bluuebunny/crossref_metadata_2025_split huggingface dataset:
Universal iterative learning control using adaptive high‐gain feedback
Authors: Owens David H. 
Published: July 2007

AbstractConvergence theorems for adaptive (universal) iterative learning control systems provide a well‐defined convergence criterion parametrized by a single adaptive gain parameter. The convergence is in the weak topology of L(0,T) with T finite and applies to both finite‐dimensional systems and a class of infinite‐dimensional systems.
An adaptive force/position regulator for robot manipulators
Authors: Siciliano Bruno , Villani Luigi 
Published: July 2007

AbstractAn adaptive force/position regulator for robot manipulators in contact with an elastically compliant surface is proposed in this paper. The controller consists of a PD action on the position loop, a PI action on the force loop, together with estimated gravity compensation and desired contact force feedforward. The closed‐loop system is shown to have a unique equilibrium state and asymptotic stability in the neighbourhood of this state is proven via the classical Lyapunov method. To cope with imperfect gravity compensation, the controller is made adaptive with respect to a suitable set of parameters in the gravity term. Numerical case studies are developed for a three‐joint elbow manipulator.
Learning gravity compensation in robots: Rigid arms, elastic joints, flexible links
Authors: Luca Alessandro De , Panzieri Stefano 
Published: July 2007

AbstractThe setpoint regulation problem for robotic manipulators is a basic task that can be solved either by PID control or by model‐based gravity compensation. These approaches are commonly applied both to rigid arms and to robots with flexible links and/or elastic joints. However, PID control requires fine and lengthy tuning of gains in order to achieve good performance over the whole workspace. Moreover, no global convergence proof has yet been given for this control law in the case of flexible links or elastic joints. On the other hand, a constant or even a configuration‐dependent gravity compensation is only an approximate solution when an unknown payload is present or when model parameters are poorly estimated. In this paper a simple iterative scheme is proposed for generating exact gravity compensation at the desired setpoint without the knowledge of dynamic model terms. The resulting control law is shown to be global asymptotically stable for rigid arms as well as for manipulators with elastic joints or flexible links. Starting with a PD action on the error at the joint (i.e. motor) level, an additional feedforward term is built and updated at discrete instants. Convergence of the scheme is proved under a mild condition on the proportional gain, related to a bound on the gravity terms. In the presence of concentrated or distributed flexibility a structural property of the joint or of the link stiffness is further required, largely satisfied in practice. Simulation results are given for a three‐link rigid arm and experimental results are also presented for a two‐link robot with a flexible forearm.
Adaptive trajectory/force control scheme for constrained robot manipulators
Authors: Panteley E. V. , Stotsky A. A. 
Published: July 2007

AbstractAn adaptive control scheme for the trajectory/force tracking of robot manipulators is presented. Asymptotic stability of state variables and convergence of constraint forces to any prespecified set are proven. The design procedure avoids the restrictive solvability condition of the constraint equation in the whole space of robot co‐ordinates. A fairly accurate bound of the tracking error is derived by means of a Lyapunov analysis.
Blind adaptation of decision feedback equalizers: Gross convergence properties
Authors: Kennedy Rodney A. , Anderson Brian D. O. , Bitmead Robert R. 
Published: July 2007

AbstractAn analysis of the stochastic dynamics of the blind adaptation of decision feedback equalizers is presented. The analysis accounts for the presence of decision errors which, under feedback, are propagated. A number of blind algorithms are presented and a theory is developed to explain gross convergence properties observed through simulations. The possibility of and mechanism behind undesirable local minima are highlighted and a detailed case study is given. The potential capture by local minima shows the importance of good initialization. These results superficially resemble those obtained for blind adaptation applied to linear equalizers.
Performance analysis of the forgetting factor RLS algorithm
Authors: Guo Lei , Ljung Lennart , Priouret Pierre 
Published: July 2007

AbstractAn analysis is given of the performance of the standard forgetting factor recursive least squares (RLS) algorithm when used for tracking time‐varying linear regression models. Three basic results are obtained: (1) the ‘P‐matrix’ in the algorithm remains bounded if and only if the (time‐varying) covariance matrix of the regressors is uniformly non‐singular; (2) if so, the parameter tracking error covariance matrix is of the order O(μ + γ2/μ), where μ = 1 ‐ λ, λ is the forgetting factor and γ is a quantity reflecting the speed of the parameter variations; (3) this covariance matrix can be arbitrarily well approximated (for small enough μ) by an expression that is easy to compute.
An improved variable structure model‐following control for robotic manipulators
Authors: Zhihong Man , Palaniswami M. 
Published: July 2007

AbstractAn improved variable structure model‐following control scheme for rigid robotic manipulators is proposed in this paper. It is shown that the controller parameters are determined by only a few uncertain system matrix bounds. The proposed scheme results in greatly simplified controller design; furthermore, strong robustness and asymptotic convergence can be guaranteed. It is shown that this scheme can be easily used for practical applications.
Guranteed state estimation for dynamical systems: Ellipsoidal techniques
Authors: Kurzhanski A. B. , Sugimoto K. , Vályi I. 
Published: July 2007

AbstractThis paper gives a concise description of effective solutions to the guaranteed state estimation problems for dynamic systems with uncertain items being unknown but bounded. It indicates a rather unconventional, rigorous theory for these problems based on the notion of evolution equations of the ‘funnel’ type which could be further transformed ‐ through exact ellipsoidal representations ‐ into algorithmic procedures that allow effective simulation, particularly with computer graphics. the estimation problem is also interpreted as a problem of tracking a partially known system under incomplete measurements.Mathematically, the technique described in this paper is based on a theory of set‐valued evolution equations with the approximation of solutions formulated in terms of set‐valued calculus by ellipsoidal‐valued functions.
Optimization of stochastic terminal control systems adapting to changes in disturbance characteristics
Authors: Petrov A. I. , Stetsko G. O. 
Published: July 2007

AbstractThe optimization problem of adaptive stochastic terminal control systems accommodating to changing disturbance characteristics is considered. It is assumed that a plant with randomly changing or fixed structure is operated under non‐Gaussian disturbances described by non‐linear models. Direct information about random structural changes of the disturbance model is absent. For systems with various types of structural changes the optimal adaptive terminal control laws are obtained in analytical form. Filtering‐prediction‐recognition algorithms are designed for realization of these control laws. It is found that the synthesized regulators provide a disturbance accommodation control strategy with adaptation to structural changes of the disturbance model. A principal difference from traditional control laws is illustrated by simulation studies.
Local robust stability of mrac under persistent excitation assumption
Authors: Janecki Dariusz 
Published: July 2007

AbstractIn this paper the local robust stability of model reference adaptive control for time‐varying plants with unmodelled dynamics under the persistent excitation condition is studied. It is shown that for arbitrary initial conditions of the adaptive system all signals in the adaptive loop are bounded if the command input is persistently exciting and the plant parameter variation and the magnitude of the unmodelled component are sufficiently small in the mean.
Asymptotic performance guarantees in adaptive control
Authors: Tsakalis Kostas S. , Limanond Suttipan 
Published: July 2007

AbstractIn this paper we address the problem of improving the asymptotic performance guarantees of a class of model reference adaptive controllers under insufficient excitation and in the presence of perturbations such as non‐parametric uncertainty and bounded disturbances. We show that for a class of perturbations whose normalized effect is bounded by an a priori known function of time an estimate of the parametric uncertainty set can be obtained on‐line via a set membership estimator. Furthermore, by incorporating this estimate as an additional constraint in the adaptive law generating the controller parameters, we show that the resulting adaptive controller offers dead‐zone‐like performance guarantees in the lim‐sup sense while preserving the desirable root‐mean‐square performance guarantees of gradient‐based algorithms.
Computationally efficient high‐order yule‐walker frequency estimation
Authors: Kangas A. , Stoica P. 
Published: July 2007

AbstractThis paper introduces a novel high‐order Yule‐Walker (HOYW) method for estimating sinusoidal frequencies. the proposed method does not require singular value decomposition (SVD) of the data covariance matrix, has a computational complexity similar to the overdetermined Yule‐Walker (OYW) method and a statistical accuracy close to that of the SVD‐based HOYW method, and is easy to implement in recursive form since it involves only linear operations on the data. an example of the performance that can be achieved by an adaptive form of this new HOWY method in a time‐varying scenario is also given.
The application of artificial intelligence techniques for intelligent control of dynamical physical systems
Authors: Kumar V. R. , Mani N. 
Published: July 2007

AbstractThere is an increasing concern among the scientific community and industrialists over the safe and reliable operation of control systems in industries. Although several adaptive control techniques have been introduced, they are not robust enough for many real‐world problem domains where the degree of uncertainty is high and therefore classical methods of mathematical modelling and control fail. Computer technology has reached a point wheremachine intelligencecan be incorporated in many of the systems that we use daily. Changes in environments, unmeasurable disturbances, changing reference models and performance criteria and component failures are some of the characteristics which necessitate intelligent control. the developments in the field of artificial intelligence have reached a stage which will help to reduce these control complexities by incorporatingintelligenceinto the control systems. In this paper we explain various artificial intelligence techniques that can be used to control dynamical physical systems.
An HMM approach to adaptive demodulation of QAM signals in fading channels
Authors: Collings I. B. , Moore J. B. 
Published: July 2007

AbstractIn this paper the techniques of extended Kalman filtering (EKF) and hidden Markov model (HMM) signal processing are combined to adaptively demodulate quadrature amplitude‐modulated (QAM) signals in noisy fading channels. This HMM approach is particularly suited to signals for which the message symbols are not equally probable, as is the case with many types of coded signals. Our approach is to formulate the QAM signal by a finite‐discrete state process and represent the channel model by a continuous state process. the mixed state model is then reformulated in terms of conditional information states using HMM theory. This leads to models which are amenable to standard EKF or related techniques. A sophisticated EKF scheme with an HMM subfilter is discussed, as well as more practical schemes coupling discrete state HMM filters and continuous state Kalman filters. the case of white noise is considered, as well as generalizations to cope with coloured noise. Simulation studies demonstrate the improvement gained over standard schemes.
Adaptive stabilization for continuous time systems with disturbances
Authors: Zhang J. F. , Chen H. F. 
Published: July 2007

AbstractThis paper concerns adaptive stabilization for single‐input/single‐output (SISO) continuous time systems with unknown coefficients and containing stochastic or deterministic disturbances. the conditions used here are possibly the weakest: neither the positive realness condition nor the availability of the upper bound of system disturbances is needed; the only condition imposed on the system structure is stabilizability, which is necessary for stabilizing a system even in the case where the system coefficients are known. the adaptive control given in the paper is switched at stopping times either on external excitations or on certainty equivalence controls defined by the pole assignment method at fixed times. It is shown that after a finite period of time the external excitation is no longer used and the system is stabilized in the long run average sense.
Approximating linear functionals on unitary spaces in the presence of bounded data errors with applications to signal recovery
Authors: Kacewicz Bolesław Z. , Kowalski Marek A. 
Published: July 2007

AbstractThe paper deals with estimating linear continuous functionals on unitary spaces from inaccurate information, with applications to recovering band‐limited signals. We show how the minimal estimation error depends on a bound on data perturbations and specify the form of an optimal algorithm.
Identification by parameter bounds in adaptive control
Authors: Veres Sandor M. 
Published: July 2007

AbstractApproximate solutions are suggested for receding horizon dual control to guarantee acceptable control performance of a plant with large a priori parameter uncertainties under poor excitation by the output reference and to satisfy the requirement of very fast adaptation using knowledge available on sensor and performance in industrial applications of adaptive control. the aim of the paper is to present several levels of interaction between on‐line identification and control performance using parameter bounds. an interesting theorem shows that parameter bounding is a necessary part of the solution of the dual control problem. Starting from the complete separation of identification and control, various approximations are presented at different levels of optimality. Finally, the exact solution of the dual control problem is found for static gain adaptation, which implicitly involves a parameter‐bounding identification procedure.
On bounded‐error identification of feedback systems
Authors: Mäkilä P. M. , Partington J. R. 
Published: July 2007

AbstractThis paper studies identification of linear feedback systems from closed loop time series. Unfalsified approximate bounded error identification is shown to result in a control‐relevant identification methodology for robustness optimization under BIBO‐stable coprime factor uncertainty. Furthermore, well‐posedness (continuity) of the robustness optimization method for controller synthesis is established.
l2projection in bounded‐error estimation
Authors: Keesman Karel J. 
Published: July 2007

AbstractThe problem of parameter estimation from bounded‐error data is considered. In particular, the possibilities of using an l2projection (least squares) procedure are explored. Exact as well as approximate (polytopic) outer‐bounding solutions are proposed. the main properties and difficulties in implementation of the l2projection procedures are discussed and illustrated. Since these methods appear to be computationally cumbersome when the number of measurements is large, a simpler (rectangular) characterization of the solution set, as a result of natural interval calculations, is proposed and discussed.
Rapprochement between bounded‐error and stochastic estimation theory
Authors: Ninness Brett M. , Goodwin Graham C. 
Published: July 2007

AbstractThere has been a recent surge of interest in estimation theory based on very simple noise descriptions; for example, the absolute value of a noise sample is simply bounded. to date, this line of work has not been critically compared with pre‐existing work on stochastic estimation theory which uses more complicated noise descriptions. the present paper attempts to redress this by examining the rapprochement between the two schools of work. For example, we show that for many problems a bounded‐error estimation approach is precisely equivalent in terms of the final result to the stochastic approach of Bayesian estimation. We also show that in spite of having the advantages of being simple and intuitive, bounded‐error estimation theory is demanding on the quantitative accuracy of prior information. In contrast, we discuss how the assumptions underlying stochastic estimation theory are more complex but have the key feature that qualitative assumptions on the nature of a typical disturbance sequence can be made to reduce the importance of quantitative assumptions being correct. We also discuss how stochastic theory can be extended to deal with a problem at present tackled only by bounded‐error estimation methods: the quantification of estimation errors arising from the presence of undermodelling.
Adaptive control of non‐minimum phase systems
Authors: Campi Marco C. 
Published: July 2007

AbstractAdaptive control schemes based on the recursive least squares (RLS) estimation technique are analysed in this paper. First, under the assumption that the asymptotically estimated system does not present pole‐zero cancellations, a general stability result is proven. Such a result does not require the system to be minimum phase. Then it is shown that on the grounds of the stability property the control system attains asymptotic optimality.The results of the present paper rely on previous work by Rootzen and Sternby concerning the consistency of RLS estimates and are intended as a first step in the extension of Kumar's work in adaptive control to non‐minimum phase systems.
Self‐tuning Smith predictors for processes with long dead time
Authors: Hang Chang‐Chieh , Wang Qingguo , Cao Li‐Sheng 
Published: July 2007

AbstractA simple relay feedback auto‐tuning method is proposed for the Smith predictor, an advanced controller for processes with long dead time. the relay feedback control gives information on one point of the Nyquist curve in terms of ultimate gain and frequency. With an additional measurement of the static gain, a reduced order process model in terms of a first‐ or second‐order dynamics plus dead time could be computed and used to auto‐tune the Smith predictor. When the process dynamics changes more frequently, a self‐tuning controller is required. In this case the Fast Fourier Transform technique can be further employed to track the points on the Nyquist curve by estimating the process frequency response and then used to update the Smith predictor. Excellent performance of the auto‐tuned and self‐tuned Smith predictor has been substantiated by simulations.
Effects of dead‐zone identification in robust self‐tuning cheap control
Authors: Borrelli Donato , Mosca Edoardo 
Published: July 2007

AbstractIn this paper we deal with adaptive algorithms based on cheap control in the presence of neglected dynamics. Convergence analysis is carried out for a relative dead‐zone of both discontinuous and continuous type with the aim of pointing out in an explicit fashion the main differences related to these two facilities.
The autocalibration of PI controllers based on two frequency measurements
Authors: Voda A. , Landau I. D. 
Published: July 2007

AbstractA generalization of the previously presented KLV method for the autocalibration of PI controllers (based on one frequency experiment) is realized so as to extend its validity domain. The underlying process models and PI designs are given. The new autocalibration procedure includes two frequency experiments on the plant (closed‐loop relay experiments) to extract the process dynamics. Comparisons with other tuning methods are presented. Simulations and experiments on a real plant illustrate the feasibility of the proposed approach.
Tools for semiphysical modelling
Authors: Lindskog P. , Ljung L. 
Published: July 2007

AbstractSemiphysical modelling is often interpreted as an application of system identification where physical insight into the application is used to come up with suitable non‐linear transformations of the raw measurements so as to allow for a good model structure. This modelling procedure is less ‘ambitious’ than those used for traditional physical modelling in that no complete physical structure is sought, just suitable inputs and outputs that can be subjected to more or less standard model structures such as linear regressions. In this paper we discuss a semiphysical modelling procedure and various tools supporting it. These include constructive algorithms originating from commutative and differential algebra as well as more informal tools such as the programming environment.
Recursive approximation by ARX model: A tool for grey box modelling
Authors: Kárný Miroslav , Halousková Alena , Nedoma Petr 
Published: July 2007

AbstractThe presented procedure computes approximate probabilistic models of complex dynamic phenomena recursively with respect to an increasing amount of observed evidence. Measured, fictitious as well as simulated data can be used in combination for obtaining a reasonably conservative approximate model. Thus information from a number of sources can be systematically merged using a refinement of the recently proposed method of Bayesian pooling of imprecise opinions from a variety of experts. It can be applied recursively as the number of treated items grows.The procedure provides (i) a new tool needed for grey as well as black box modelling, (ii) a novel adaptation of probabilistic models and (iii) an approximation of a given model by a simpler one.The general procedure is applied to the autoregressive model with exogenous variables (AM). This example illustrates the adopted approach and conmbutes to the solution of the following tasks: (i) estimation of an appropriate model structure; (ii) incorporation of prior knowledge into the initial conditions of recursive least squares; (iii) construction of a reference for an advanced forgetting technique; (iv) approximation of a complex analytic/simulation model by an ARX model.The behaviour of the procedure is illustrated on typical examples.
Alice in Technoland
Authors: Bullivant Lucy 
Published: July 2007

AbstractAt their most supernatural, interactive design environments can have a transformative effect. They take the visitor to somewhere else. By actively involving the public they are both ‘porous’ and ‘responsive’, beckoning us like the rabbit in Alice in Wonderland to enter and participate in another world. Here Lucy Bullivant kicks off her introduction to this issue of AD by looking at an installation designed by Daan Roosegaarde for the Netherlands Media Art Institute in Amsterdam which epitomises this approach. Copyright © 2007 John Wiley & Sons, Ltd.
Beyond the Kiosk and the Billboard
Authors: Bullivant Lucy 
Published: July 2007

AbstractThe proliferation of commercial billboards and electronic signage in New York's Manhattan limits the possibility for interactive art sites. Lucy Bullivant describes how Eskyiu transformed an HSBC bank in Chinatown's Canal Street with an interactive facade and Antenna Design have created their Civic Exchange project for a site in Battery Park City. Copyright © 2007 John Wiley & Sons, Ltd.