bluuebunny/crossref_metadata_2025 huggingface dataset:
New results on the structure of non‐linear systems: Application to the (dynamic) block‐decoupling problem
Authors: Descusse J.
Published: July 2007

In this paper our aim is twofold. Firstly, we briefly recall the solution to the dynamic block‐decoupling problem (DBDP) we presented at ‘NOLCOS 89’. This solution generalizes the approach taken for solving Morgan's problem, i.e. the row‐by‐row decoupling problem, by Descusse and Moog. The results we develop here are strongly connected with the structure algorithm of Hirschorn. Using the latter it is possible to exhibit particular (degenerate) controlled invariant distributions which appear to be crucial in the solution of the considered problem. The solution goes through an algorithmic procedure. Secondly, we give a new structure algorithm which makes possible the computation of the aforementioned distributions as well as the differential output rank of the considered system, which we must know for solving the DBDP. It is well known that the differential output rank, and more generally the structure at infinity, can be computed from the structure algorithm of Singh but not from the structure algorithm of Hirschorn. The former provides algebraic quantities (the structure at infinity) while the latter is mainly of interest for knowing geometric quantities (controlled invariant distributions). Our structure algorithm provides both features simultaneously.
Some remarks on the convergence analysis of adaptive controllers for robot manipulators
Authors: Maliotis G. N. , Dawson D. M. , Lewis F. L.
Published: July 2007

The objective of this paper is to explore several robust adaptive control schemes for a class of partially known robot systems described by the equationM(q)q̈ + F(q, q̇)q̇ + G(q) = f(t) wheref(t) is the control input. First we take advantage of any known system dynamics to simplify the adaptive control problem for the unknown portion of the dynamics. Then a model‐reference adaptive control based on the Lyapunov stability criterion is designed for the remaining unknown portion of the plant. Using the Lyapunov approach, under a slowly time‐varying assumption we derive a magnitude bound and amaximum achievable convergence ratefor the error. The magnitude bound as well as the convergence rate are functions of the reference model degree of stability α (‐ α is the maximum real part of model poles).

We show that there is an optimal location for the reference model poles that isindependent of the robot dynamics.This value of α is well defined, and if the feedback gains are further increased, making the model more stable,the performance of the adaptive system is degraded.

It is also shown that the gains in the adaptive portion of the algorithm may be made as high as desired in order to make the actual performance approach the maximum achievable convergence rate. The effect of these adaptive gainsdoesdepend on the unknown dynamics. Simulation results will demonstrate the improved performance of the adaptive controller and the detrimental effects of high gains on the system performance.

Next the slowly time‐varying assumption is relaxed to obtain more realistic bounds on the error. Formulation into a more structured system also allows us to derive a control law that does not require accelerations.
Merging ‘reasoning’ and filtering in a bayesian framework—some sensitivity and optimality aspects
Authors: Forsman K. , Ljung L. , Millnert M. , Skeppstedt A.
Published: July 2007

An approach is described how to incorporate knowledge of symbolic/logic character into a conventional framework of noisy observations in dynamical systems. The idea is based on approximating the optimal solution that could theoretically be computed if a complete Bayesian framework were known (and infinite computational power were available). The nature of the approximations, the deviations from optimality and the sensitivity to ad hoc parameters are specifically addressed. This merging of logic and numerics is essential in many problems of adaptation in control and signal processing.
Stability of the equilibria of adaptive systems with leakage estimator
Authors: Ortega Romeo , Espinosa Gerardo
Published: July 2007

We study the stability of the equilibria of the differential equations that describe an adaptive controller in closed loop with a linear time‐invariant (LTI) undermodelled plant when the parameter update law is a leaky gradient, i.e. a s̀‐modified estimator. Hsu and Costa studied the full‐order case and showed that under certain limiting conditions the resulting dynamic system has three, possibly unstable, equilibrium points. Here we provide a modest extension to that work by further characterizing the class of undermodelled LTI plants for which the equilibria exist and are (un)stable Interestingly enough, it is shown that the equilibria are stable iff a given compensator stabilizes the plant. This compensator is, up to the plant ‘steady state gain’, known to the designer.
Reference model decomposition in direct adaptive control
Authors: Butler H. , Honderd G. , Van Amerongen J.
Published: July 2007

This paper introduces the method of ‘reference model decomposition’ as a way to improve the robustness of model reference adaptive control systems (MRACs) with respect to unmodelled dynamics with a known structure. Such unmodelled dynamics occur when some of the nominal plant dynamics are purposely neglected in the controller design with the aim of keeping the controller order low. One of the effects of such ‘undermodelling’ of the controller is a violation of the perfect model‐matching condition of the primary controller. The decomposition can be seen as a way to adjust the reference model output (and hence the control goal) to the actual model‐matching capabilities. It is shown that the decomposition alleviates the negative effects unmodelled dynamics have on the error equation. Simulation examples illustrate the decomposition design steps and show the obtained improvements.
A result on the mean square error obtained using general tracking algorithms
Authors: Ljung Lennart , Priouret Pierre
Published: July 2007

Tracking time‐varying properties is of crucial importance in all adaptive algorithms. In this contribution we study a fairly general algorithm for tracking properties of model parameters that can be described in a linear regression form (including AR models and the like). An explicit expression for the mean square error between the estimated and the true (time‐varying) parameter is established. For slow adaptation this expression can be arbitrarily well approximated by a much simpler expression. The treatment differs from other related studies using weak convergence theory, averaging, etc. in that the results are not asymptotic in nature and are applicable also to the transient phase as well as over unbounded time intervals.
On the radius of stabilizability of lti systems: Application to projection implementation in indirect adaptive control
Authors: Barmish B. R. , Ortega R.
Published: July 2007

In this paper we solve the problem of finding the ‘largest sphere’ around a linear time‐invariant (LTI) stabilizable plant such that all plants of the same order in the sphere are also stabilizable and there exists a plant in its boundary that is not stabilizable. The sphere is described in the plant parameter space and an explicit expression for its radius is given. This result solves the open problem in indirect adaptive control of determining the region to which the parameter search procedure should be constrained so that stabilizability of the estimated plant is preserved.
Some asymptotic results in recursive identification using laguerre models
Authors: Gunnarsson Svante , Wahlberg Bo
Published: July 2007

This paper deals with recursive identification of time‐varying systems using Laguerre models. Laguerre models generalize finite impulse response (FIR) models by using a priori information about the dominating time constants of the system to be identified. Three recursive algorithms are considered: the stochastic gradient algorithm, the recursive least squares algorithm and a Kalman‐filter‐like recursive identification algorithm. Simple and explicit expressions for the model quality are derived under the assumptions that the system varies slowly, that the model is updated slowly and that the model order is high. The derived expressions show how the use of Laguerre models affects the model quality with respect to tracking capability and disturbance rejection.
Constrained long‐range adaptive predictive control of arterial blood pressure
Authors: Kwok K. Y. , Mutha R. K. , Shah S. L. , Clanachan A. S. , Finegan B.
Published: July 2007

An adaptive blood pressure control system based on the long‐range or finite‐time‐horizon prediction strategy known as generalized predictive control has been examined. The system was designed to run as a real‐time, multitasking operating system providing real‐time monitoring and control of mean arterial pressure. The ability to control in the presence of constraints was also incorporated into the system.

Results from both unconstrained and constrained control runs were obtained from experiments on aneasthetized dogs. The problems caused by model‐plant mismatch, varying time delays and non‐linearity were handled satisfactorily by the system. The disturbance rejection of the constrained algorithm is better than the unconstrained one. The results demonstrate the applicability of both the constrained and unconstrained generalized predictive control algorithm for regulation of mean arterial pressure.
Passive least‐squares‐type estimation algorithm for direct adaptive control
Authors: Brogliato Bernard , Lozano Rogelio
Published: July 2007

The standard least squares (LS) estimation algorithm is modified in such a way that it becomes passive. Therefore the scope of application of the LS algorithm is extended to those adaptive schemes whose convergence proof is based on passivity. The passive version of LS is presented in both continuous and discrete time.
Self‐tuning multivariable pole placement control of a multizone crystal growth furnace
Authors: Batur C. , Sharpless R. B. , Duval W. M. B. , Rosenthal B. N.
Published: July 2007

This paper presents the design and implementation of a multivariable self‐tuning temperature controller for the control of lead bromide crystal growth. The crystal grows inside a multizone transparent furnace. There are eight interacting heating zones shaping the axial temperature distribution inside the furnace. A multi‐input, multi‐output furnace model is identified on‐line by a recursive least squares estimation algorithm. A multivariable pole placement controller based on this model is derived and implemented. Comparison between single‐input, single‐output and multi‐input, multi‐output self‐tuning controllers demonstrates that the zone‐to‐zone interactions can be minimized better by a multi‐input, multi‐output controller design. This directly affects the quality of crystal grown.
Sensitivity, robustness and efficiency of adaptation
Authors: Pervozvanski A. A.
Published: July 2007

The paper contains a brief review of some new results for the classical parametric ‘sensitivity‐robustness’ problem. Some stability robustness criteria are shown and an optimal robust feedback design is presented. The main attention is paid to the performance index robustness. Sensitivity coefficients and their majorants are found for H2‐ and H∞‐norms of transfer functions. Analytical properties of H2‐ and H∞‐optimal feedback systems are investigated. In conclusion, robustification and adaptation as two competitive approaches to feedback design under parametric uncertainty are discussed.
Evolution of intelligent control in adaptive systems
Authors: Timofeyev A. V. , Yussupov R. M.
Published: July 2007

The evolution of adaptive control systems is considered from the viewpoint of the use of informatics and artificial intelligence (AI) tools. Some general principles of intellectronics (AI tools) are discussed taking into consideration adaptive control systems with non‐linear dynamics operating under conditions of uncertainty. The principles are illustrated by an analytical review of adaptive‐intelligent robot control and logical‐probabilistic decision rules as applied to expert systems of adaptive control. Example of the use of intellectronics in adaptive systems are given.
Speed gradient adaptive control algorithms for mechanical systems
Authors: Fradkov A. L. , Stotsky A. A.
Published: July 2007

A number of recent and some new adaptive control algorithms for robot manipulators are considered from the viewpoint of the speed gradient method. Various types of algorithms are considered in a unified framework that allows useful comparisons to be made.
Preliminary results about robust lagrange stability in adaptive non‐linear regulation
Authors: Jiang Z. P. , Praly L.
Published: July 2007

We are concerned with the problem of regulating the equilibrium point of a non‐linear system in the presence of both parametric and dynamic uncertainties. For the parametric uncertainty we propose a new adaptive controller based on a Lyapunov design and guaranteeing the global boundedness of the solution if a growth condition is satisfied. For the dynamic uncertainty we propose a new way of characterizing the unmodelled effects which encompasses some singular and regular perturbations as illustrated by our worked example. Finally we show how, by modifying the above controller, the boundedness property can be made robust to these unmodelled effects.
Adaptively controlling non‐linear systems, using trajectory approximations
Authors: Penfold H. B. , Mareels I. M. Y. , Evans R. J.
Published: July 2007

The structure underlying most adaptive algorithms is a linear parametrized set. This set either represents the universe of controllers the adaptive algorithm can choose from in order to achieve the control task or it may represent a class of models the adaptive algorithm may exploit to approximate the open‐loop plant behaviour. In the latter approach the controller used to close the loop is selected via some non‐linear map of the identified model parameters. The emphasis is on approximating the vector field that generates the trajectories of the system.

Alternatively we propose to predict the trajectories over a short period of time directly, not indirectly involving a representation of the underlying vector fields. The feasibility of such an approach using a one‐step‐ahead‐type algorithm for both prediction and control is analysed. The scheme is hybrid in that the plant is continuous‐time, whilst the control action is implemented in discrete time. The control action is of the model reference type. The algorithm is applied to a class of (non)‐linear time‐varying systems of a given structure (known relative degree) and possessing a stable inverse. Given input/output measurements only, the algorithm can enforce a desired response within guaranteed error bounds. Robustness properties with respect to partial state measurement (e.g. neglecting parasitic dynamics) and violation of the stable inverse assumption are investigated.
Identification of a class of dynamic errors‐in‐variables models
Authors: Zheng Wei‐Xing , Feng Chun‐Bo
Published: July 2007

Dynamic errors‐in‐variables (EV) models are a new type of linear system models and have found extensive practical applications. One common and important concern with EV models is how to remove noise‐induced bias in parameter estimators. In this paper some significant extensions to the newly established bias‐eliminated least‐squares (BELS) method are made, so that this BELS method can be applied to unbiased identification of a general class of dynamic EV models where input noise is white noise and output noise is correlated noise but the noise statistics are unknown a priori. Though still based on the bias correction principle, this method is very meaningful in that it presents a novel and efficient way of utilizing signal‐processing techniques to draw much more useful information from sampled data in order to get desirable identification results. The performance of the proposed method is illustrated by numerical examples.
Construction of a robust adaptive regulator for time‐varying discrete time systems
Authors: Jerbi A. , Kamen E. W. , Dorsey J. F.
Published: July 2007

A robust adaptive regulator is constructed for single‐input/single‐output discrete time systems modelled by a linear time‐varying difference equation that includes an error term to incorporate model errors and/or disturbances. It is assumed that the parameters of the nominal model belong to a known bounded convex set and that the ‘frozen time’ nominal model is stabilizable for all possible parameter values. The estimation of the parameters of the nominal model is carried out using a standard gradient‐type algorithm with a projection operation. An adaptive regulator is then constructed from the solution to a finite time Riccati equation. It is shown that the resulting closed‐loop system is globally stable if the mean of the parameter time variations is sufficiently small and if the model error is sufficiently small, but where the disturbances applied to the plant may be arbitrarily large.
A new decentralized model reference adaptive control for a class of interconnected dynamic systems using variable structure design
Authors: Wu A‐Cheng , Fu Li‐Chen , Hsu Chen‐Fa
Published: July 2007

This paper presents a new decentralized model reference adaptive control for a class of large‐scale interconnected dynamic systems. Interconnections among subsystems may be time‐invariant or time‐varying and linear or non‐linear. The scheme proposed here only takes input and output measurements from each subsystem for input synthesis. Using a variable structure design concept, we show that the tracking errors will converge to zero in finite time despite the interconnections with any possible strengths.
Adaptive predictive control of systems with time‐varying time delay
Authors: Dumont Guy A. , Elnaggar Ashraf , Elshafei Abdel
Published: July 2007

This paper discusses three different methodologies for performing adaptive predictive control in the presence of unknown or time‐varying time delays. The first methodology is the well‐known extended B‐polynomial. The second methodology consists of describing the process dynamics by a network of Laguerre functions. The third methodology, named the variable regressor estimator (VRE), consists of an explicit time delay estimator running in parallel with a standard recursive estimator. Theoretical as well as practical aspects of these methodologies will be discussed. Finally, our industrial experience with these is discussed.
Passive adaptive control of non‐linear systems
Authors: Kanellakopoulos I.
Published: July 2007

We develop a new recursive procedure for the design of adaptive controllers for non‐linear systems with unknown parameters and unmeasured states. The use of novel nonlinear damping terms endows the closed‐loop systems with the remarkable property of boundedness for arbitrary parametric uncertainty even when the adaptation is switched off. The combination of these nonlinear terms with interlacing functions produces ‘damped interlacing’, which results in new passivity and transient performance properties. Damped interlacing allows us to construct a full Lyapunov function that encompasses all the variables of the adaptive system.
New relationships between Lyapunov functions and the passivity theorem
Authors: Brogliato B. , Lozano R. , Landau I. D.
Published: July 2007

In this paper we study new relationships between a class of Lyapunov functions and the passivity theorem. It is proved that under some (sufficient) conditions a Lyapunov‐stable system can be analysed as the feedback connection of two (strictly) passive subsystems. It is also shown that very recent adaptive schemes for linear plants of any relative degree can in a certain sense be unified through a passivity point of view.
Universal iterative learning control using adaptive high‐gain feedback
Authors: Owens David H.
Published: July 2007

Convergence 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

An 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

The 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

An 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

An 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

An 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

An 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

This 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

The 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

In 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

In 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

This 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

There 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

In 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

This 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

The 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

Approximate 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

This 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

The 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

There 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

Adaptive 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

A 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

In 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

A 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

Semiphysical 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

The 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

At 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

The 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.
Playing with Art
Authors: Bullivant Lucy
Published: July 2007

During the last decade and a half, a revolution has been under way in London's public museums and galleries. Lucy Bullivant explains how the digital has emancipated institutions from their previous physical constrictions, enabling artworks to break out from behind glass and liberating artefacts from their sealed cabinets. The perception of permanent collections is being transformed, as visitors are encouraged to make new connections between objects and break down physical distances between separate gallery spaces. Another new level of interaction is being encouraged by specially commissioned installations that encourage the user to physically engage with art. Copyright © 2007 John Wiley & Sons, Ltd.
Otherwise Engaged: New Projects in Interactive Design
Authors: Garcia Mark
Published: July 2007

What are the possibilities of interactive technologies delivering a new level of social engagement in architecture? Taking 2002, with the ETH Ada project and Diller & Scofidio's Braincoat project, as an important watershed, Mark Garcia reviews the advances that have been made per se, and with four projects in particular, in socially interactive spatial design. Copyright © 2007 John Wiley & Sons, Ltd.
Shadow Play: The Participative Art of Scott Snibbe
Authors: Bullivant Lucy
Published: July 2007

Scott Snibbe aspires to an art that aids people to abandon their bodily inhibitions and ‘get out of their sense of their selves’. Lucy Bullivant profiles the work of this Californian artist and computer scientist, who is realising the potential of the ancient Chinese tradition of shadow play through an interactive digital media. Copyright © 2007 John Wiley & Sons, Ltd.
I am a Camera: Electroland
Authors: Hart Hugh
Published: July 2007

Hugh Hart describes how West Coast practice Electroland has repurposed surveillance technology in an interactive installation in Top of the Rock ‐ the observation platform on top of Rockefeller Center, which has become one of New York's premier visitor attractions. This playful, seemingly whimsical public art is the product of a creative partnership between architect‐trained Cameron McNall and programmer Damon Seeley. Copyright © 2007 John Wiley & Sons, Ltd.
Modular Mountain Retreat
Authors: Merkel Jayne
Published: July 2007

This prefabricated tree house in the Catskill Mountains two hours north of New York City nestles into its evergreen site. Designed by Joseph Tanney and Robert Luntz of Resolution: 4 Architecture, it combines two types of existing prefabricated components and two prototypes that the architects have developed out of their own research. Jayne Merkel explains how the design supports the owner's rugged outdoor lifestyle with an airy refined retreat that is tailored to the bachelor art director's particular needs. Copyright © 2007 John Wiley & Sons, Ltd.
The US Solar Decathlon 2007
Authors: Yeang Ken
Published: July 2007

As the focus in the UK turns to the London Olympics in 2012, the Eco‐Maestro Ken Yeang brings our attention to an event on the other side of the Atlantic ‐ the US Solar Decathlon ‐ that offers teams of young designers the opportunity to flex their ecological muscles. Copyright © 2007 John Wiley & Sons, Ltd.
Alternative approaches in detecting asymmetry in farm‐retail price transmission of fluid milk
Authors: Capps Oral , Sherwell Pablo
Published: July 2007

Testing for asymmetric price transmission and calculating elasticities of price transmission are important areas of research for providing insights into market efficiency issues. We analyze the behavior of tests for asymmetry according to the conventional Houck approach and to the von Cramon‐Taubadel and Loy error correction model (ECM) approach. We also estimate the short‐run and long‐run elasticities of price transmission between the farm and retail levels of the marketing channel for whole milk and 2% milk for seven U.S. cities by model specification. We employ monthly data over the period from January 1994 to October 2002. Consistent with the extant literature, empirical results suggest that the farm‐retail price transmission process for milk is asymmetric. Price transmission elasticities, in conjunction with rising farm prices, are generally larger than corresponding elasticities associated with falling farm prices, but they all are quite inelastic. [JEL Classification: Q11, Q13]. © 2007 Wiley Periodicals, Inc. Agribusiness 23: 313–331, 2007.