an adaptation algorithm with improved identifiability and convergence rate. A robustness enhancement, via a ,-modification with the ,-factor depending on the prediction error estimate, ensures that in transients the parameter estimate converges to a pre-determined a priori value. In close to steady-state conditions, the ,-modification is rendered inactive and the evolution of the parameter estimate is determined by both tracking error and prediction error estimate. Further enhancements are made by incorporating a functional dependence of the a priori value on the engine operating conditions such as the intake manifold pressure. The coefficients of this function can be learned during engine operation from the values to which the parameter estimate converges in close to steady-state conditions. This feedforward learning functionality improves transient estimation accuracy and reduces the convergence time of the parameter estimate. Copyright 2004 John Wiley & Sons, Ltd. [source]

The radial basis functions method for identifying an unknown parameter in a parabolic equation with overspecified data

Mehdi Dehghan
Abstract Parabolic partial differential equations with overspecified data play a crucial role in applied mathematics and engineering, as they appear in various engineering models. In this work, the radial basis functions method is used for finding an unknown parameter p(t) in the inverse linear parabolic partial differential equation ut = uxx + p(t)u + ,, in [0,1] (0,T], where u is unknown while the initial condition and boundary conditions are given. Also an additional condition ,01k(x)u(x,t)dx = E(t), 0 , t , T, for known functions E(t), k(x), is given as the integral overspecification over the spatial domain. The main approach is using the radial basis functions method. In this technique the exact solution is found without any mesh generation on the domain of the problem. We also discuss on the case that the overspecified condition is in the form ,0s(t)u(x,t)dx = E(t), 0 < t , T, 0 < s(t) < 1, where s and E are known functions. Some illustrative examples are presented to show efficiency of the proposed method. 2007 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq, 2007 [source]

An optimal investment and consumption model with stochastic returns

Xikui Wang
Abstract We consider a financial market consisting of a risky asset and a riskless one, with a constant or random investment horizon. The interest rate from the riskless asset is constant, but the relative return rate from the risky asset is stochastic with an unknown parameter in its distribution. Following the Bayesian approach, the optimal investment and consumption problem is formulated as a Markov decision process. We incorporate the concept of risk aversion into the model and characterize the optimal strategies for both the power and logarithmic utility functions with a constant relative risk aversion (CRRA). Numerical examples are provided that support the intuition that a higher proportion of investment should be allocated to the risky asset if the mean return rate on the risky asset is higher or the risky asset return rate is less volatile. Copyright 2008 John Wiley & Sons, Ltd. [source]

A Class of Multiplicity Adjusted Tests for Spatial Clustering Based on Case,Control Point Data

BIOMETRICS, Issue 1 2007
Toshiro Tango
Summary A class of tests with quadratic forms for detecting spatial clustering of health events based on case,control point data is proposed. It includes Cuzick and Edwards's test statistic (1990, Journal of theRoyal Statistical Society, Series B52, 73,104). Although they used the property of asymptotic normality of the test statistic, we show that such an approximation is generally poor for moderately large sample sizes. Instead, we suggest a central chi-square distribution as a better approximation to the asymptotic distribution of the test statistic. Furthermore, not only to estimate the optimal value of the unknown parameter on the scale of cluster but also to adjust for multiple testing due to repeating the procedure by changing the parameter value, we propose the minimum of the profile p-value of the test statistic for the parameter as an integrated test statistic. We also provide a statistic to estimate the areas or cases which make large contributions to significant clustering. The proposed methods are illustrated with a data set concerning the locations of cases of childhood leukemia and lymphoma and another on early medieval grave site locations consisting of affected and nonaffected grave sites. [source]

Significance of Modeling Error in Structural Parameter Estimation

Masoud Sanayei
Structural health monitoring systems rely on algorithms to detect potential changes in structural parameters that may be indicative of damage. Parameter-estimation algorithms seek to identify changes in structural parameters by adjusting parameters of an a priori finite-element model of a structure to reconcile its response with a set of measured test data. Modeling error, represented as uncertainty in the parameters of a finite-element model of the structure, curtail capability of parameter estimation to capture the physical behavior of the structure. The performance of four error functions, two stiffness-based and two flexibility-based, is compared in the presence of modeling error in terms of the propagation rate of the modeling error and the quality of the final parameter estimates. Three different types of parameters are used in the parameter estimation procedure: (1) unknown parameters that are to be estimated, (2) known parameters assumed to be accurate, and (3) uncertain parameters that manifest the modeling error and are assumed known and not to be estimated. The significance of modeling error is investigated with respect to excitation and measurement type and locations, the type of error function, location of the uncertain parameter, and the selection of unknown parameters to be estimated. It is illustrated in two examples that the stiffness-based error functions perform significantly better than the corresponding flexibility-based error functions in the presence of modeling error. Additionally, the topology of the structure, excitation and measurement type and locations, and location of the uncertain parameters with respect to the unknown parameters can have a significant impact on the quality of the parameter estimates. Insight into the significance of modeling error and its potential impact on the resulting parameter estimates is presented through analytical and numerical examples using static and modal data. [source]

Two W-containing formate dehydrogenases (CO2 -reductases) involved in syntrophic propionate oxidation by Syntrophobacter fumaroxidans

FEBS JOURNAL, Issue 11 2003
Frank A. M. De Bok
Two formate dehydrogenases (CO2 -reductases) (FDH-1 and FDH-2) were isolated from the syntrophic propionate-oxidizing bacterium Syntrophobacter fumaroxidans. Both enzymes were produced in axenic fumarate-grown cells as well as in cells which were grown syntrophically on propionate with Methanospirillum hungatei as the H2 and formate scavenger. The purified enzymes exhibited extremely high formate-oxidation and CO2 -reduction rates, and low Km values for formate. For the enzyme designated FDH-1, a specific formate oxidation rate of 700 Umg,1 and a Km for formate of 0.04 mm were measured when benzyl viologen was used as an artificial electron acceptor. The enzyme designated FDH-2 oxidized formate with a specific activity of 2700 Umg,1 and a Km of 0.01 mm for formate with benzyl viologen as electron acceptor. The specific CO2 -reduction (to formate) rates measured for FDH-1 and FDH-2, using dithionite-reduced methyl viologen as the electron donor, were 900 Umg,1 and 89 Umg,1, respectively. From gel filtration and polyacrylamide gel electrophoresis it was concluded that FDH-1 is composed of three subunits (89 3, 56 2 and 19 1 kDa) and has a native molecular mass of approximately 350 kDa. FDH-2 appeared to be a heterodimer composed of a 92 3 kDa and a 33 2 kDa subunit. Both enzymes contained tungsten and selenium, while molybdenum was not detected. EPR spectroscopy suggested that FDH-1 contains at least four [2Fe-2S] clusters per molecule and additionally paramagnetically coupled [4Fe-4S] clusters. FDH-2 contains at least two [4Fe-4S] clusters per molecule. As both enzymes are produced under all growth conditions tested, but with differences in levels, expression may depend on unknown parameters. [source]

Optimal measurement of relative and absolute delay times by simulated annealing

S. Chevrot
Summary Conventional approaches to determine relative arrival times of body waves recorded by a local or a regional array rely on cross-correlations between pairs of traces. This problem is better posed as a non-linear inverse problem, which involves the minimization of a cost function that measures the difference between a reference unknown waveform that can be time delayed, and the observed data. The unknown parameters that are solved for, are the amplitude values at each time sample of the optimal waveform and its time delay defined for each trace. The problem, which has a large number of unknown parameters, is solved with simulated annealing. The algorithm is very efficient and can be used for a routine analysis of seismic data. The application of this method to several earthquakes recorded during different PASSCAL experiments demonstrates that it provides accurate and robust differential traveltime measurements even with very noisy data. [source]

Artificial neural network inversion of magnetotelluric data in terms of three-dimensional earth macroparameters

Vjacheslav Spichak
The possibility of solving the three-dimensional (3-D) inverse problem of geoelectrics using the artificial neural network (ANN) approach is investigated. The properties of a supervised ANN based on the back-propagation scheme with three layers of neurons are studied, and the ANN architecture is adjusted. A model class consisting of a dipping dyke in the basement of a two-layer earth with the dyke in contact with the overburden is used for numerical experiments. Six macroparameters of the 3-D model, namely the thickness of the top layer, which coincides with the depth of the dyke (D), the conductivity ratio between the first and second layers (C1,/C2,), the conductivity contrast of the dyke (C/C2,), and the width (W ), length (L ) and dip angle of the dyke (A), are used. Various groups of magnetotelluric field components and their transformations are studied in order to estimate the effect of the data type used on the ANN recognition ability. It is found that use of only the xy - and yx -components of impedance phases results in reasonable recognition errors for all unknown parameters (D: 0.02 per cent, C1/C2: 8.4 per cent, C/C2: 26.8 per cent, W : 0.02 per cent, L : 0.02 per cent, A: 0.24 per cent). The influence of the size and shape of the training data pool (including the ,gaps in education' and ,no target' effects) on the recognition properties is studied. Results from numerous ANN tests demonstrate that the ANN possesses good enough interpolation and extrapolation abilities if the training data pool contains a sufficient number of representative data sets. The effect of noise is estimated by means of mixing the synthetic data with 30, 50 and 100 per cent Gaussian noise. The unusual behaviour of the recognition errors for some of the model parameters when the data become more noisy (in particular, the fact that an increase in error is followed by a decrease) indicates that the use of standard techniques of noise reduction may give an opposite result, so the development of a special noise treatment methodology is required. Thus, it is shown that ANN-based recognition can be successfully used for inversion if the data correspond to the model class familiar to the ANN. No initial guess regarding the parameters of the 3-D target or 1-D layering is required. The ability of the ANN to teach itself using real geophysical (not only electromagnetic) data measured at a given location over a sufficiently long period means that there is the potential to use this approach for interpreting monitoring data. [source]

2D data modelling by electrical resistivity tomography for complex subsurface geology

E. Cardarelli
ABSTRACT A new tool for two-dimensional apparent-resistivity data modelling and inversion is presented. The study is developed according to the idea that the best way to deal with ill-posedness of geoelectrical inverse problems lies in constructing algorithms which allow a flexible control of the physical and mathematical elements involved in the resolution. The forward problem is solved through a finite-difference algorithm, whose main features are a versatile user-defined discretization of the domain and a new approach to the solution of the inverse Fourier transform. The inversion procedure is based on an iterative smoothness-constrained least-squares algorithm. As mentioned, the code is constructed to ensure flexibility in resolution. This is first achieved by starting the inversion from an arbitrarily defined model. In our approach, a Jacobian matrix is calculated at each iteration, using a generalization of Cohn's network sensitivity theorem. Another versatile feature is the issue of introducing a priori information about the solution. Regions of the domain can be constrained to vary between two limits (the lower and upper bounds) by using inequality constraints. A second possibility is to include the starting model in the objective function used to determine an improved estimate of the unknown parameters and to constrain the solution to the above model. Furthermore, the possibility either of defining a discretization of the domain that exactly fits the underground structures or of refining the mesh of the grid certainly leads to more accurate solutions. Control on the mathematical elements in the inversion algorithm is also allowed. The smoothness matrix can be modified in order to penalize roughness in any one direction. An empirical way of assigning the regularization parameter (damping) is defined, but the user can also decide to assign it manually at each iteration. An appropriate tool was constructed with the purpose of handling the inversion results, for example to correct reconstructed models and to check the effects of such changes on the calculated apparent resistivity. Tests on synthetic and real data, in particular in handling indeterminate cases, show that the flexible approach is a good way to build a detailed picture of the prospected area. [source]

A non-linear fin design problem in determining the optimum shape of spine and longitudinal fins

Cheng-Hung Huang
Abstract A non-linear fin design algorithm is applied to estimate the optimum shapes for the spine and longitudinal fins by using the conjugate gradient method (CGM) based on the desired fin efficiency and fin volume. One of the advantages in using CGM in the inverse design problem lies in that it can handle problems having a large number of unknown parameters easily and converges very fast. The validity of the present algorithm by using the CGM to solve the non-linear fin design problem is justified based on numerical experiments. Results show that the optimum fin shapes can always be obtained. Copyright 2003 John Wiley & Sons, Ltd. [source]

Advanced 4-node tetrahedrons

Rong Tian
Abstract Tetrahedral elements are indispensable to complex finite element structural analysis. Two existing and two newly developed advanced 4-node tetrahedrons are studied in this paper. The existing elements that use complicated displacement fields are significantly simplified. The spurious zero-energy modes typical of all these elements are identified to be rigid-body-alike modes and are found to be naturally suppressible, making it possible to avoid any stabilization techniques and unknown parameters in formulation. Through the simplified form, we connect these four tetrahedrons and view them in a general framework of the partition-of-unity-based approximation. This general view allows us to reveal many promising features of the newly developed tetrahedrons by comparing them with their existing counterparts: the newly developed tetrahedrons have straightforward formulation, no unsuppressed zero-energy modes, no stabilization required, no unknown parameters contained, and a high consistency in implementation, in addition to good accuracy and extremely straightforward mesh generation. Copyright 2006 John Wiley & Sons, Ltd. [source]

A dynamic approach for evaluating parameters in a numerical method

A. A. Oberai
Abstract A new methodology for evaluating unknown parameters in a numerical method for solving a partial differential equation is developed. The main result is the identification of a functional form for the parameters which is derived by requiring the numerical method to yield ,optimal' solutions over a set of finite-dimensional function spaces. The functional depends upon the numerical solution, the forcing function, the set of function spaces, and the definition of the optimal solution. It does not require exact or approximate analytical solutions of the continuous problem, and is derived from an extension of the variational Germano identity. This methodology is applied to the one-dimensional, linear advection,diffusion problem to yield a non-linear dynamic diffusivity method. It is found that this method yields results that are commensurate to the SUPG method. The same methodology is then used to evaluate the Smagorinsky eddy viscosity for the large eddy simulation of the decay of homogeneous isotropic turbulence in three dimensions. In this case the resulting method is found to be more accurate than the constant-coefficient and the traditional dynamic versions of the Smagorinsky model. Copyright 2004 John Wiley & Sons, Ltd. [source]

Neural network-based adaptive attitude tracking control for flexible spacecraft with unknown high-frequency gain

Qinglei Hu
Abstract Adaptive control design using neural networks (a) is investigated for attitude tracking and vibration stabilization of a flexible spacecraft, which is operated at highly nonlinear dynamic regimes. The spacecraft considered consists of a rigid body and two flexible appendages, and it is assumed that the system parameters are unknown and the truncated model of the spacecraft has finite but arbitrary dimension as well, for the purpose of design. Based on this nonlinear model, the derivation of an adaptive control law using neural networks (NNs) is treated, when the dynamics of unstructured and state-dependent nonlinear function are completely unknown. A radial basis function network that is used here for synthesizing the controller and adaptive mechanisms is derived for adjusting the parameters of the network and estimating the unknown parameters. In this derivation, the Nussbaum gain technique is also employed to relax the sign assumption for the high-frequency gain for the neural adaptive control. Moreover, systematic design procedure is developed for the synthesis of adaptive NN tracking control with L2 -gain performance. The resulting closed-loop system is proven to be globally stable by Lyapunov's theory and the effect of the external disturbances and elastic vibrations on the tracking error can be attenuated to the prescribed level by appropriately choosing the design parameters. Numerical simulations are performed to show that attitude tracking control and vibration suppression are accomplished in spite of the presence of disturbance torque/parameter uncertainty. Copyright 2009 John Wiley & Sons, Ltd. [source]

Robust stabilization of a class of non-minimum-phase nonlinear systems in a generalized output feedback canonical form

Jun Fu
Abstract In this paper, a globally robust stabilizer for a class of uncertain non-minimum-phase nonlinear systems in generalized output feedback canonical form is designed. The system contains unknown parameters multiplied by output-dependent nonlinearities and output-dependent nonlinearities enter such a system both additively and multiplicatively. The proposed method relies on a recently developed novel parameter estimator and state observer design methodology together with a combination of backstepping and small-gain approach. Our design has three distinct features. First, the parameter estimator and state observer do not necessarily follow the classical certainty-equivalent principle any more. Second, the design treats unknown parameters and unmeasured states in a unified way. Third, the technique by combining standard backstepping and small-gain theorem ensures robustness with respect to dynamic uncertainties. Finally, two numerical examples are given to show that the proposed method is effective, and that it can be applied to more general systems that do not satisfy the cascading upper diagonal dominance conditions developed in recent papers, respectively. Copyright 2008 John Wiley & Sons, Ltd. [source]

Passivity-based sliding mode control for nonlinear systems

Ali J. Koshkouei
Abstract Passivity with sliding mode control for a class of nonlinear systems with and without unknown parameters is considered in this paper. In fact, a method for deriving a nonlinear system with external disturbances to a passive system is considered. Then a passive sliding mode control is designed corresponding to a given storage function. The passivity property guarantees the system stability while sliding mode control techniques assures the robustness of the proposed controller. When the system includes unknown parameters, an appropriate updated law is obtained so that the new transformed system is passive. The passivation property of linear systems with sliding mode is also analysed. The linear and nonlinear theories are applied to a simple pendulum model and the gravity-flow/pipeline system, respectively. Copyright 2008 John Wiley & Sons, Ltd. [source]

Dual high-gain-based adaptive output-feedback control for a class of nonlinear systems,

P. Krishnamurthy
Abstract We propose an adaptive output-feedback controller for a general class of nonlinear triangular (strict-feedback-like) systems. The design is based on our recent results on a new high-gain control design approach utilizing a dual high-gain observer and controller architecture with a dynamic scaling. The technique provides strong robustness properties and allows the system class to contain unknown functions dependent on all states and involving unknown parameters (with no magnitude bounds required). Unlike our earlier result on this problem where a time-varying design of the high-gain scaling parameter was utilized, the technique proposed here achieves an autonomous dynamic controller by introducing a novel design of the observer, the scaling parameter, and the adaptation parameter. This provides a time-invariant dynamic output-feedback globally asymptotically stabilizing solution for the benchmark open problem proposed in our earlier work with no magnitude bounds or sign information on the unknown parameter being necessary. Copyright 2007 John Wiley & Sons, Ltd. [source]

Adaptive unknown input observer approach for aircraft actuator fault detection and isolation

Dan Wang
Abstract In this paper, an adaptive unknown input observer (UIO) approach is developed to detect and isolate aircraft actuator faults. In a multiple-model scheme, a bank of parallel observers are constructed, each of which is based on a model that describes the system in the presence of a particular actuator fault. The observers are constructed based on a modified form of the standard UIO to generate fault-dependant residual signals, such that when a model matches the system, the residual signal will be zero. Otherwise, the residual will be definitely non-zero and governed uniquely by the faulty signal. For locked actuators and loss of actuator effectiveness, in which the locked position and the reduced effectiveness are additional unknowns, we develop an adaptive scheme to estimate these unknown parameters. To the best of our knowledge, this is the first adaptive UIO presented. We prove that the proposed adaptive algorithms guarantee that both the residual signals and the estimation errors of the unknown parameters converge exponentially when a model matches the plant. By further designing a model-matching index, the fault can be isolated accurately. A condition for the approach is that for an nth order system, there must be n independent measurements available. This requirement limits the applicability of our proposed approach. The condition is certainly satisfied by all state-feedback control systems. However, for some other systems, extra efforts may be needed to increase the number of measurements. The method is applied to a linear model of the F-16 aircraft with controller. The results show that the approach is effective. Copyright 2006 John Wiley & Sons, Ltd. [source]

Model reference adaptive iterative learning control for linear systems

A. Tayebi
Abstract In this paper, we propose a model reference adaptive control (MRAC) strategy for continuous-time single-input single-output (SISO) linear time-invariant (LTI) systems with unknown parameters, performing repetitive tasks. This is achieved through the introduction of a discrete-type parametric adaptation law in the ,iteration domain', which is directly obtained from the continuous-time parametric adaptation law used in standard MRAC schemes. In fact, at the first iteration, we apply a standard MRAC to the system under consideration, while for the subsequent iterations, the parameters are appropriately updated along the iteration-axis, in order to enhance the tracking performance from iteration to iteration. This approach is referred to as the model reference adaptive iterative learning control (MRAILC). In the case of systems with relative degree one, we obtain a pointwise convergence of the tracking error to zero, over the whole finite time interval, when the number of iterations tends to infinity. In the general case, i.e. systems with arbitrary relative degree, we show that the tracking error converges to a prescribed small domain around zero, over the whole finite time interval, when the number of iterations tends to infinity. It is worth noting that this approach allows: (1) to extend existing MRAC schemes, in a straightforward manner, to repetitive systems; (2) to avoid the use of the output time derivatives, which are generally required in traditional iterative learning control (ILC) strategies dealing with systems with high relative degree; (3) to handle systems with multiple tracking objectives (i.e. the desired trajectory can be iteration-varying). Finally, simulation results are carried out to support the theoretical development. Copyright 2006 John Wiley & Sons, Ltd. [source]

On parameter estimation for excitation control of synchronous generators

Martha Galaz
Abstract This paper deals with the problem of identification of the network parameters and the desired equilibrium in applications of excitation control for synchronous generators. Our main contribution is the construction of a new non-linear identifier that provides asymptotically consistent estimates (with guaranteed transient bounds) of the line impedance and the equilibrium for the classical three-dimensional flux-decay model of a single generator connected to an infinite bus. This model is non-linear, and non-linearly parameterized, and the equilibria depend also non-linearly on the unknown parameters. The proposed estimator can be used, adopting a certainty equivalent approach, to make adaptive any power system stabilizer that relies on the knowledge of these parameters. The behaviour of the scheme is illustrated in two simulated case studies with the interconnection and damping assignment passivity-based controller recently proposed by the authors. Copyright 2004 John Wiley & Sons, Ltd. [source]

Adaptive tracking control for electrically-driven robots without overparametrization

Yeong-Chan Chang
Abstract This paper addresses the motion tracking control of robot systems actuated by brushed direct current motors in the presence of parametric uncertainties and external disturbances. By using the integrator backstepping technique, two kinds of adaptive control schemes are developed: one requires the measurements of link position, link velocity and armature current for feedback and the other requires only the measurements of link position and armature current for feedback. The developed adaptive controllers guarantee that the resulting closed-loop system is locally stable, all the states and signals are bounded, and the tracking error can be made as small as possible. The attraction region can be not only arbitrarily preassigned but also explicitly constructed. The main novelty of the developed adaptive control laws is that the number of parameter estimates is exactly equal to the number of unknown parameters throughout the entire electromechanical system. Consequently, the phenomenon of overparametrization, a significant drawback of employing the integrator backstepping technique to treat the control of electrically driven robots in the previous literature, is eliminated in this study. Finally, simulation examples are given to illustrate the tracking performance of electrically driven robot manipulators with the developed adaptive control schemes. Copyright 2002 John Wiley & Sons, Ltd. [source]

Identification and adaptive control of some stochastic distributed parameter systems

B. Pasik-Duncan
Abstract An important class of controlled linear stochastic distributed parameter systems is that with boundary or point control. A survey of some existing adaptive control problems with their solutions for the boundary or the point control of a partially known linear stochastic distributed parameter systems is presented. The distributed parameter system is described by an analytic semigroup with cylindrical white noise and a control that occurs only on the boundary or at discrete points. The unknown parameters in the model appear affinely in both the infinitesimal generator of the semigroup and the linear transformation of the control. The noise in the system is a cylindrical white Gaussian noise. Strong consistency is verified for a family of least-squares estimates of the unknown parameters. For a quadratic cost functional of the state and the control, the certainty equivalence control is self-optimizing, that is the family of average costs converges to the optimal ergodic cost. Copyright 2001 John Wiley & Sons, Ltd. [source]

Adaptive synchronization of GLHS with unknown parameters

Yan-Wu Wang
Abstract In this paper, an adaptive controller for the synchronization of two generalized Lorenz hyperchaos systems (GLHSs) is designed by using the Lyapunov method. In the synchronization schema, the parameters of the drive system are unknown and different from those of the response system. By introducing update laws for both the control coefficients and the parameters of the response system, the adaptive controller proposed in this paper is brand new compared with the former relative works. The proposed adaptive controller is feasible for any possible parameters of GLHS. Numerical simulation is carried out to verify and illustrate the analytical result. Copyright 2008 John Wiley & Sons, Ltd. [source]

Using symbolic computing in building probabilistic models for atoms

Silviu Guiasu
Abstract This article shows how symbolic computing and the mathematical formalism induced by maximizing entropy and minimizing the mean deviation from statistical equilibrium may be effectively applied to obtaining probabilistic models for the structure of atoms, using trial wave functions compatible with an average shell picture of the atom. The objective is not only to recover the experimental value of the ground state mean energy of the atom, but rather to better approximate the unknown parameters of these trial functions and to calculate both correlations between electrons and the amount of interdependence among different subsets of electrons of the atoms. The examples and numerical results refer to the hydrogen, helium, lithium, and beryllium atoms. The main computer programs, using the symbolic computing software MATHEMATICA, are also given. 2005 Wiley Periodicals, Inc. Int J Quantum Chem, 2006 [source]

Kalman filter-based adaptive control for networked systems with unknown parameters and randomly missing outputs

Y. Shi
Abstract This paper investigates the problem of adaptive control for networked control systems with unknown model parameters and randomly missing outputs. In particular, for a system with the autoregressive model with exogenous input placed in a network environment, the randomly missing output feature is modeled as a Bernoulli process. Then, an output estimator is designed to online estimate the missing output measurements, and further a Kalman filter-based method is proposed for parameter estimation. Based on the estimated output and the available output, and the estimated model parameters, an adaptive control is designed to make the output track the desired signal. Convergence properties of the proposed algorithms are analyzed in detail. Simulation examples illustrate the effectiveness of the proposed method. Copyright 2008 John Wiley & Sons, Ltd. [source]

Adaptive regulation of cascade systems with nonlinear parameterization

Wei Lin
Abstract This article provides a solution to the problem of global adaptive regulation, for a class of nonlinearly parameterized cascade systems including feedback linearizable and minimum-phase systems with nonlinear parameterization. The solution is derived by using a novel parameter separation technique combined with a feedback domination design. We remove all the restrictive conditions previously imposed on the unknown parameters, such as linear parameterization or convex/concave parameterization conditions, which have been commonly assumed so far in the literature of nonlinear adaptive control. Copyright 2002 John Wiley & Sons, Ltd. [source]

Bayesian inference in a piecewise Weibull proportional hazards model with unknown change points

J. Casellas
Summary The main difference between parametric and non-parametric survival analyses relies on model flexibility. Parametric models have been suggested as preferable because of their lower programming needs although they generally suffer from a reduced flexibility to fit field data. In this sense, parametric survival functions can be redefined as piecewise survival functions whose slopes change at given points. It substantially increases the flexibility of the parametric survival model. Unfortunately, we lack accurate methods to establish a required number of change points and their position within the time space. In this study, a Weibull survival model with a piecewise baseline hazard function was developed, with change points included as unknown parameters in the model. Concretely, a Weibull log-normal animal frailty model was assumed, and it was solved with a Bayesian approach. The required fully conditional posterior distributions were derived. During the sampling process, all the parameters in the model were updated using a Metropolis,Hastings step, with the exception of the genetic variance that was updated with a standard Gibbs sampler. This methodology was tested with simulated data sets, each one analysed through several models with different number of change points. The models were compared with the Deviance Information Criterion, with appealing results. Simulation results showed that the estimated marginal posterior distributions covered well and placed high density to the true parameter values used in the simulation data. Moreover, results showed that the piecewise baseline hazard function could appropriately fit survival data, as well as other smooth distributions, with a reduced number of change points. [source]