bluuebunny/crossref_metadata_2025 huggingface dataset:
Adaptive control for time‐varying systems: A combination of Martingale and Markov chain techniques
Authors: Guo Lei , Meyn Sean P.
Published: July 2007

Adaptive control problems of a first‐order randomly time‐varying stochastic system are considered. A class of adaptive controllers based on the Kalman filter is introduced and is analysed using a combination of martingale and Markov chain techniques. It is shown that both the expected value and sample path averages of the square of the output of the closed‐loop system remain bounded and that the long‐run cost is a continuous functional of the parameters of the controller and the distribution of the disturbance process. These results hold even when the Gaussian assumption used in previous papers is removed and the a priori estimate of the noise variance is incorrect.
Applications of the passive systems approach to the stability analysis of adaptive controllers for robot manipulators
Authors: Landau I. D. , Horowitz R.
Published: July 2007

Utilizing the fact that a manipulator composed of rigid links is a passive system, an approach based on the properties of interconnected passive systems is proposed for the analysis and synthesis of fixed and adaptive controllers for robot manipulators. This approach clarifies the rationale behind the various fixed and adaptive control laws synthesized using Lyapunov functions and provides new adaptation algorithms.
Fourier series expansions of noisy signals — consistent estimation of the whole spectrum
Authors: Rafajłsowicz Ewaryst
Published: July 2007

In this note the problem of estimating the Fourier series coefficients of a deterministic signal measured in a noise is discussed. Firstly, it is shown that if random errors are not taken into account, then the mean square error between the true spectrum and its commonly used estimator is infinite. The method proposed for overcoming this difficulty is based on multiplying the estimates by the geometric sequence. It is shown that if this sequence depends on the number of observations and is appropriately chosen, then consistent estimation of the whole spectrum is possible. The method introduces a bias which is shown to be asymptotically vanishing. For smooth signals an upper bound for the bias is also derived.
A real‐time implementation of an iir adaptive line enhancer
Authors: Vishwanath T. G. , Campbell D. R. , Moir T. J.
Published: July 2007

An IIR adaptive line enhancer (ALE) based on the innovations model is implemented in real time using a TMS32010 microprocessor. Results are presented to illustrate the performance of the ALE under different signal‐to‐noise (SNR) ratios.
MAR: A multiprocessor adaptive regulator
Authors: Bonivento Claudio , Melchiorri Claudio
Published: July 2007

The present state of the multiprocessor adaptive regulator (MAR) project, a prototype controller developed at the University of Bologna, is presented. The controller is designed in a distributed environment, based on the following components: a VME bus hardware, MC68000 CPU boards and DRM, a distributed real‐time operating system. The project is carried out in the framework of a research programme between DEIS and Philips Italia, I&E Division. MAR represents a significant evolution of previous experiences at DEIS in the implementation of self‐tuning regulators, developed both on 8‐bit and 16‐bit monoprocessor configurations. The prototype software is supported by the concept of a‘software machine’(SOMA) running on a‘hardware machine’(HAMA) typical of the DRM system. Particular attention is given to the definition of a user‐friendly interface and to the development of supervisory features in order to obtain well‐conditioned and robust performances. These features mainly deal with controller start‐up, parameter estimator wind‐up and actuator wind‐up. Two adaptive control policies are presently available, i.e. pole‐zero placement and minimum variance control. Experiments snowing the implemented functions and results obtained from different SOMA distributions are presented and discussed.
Further evaluation of partial, state model reference adaptive design
Authors: M'Saad M. , Landau I. D. , Samaan M.
Published: July 2007

This paper is a follow up to Reference 1. It presents further evaluation of the partial state model reference adaptive control approach proposed in Reference 2. This evaluation involves suitable benchmark examples that have been proposed in Reference 3 to illustrate the essence of today's adaptive control design techniques. Particular emphasis is put on the ability of the considered adaptive controller to accommodate state disturbances, sensor noise and unmodelled dynamics.
Illustration of an integrated approach to adaptive control
Authors: Goodwin Graham C. , Ninness Brett , Cockerell Ross , Salgado Mario
Published: July 2007

This paper illustrates the application of an integrated approach to adaptive control by reference to the case study problem proposed by Masten and Cohen. The underlying philosophy of our approach includes the integration of robust and adaptive control and the unification of continuous and discrete systems theory. A feature of the examples presented below is that they have been run in real time using a general‐purpose adaptive control toolbox.
Partial convergence of extended least squares for insufficiently excited linear systems
Authors: Viano Marie‐Claude
Published: July 2007

We establish the almost sure convergence of recursive extended least squares towards a linear space when the deterministic input fails to furnish enough excitation to the underlying ARMAX system for a consistent estimation of its coefficients. A speed of convergence is obtained.
An adaptive filter for time‐varying‐parameter models
Authors: Abutaleb A. , Papaioannou M.
Published: July 2007

A non‐linear adaptive filter is introduced and applied to the classical problem of estimating time‐varying‐parameter linear regression models with unknown error variances and a time‐varying transition matrix. The filter is basically a new result in what is known as Sridhar filtering theory. In deriving the filter, which we call the ‘Pontryagin filter’, the Pontryagin minimum principle and the method of invariant imbedding were used. The properties (bias and consistency) of the estimates of the time‐varying parameters are then established.
Factorizations that relax the positive real condition in continuous‐time and fast‐sampled ELS schemes
Authors: Krishnamurthy Vikram , Wahlberg Bo , Moore John B.
Published: July 2007

This paper proposes extended least‐squares (ELS) for ARMAX model identification of continuous‐time and certain discrete‐time systems. The schemes have a relaxed strictly positive real (SPR) condition for global convergence. The relaxed SPR scheme is achieved by introducing overparametrization and prefiltering but without introducing ill‐conditioning. The schemes presented are the first such proposed for continuous‐time systems.

The concepts developed in continuous time carry through to fast‐sampled continuous‐time systems and associated discrete‐time ELS algorithms. For such situations, in comparison with previously proposed discrete‐time schemes, the degree of overparametrization required in the proposed scheme of this paper is significantly lower. The reduction is achieved by using more suitable prefiltering and overparametrization techniques than previously proposed.

We also establish the persistence of excitation (PE) of the regression vectors in the proposed ELS schemes to assure strong consistency, obtain convergence rates and provide robustness to unmodelled dynamics. To prove the PE of continuous‐time regression vectors, we develop output reachability characterization for MIMO linear continuous‐time systems.
Computed torque control via a non‐linear observer
Authors: Canudas Wit C. De , Åström K. J. , Fixot N.
Published: July 2007

High‐precision measurements of joint displacements are available on robot manipulators. In contrast, the velocity measurements obtained through tachometers are in many cases contaminated by noise. It is therefore economically and technically interesting to investigate the possibility of accurately estimating the speed from direct available measurements such as the angular positions. This paper proposes a ‘smooth non‐linear observer’ (i.e. an observer with ‘smooth’ or ‘differentiable’ gains) and a modified computed torque law which is a function of estimated velocities and measurement positions for rigid industrial robot manipulators. We derive local conditions for asymptotic stability of the closed‐loop system.
Robot non‐linearity bounds evaluation techniques for robust control
Authors: Grimm Wolfgang M.
Published: July 2007

Robust controller design approaches for non‐linear control systems usually require the evaluation of upper bounds on non‐linear quantities of the process for the robustness analysis. The conservatism and usefulness of robustness criteria are determined by the accuracy of the robustness bounds. This contribution deals with the robust control of robot manipulators and presents generally applicable evaluation techniques for bounds on robot manipulator non‐linearities which may be remnants of an incomplete non‐linear decoupling or feedback linearization. These bounds can be used to verify several robust stability criteria for the control of rigid and elastic joint robots. Apparently, these bounds give insight to the strength and significance of non‐linear couplings and may be used to judge whether complicated model‐based control laws should be preferred rather than simple linear controllers.
Assessment of achievable performance of simple feedback loops
Authors: Årström K. J.
Published: July 2007

This paper develops techniques for qualitative and quantitive assessment of the performance that can be achieved with a simple feedback loop. Methods which give order‐of‐magnitude estimates for exploring the achievable performance are presented. The results are of interest for control design, auto‐tuning and expert control
Multivariable controller design in an adaptive control scheme
Authors: Pang Grantham K. H.
Published: July 2007

A multivariable control system design approach in the frequency domain is presented. The issue of control system redesign in an adaptive control scheme is discussed. Expert system techniques have been used for the automated design of controllers for multivariable systems. Design knowledge has been represented using rules, facts and objects. The design process consists of a sequence of operations obtained by heuristics and experience in the the design techniques. An automated design session of a multivariable plant using the expert system demonstrates that the expert system appears to work well in its prototype implementation.
Optical adaptive signal processing: An appraisal
Authors: Bitmead Robert R. , Johnson C. Richard , Pollock Clifford R.
Published: July 2007

Potential areas of development in optically based adaptive signal processing are considered together with an assessment of current technological directions. The rapid adoption of superhigh‐throughput lightwave communications media and systems suggests that fully optical signal processing would be very desirable to take advantage of the speed. The broad conclusion of this analysis is that at present it is not feasible to implement fully optical adaptive signal processors but that in time it might be so. ‘Not yet’ is our surmise. A brief literature survey and description of existing prototype optoelectronic adaptive systems is also provided.
Model reference adaptive control for the azimuth‐pointing system of a balloon‐borne stabilized platform
Authors: Chingcuanco Alfredo O. , Lubin Philip M. , Meinhold Peter R. , Tomizuka Masayoshi
Published: July 2007

A balloon‐born stabilized platform has been developed for the remotely operated altitude‐azimuth pointing of a millimetre wave telescope system. This paper presents a development and implementation of model reference adaptive control (MRAC) for the azimuth‐pointing system of the stabilized platform. The primary goal of the controller is to achieve pointing RMS better than 0.1°. Simulation results indicate that MRAC can achieve pointing RMS better than 0.01°. Ground test results show pointing RMS better than 0.03°. Data from the first flight at the National Scientific Balloon Facility (NSBF), Palestine, TX show pointing RMS better than 0.02°.
Stable adaptive control system subject to bounded external disturbances
Authors: Cheng Shao
Published: July 2007

By means of minimizing a quadratic cost function and employing a modified least squares estimator with a dead zone, a new self‐tuning controller is derived. Under simple assumptions the stability of the resulting closed‐loop adaptive control system is ensured in the presence of bounded external disturbances even for non‐minimum phase systems. The analysis performed also gives the upper bounds of tracking errors and parameter estimates. For the disturbance‐free case a modified version of the basic algorithm ensures asymptotic stability, i.e. the output error converges to zero, without any additional assumptions. Simulation results demonstrate the effectiveness of the new controller.
Remarks on the mean square tracking error
Authors: Ljung Lennart , Priouret Pierre
Published: July 2007

The purpose of this paper is to extend recent results of Ljung and Priouret to a more general class of regressors.
Development of a new self‐tuning control algorithm for finite and infinite horizon quadratic adaptive optimization
Authors: Casalino G. , Minciardi R. , Parisini T.
Published: July 2007

The theory of implicit models, introduced in previous papers, is used here in order to define a new adaptive control algorithm based on either m‐step‐ahead or infinite horizon LQ optimization and on recursive least squares identification techniques in the presence of systems having an ARMAX structure. The adaptive algorithm is based on the identification of a single ARX implicit model, which is defined as a model capable of representing the system input‐output behaviour correctly only in certain closed‐loop conditions. It is shown that, by properly structuring the algorithm, a single whitening (i.e. yielding a white residual sequence) possible convergence point exists coinciding with the optimal control law. Simple conditions assuring that a generic convergence point coincides with the above one are also provided, as well as preliminary simulation experience.
A new robust model reference control with improved performance for a class of multivariable unknown plants
Authors: Chien Chiang‐Ju , Fu Li‐Chen
Published: July 2007

Motivated by recent works on parametrization of multivariable plants for model reference adaptive control (MRAC), a new robust model reference control (MRC) scheme for a class of multivariable unknown plants is presented. The salient feature of this control scheme is the improved performance of the output‐tracking property, which is hardly attainable by the traditional MRAC schemes. The controller here is devised using the concept of variable structure design which prevails in the robust control context. It is shown by a Lyapunov approach that without any persistent excitation the global stability of the overall system is achieved and the tracking errors will converge to a residual set. The size of that set can be directly related to the size of unmodelled dynamics and output disturbances explicitly as long as a set of control parameters is chosen properly (large).
Adaptation and robustness in control systems
Authors: Kuntsevich V. M.
Published: July 2007

Two alternative approaches to the solution of control problems under conditions of uncertainty are considered: (i) synthesis of adaptive systems; (ii) synthesis of robust systems.

Non‐stochastic (set) models of uncertainty are used. An estimate of the quality of the plant parameter identification is given by evaluating the diameter of the set to which the parameter vector belongs. The obtained non‐improvable set estimates of the parameters (in the form of ellipsoids or convex polyhedra) are the boundary determining the expediency of the change‐over to the construction of robust control systems.

The remaining part of the paper is devoted to the solution of the problem of synthesis of discrete robust control systems. The necessary and sufficient conditions of robust stability proposed by the author are used and are efficiently tested by means of a special software package ‘Robust stability’ for IBM‐type personal computers.
Effective identification algorithms for adaptive control
Authors: Bittanti Sergio , Campi Marco , Lorito Fabrizio
Published: July 2007

Minimum variance adaptive control schemes are considered. By means of the concept of excitation subspace, the notion of effective identification algorithm is introduced. It is shown that, if the system to be controlled is noise‐free and minimum phase, the tracking error tends to zero provided that the identification is effective. Finally, the effectiveness of the most popular recursive identification techniques (recursive least squares, stochastic gradient, projection algorithm) is discussed.
Adaptive non‐linear control of fed‐batch stirred tank reactors
Authors: Bastin G.
Published: July 2007

Chemical and biological fed‐batch stirred tank reactors (FSTRs) are often good candidates for the application of adaptive non‐linear control techniques because in many cases their dynamical models include highly uncertain kinetic parameters. A general unified methodology for the design of adaptive on‐linear controllers for FSTRs is presented. The conditions under which this control approach helps to solve process optimization problems are emphasized.
Adaptive observers for a class of multi‐output non‐linear systems
Authors: Marino Riccardo , Tomei Patrizio
Published: July 2007

Sufficient differential geometric conditions are given for the existence of global adaptive observers for a class of multi‐output non‐linear systems which are linear with respect to a vector of unknown constant parameters. They extend to multi‐output systems earlier results on adaptive observers for single‐output systems; they also extend to systems with unknown parameters results obtained previously on the existence of non‐adaptive observers with linear error dynamics.
Adaptive non‐linear control with incomplete state information
Authors: Kanellakopoulos I. , Kokotovic P. V. , Morse A. S.
Published: July 2007

Motivated by several recent adaptive non‐linear control results which use either full‐state or single‐output feedback, we present two new adaptive design tools and show how they can be used to construct systematic design procedures for non‐linear systems with incomplete state information. The main features of these procedures are illustrated on a simple third‐order system. We also provide the geometric conditions which give a co‐ordinate‐free characterization of one of the partial‐state‐feedback forms to which these procedures are applicable.
On adaptive control of fedbatch fermentation processes
Authors: Queinnec I. , Dahhou B. , M'Saad M.
Published: July 2007

This paper presents an experimental evaluation of the partial state reference model adaptive control approach proposed by M'Saad et al. The plant to be controlled is a fedbatch fermentation process of alcohol production. Experimental results are presented to demonstrate the performances of the adaptive controller considered. Particular emphasis is placed on the choice of the predictor dynamics and the initialization of the parameter estimator
Frequency‐filtering zoom
Authors: Zarader Jean‐Luc , Garnier Michel , Nicollet Michel
Published: July 2007

Spectral component discrimination and the estimation of one or more signal frequencies are classical problems in signal processing. The purpose here is to study a new technique intended to increase the frequency resolution, called frequency‐filtering zoom (FFZ). The advantage of the method is that it gives a faithful reproduction of the amplitude and phase spectra of signals, in contrast to existing methods which need a phase correction. Moreover, our zoom has been designed to enable software real‐time analysis of low‐frequency signals.
Multi‐element array signal reconstruction with adaptive least‐squares algorithms
Authors: Kumar R.
Published: July 2007

For a mechanical antenna system with a perfect parabolic reflector surface of diameter D the antenna gain is proportional to (D/λc)2, where λc denotes the received carrier wavelength. However, for a practical system an increase in D/λc is also associated with a decrease in antenna efficiency due to the imperfect reflector surface. Since the antenna efficiency is a very sensitive function of the RMS surface deviation or effectively ofD/λc, the loss in efficiency can more than offset the increase in directivity. The loss in efficiency results owing to the dispersion of the signal in the focal plane, with the result that only a fraction of the available signal power is collected by the focal feed. A configuration wherein several feeds are placed in the focal plane of the antenna is considered so as to capture all the available power. However, the signal phase and amplitude at the outputs of the various feeds vary randomly with time owing to the time‐varying deformation of the reflector surface induced, for example, by vibrational modes set up by gravitational or thermal fields or wind. Coherent signal combining techniques based on an adaptive least‐squares algorithm are investigated for nearly optimally and adaptively combining the outputs of these feeds. The performances of the two proposed versions of the least‐squares algorithm are evaluated by simulations. It is shown for the example considered that both the adaptive least‐squares algorithms are capable of offsetting most of the loss in the antenna gain incurred owing to reflector surface deformations.
Partial state model reference adaptive control of a rapid thermal processor
Authors: Djebara K. , Dahhou B. , Babary J. P. , Khellaf A. , Ganibal C.
Published: July 2007

The temperature control problem in a rapid thermal processor is addressed using first a conventional PID controller and then a partial state model reference adaptive controller derived via the long‐range predictive control and linear quadratic control approaches. These two algorithms are shown to provide such fundamental design features as offset‐free disturbance rejection and stability robustness with respect to unmodelled and time‐varying dynamics.
Adaptive control of systems with unknown physical element values
Authors: Dasoupta Soura , Anderson Brian D. O. , Kaye R. J.
Published: July 2007

An adaptive control algorithm is proposed for systems with unknown physical element values. These parameters enter the numerator and denominator polynomials of the system transfer function in a multilinear fashion. The algorithm developed is shown to be globally stable with uniformly asymptotic parameter convergence.
Experimental adaptive controllers for a class of electrohydraulic servosystems
Authors: Fullmer R. Rees , Ananthakrishnan S.
Published: July 2007

This paper investigates experimentally the application of a class of adaptive control algorithms to hydraulic servosystems. These algorithms combine parameter identification algorithms and conventional discrete‐time control design methods to continually update the coefficients of the control algorithm. Several versions of this approach were applied to the control of long‐stroke pistons and rotary vane actuators. Experimental results demonstrate that this class of adaptive controllers work well in the presence of non‐linearities such as Coulombic friction, servovalve saturation, flow non‐linearities, and plant and significant initial parameter uncertainties.
Robust and simple adaptive control system design
Authors: Iwai Z. , Mizumoto I. , Ohtsuka H.
Published: July 2007

Recently, the simple adaptive control (SAC) method has attracted considerable interest because of the simple structure of its adaptive controller. The method can only be applied to plants with so‐called almost strictly positive real (ASPR) characteristics. Unfortunately, most real plants do not satisfy this condition. Furthermore, real plants contain disturbances such as uncertain elements. This paper deals with such problems and proves that the implementation of a parallel feedforward compensator makes it possible to apply the SAC method to non‐ASPR SISO plants with plant uncertainties. Furthermore, a robust SAC algorithm in the presence of a class of external disturbances is also considered. The effectiveness of the parallel feedforward compensator and robust adaptive controller designed in this way is examined through several numerical simulations.
Linear and non‐linear adaptive control of alcoholic fermentation process: Experimental results
Authors: Dahhou B. , Roux G. , Cheruy A.
Published: July 2007

This paper deals with the experimental implementation of several control algorithms on a continuous flow fermentation process. The regulation and tracking problems of the substrate concentration are considered. Our objective is to show the advantages and drawbacks of these algorithms in tracking and regulation behaviour, overshoot, knowledge contribution, number of tuning parameters, etc. The different controllers described in this paper are the PI controller, the non‐linear adaptive L/A algorithm, the linear adaptive controller (predictive control with partial state reference model) and the adaptive controller based on the non‐linear structure of the process model (pole placement control). These controllers are applied to a pilot‐scale fermentation system with satisfactory results.
A factored form of the instrumental variable algorithm
Authors: Niu Shaohua , Fisher D. G. , Xiao Deyun
Published: July 2007

A UDVT‐factored form of the instrumental variable (IV) identification algorithm is developed for identification of systems with coloured noise. The algorithm uses a UDVT factorization technique to reformulate the conventional instrumental variable method and simultaneously produces the parameter estimates and loss functions for all models from order one to a user‐specified number n with approximately the same computational effort as the conventional IV algorithm. The algorithm possesses a unique compact structure that facilitates the interpretation and implementation of IV‐type algorithms. The UDVT factorization technique used in this algorithm also results in better numerical performance than the conventional instrumental variable method.
Evaluation of control strategies for anaerobic digestion processes
Authors: Perrier M. , Dochain D.
Published: July 2007

Adaptive non‐linear controller designs for various controlled variables are evaluated for the operation of the anaerobic digestion process. The selected controlled variables are the chemical oxygen demand (COD), propionate concentration and dissolved hydrogen concentration. The manipulated variable is the dilution rate. Because the non‐linear control algorithms are designed on the basis of the mass balance equations, distinct assumptions have to be made depending on the choice of the controlled variable. For each selected controlled variable the linearizing control design based on a reduced‐order model of the process results in a similar controller structure. Although the obtained responses are model‐specific, the methodology presented can be readily applied to an actual laboratory‐ or industrial‐scale bioreactor. Study of the time response of the volatile fatty acids and dissolved hydrogen concentrations and of the methane flow rate obtained by controlling one variable at a time reveals the intrinsic behaviour of the balances between the assumed bacterial species and can be useful to investigate for instance the inhibition induced by a given product present in the reactor. The developed methodology demonstrates the usefulness of advanced control techniques to get a better understanding of the process under study even if the controller designs are based on incomplete knowledge.
Adaptive control of a stochastic non‐linear system: An example
Authors: Wittenmark Björn
Published: July 2007

In this paper a simple non‐linear stochastic system is analysed. Various problem formulations are discussed and the performances of the resulting non‐linear adaptive controllers are investigated. The adaptive controllers are based on predictions of the output of the process.
Exact description of feasible parameter sets and minimax estimation
Authors: Piet‐Lahanier Héléne , Walter Eric
Published: July 2007

When the error between the data and the corresponding model output is affine in the parameters to be estimated, the set § of all values of the parameter vector that are feasible (in the sense that the error between the model and the data falls within known prior bounds) is a convex polyhedron, or even a convex polytope if the regressors span the parameter space. Although most of the literature on parameter bounding has been concerned with finding simple‐shaped sets (ellipsoids or boxes) guaranteed to contain §, methods have also been developed that make it possible to characterize this polytope exactly and recursively as the convex combination of its vertices. the basic principles of this approach are explained and the results obtained are illustrated. For time‐varying systems a new simple policy is suggested to expand the previous polytope before intersecting it with the feasible region corresponding to the new datum. This policy allows algorithms developed for time‐invariant systems to be used without any modification. When no reliable bounds are available for the error, an extension of the exact description approach is suggested. This makes it possible to obtain the minimax estimate of the parameters exactly and recursively. As a byproduct, the polyhedral cone obtained contains all values of § that would be obtained if a bound larger than the minimax bound were assumed for the absolute value of the error.
Minimal volume ellipsoids
Authors: Pronzato Luc , Walter Eric
Published: July 2007

In bounded‐error estimation one is interested in characterizing the set § of all values of the parameters of a model which are consistent with the data in the sense that the corresponding errors fall between known prior bounds. the problem treated here is the computation of aminimal volume ellipsoidguaranteed to contain §. Although this ellipsoidal approach to parameter bounding was initially applied to models linear in their parameters, where the only error to be accounted for was an output error, it can be extended to deal with errors‐in‐variables problems. This makes it possible to consider models nonlinear in their parameters or dynamical models where both inputs and outputs are subject to bounded errors.Recursivealgorithms are considered first. the basic algorithm of Fogel and Huang (1982), developed from the seminal work of Schweppe (1973) and modified as suggested by Belforte and co‐workers (1985, 1990), is derived in detail, which makes it possible to clarify the nature of the tests needed. This algorithm is proved to be mathematically equivalent to arecursively optimalalgorithm independently developed by Todd (1980) and König and Pallaschke (1981) in the context of linear programming after the celebrated work of Khachiyan (1979). A recursive approach, also developed in this context, is suggested for errors‐in‐variables problems. Recursively optimal ellipsoids, however, are not globally optimal because of the approximations committed at each step. Using a methodology borrowed from experiment design, we obtain optimality conditions which can be used to derive a number of algorithms guaranteed to converge to theglobal optimum.
Bounded‐error estimation using dead zone and bounding ellipsoid
Authors: Evans R. J. , Zhang C. , Soh Y. C.
Published: July 2007

The use of a dead zone and a bounding ellipsoid for parameter estimation when measurement errors are bounded is discussed. the size of the dead zone is set to be exactly equal to the assumed noise bound. The algorithm retains the properties of computing parameter point estimates and allows a bounding ellipsoid to be computed at each iterative step.
Application of a bounded‐error on‐line estimation algorithm to robotics systems
Authors: Canudas Wit C. De
Published: July 2007

This paper shows how bounded‐error on‐line estimation algorithms can be applied in the context of parameter identification of mechanical structures. the modified exponentially weighted recursive least‐squares (MEW‐RLS) estimation algorithm proposed by Canudas de Wit and Carrillo is used in connection with models of mechanical systems with state‐dependent disturbances. These disturbances are provoked on one hand by simplified friction models and on the other hand by the particular model formulation of the multivariable Lagrange mechanical systems.A prioriknowledge on the physics of these systems is used to derive upper bounds on the model disturbances, which are needed for the application of the algorithm.
Algorithms for state bounding in large‐scale systems
Authors: Brdys M. A. , Kang Y. C.
Published: July 2007

A class of linear and time‐invariant large‐scale systems under unknown input signals and measurement errors is considered. No assumptions about statistical properties of the unknown quantities are made. the uncertainty is modelled through bounds that define ellipsoids in which lie unknown initial conditions and unknown ellipsoidal tubes containing signal trajectories. the state estimation problem consists of determining on‐line the smallest sets in the state space in which the unknown system state lies. Two hierarchical estimation algorithms are proposed and compared, namely the ‘completely decentralized with interaction measurements’ (CDIM) algorithm and the two‐level hierarchical with interaction measurements (THIM) algorithm. the THIM algorithm leads to smaller estimating sets owing to additional co‐ordinator‐level information.
Adaptive versus neural adaptive control: Application to robotics
Authors: Saad M. , Dessaint L. A. , Bigras P. , Al‐Haddad K.
Published: July 2007

A comparative study evaluates the problem of determining the control that must be exerted on manipulator joints. Two different techniques are studied: (i) direct and indirect adaptive controls and (ii) neural adaptive control. In the direct adaptive technique the Lyapunov stability‐based approach is used with the objective of minimizing the tracking errors of the joints in the adaptation process. In the indirect adaptive technique the regulator parameters are updated via the estimation of the process model. This step, using a recursive least squares algorithm, is based on the error at the input and on the filtered dynamic model in order to avoid acceleration measurements.

Neural adaptive control is based on learning from input‐output measurements and not on parametricmodel‐based dynamics. It is important to note thatadaptivecontrol requires a real‐time estimation of the system parameters and a well‐defined dynamic model, whereasneural adaptivecontrol does not require any of these conditions.

All the above‐mentioned techniques are applied to the trajectory‐tracking control of a two‐degree‐of‐freedom (2DOF) manipulator. the experimental results show the effectiveness of the neural adaptive techniques for the trajectory‐tracking errors.
Multivariable process and prediction models in predictive control—A unified approach
Authors: De Vries R. A. J. , Verbruggen H. B.
Published: July 2007

In this paper multivariable generalizations of the SISO unified process model (UPCM) and unified prediction model (UPM) used in the SISO unified predictive controller are presented. the necessary assumptions regarding the multivariable UPCM (MUPCM) are established. the properties of the multivariable UPM (MUPM) are examined and the conditions for a zero steady state prediction error are established.

The MUPCM has a general noise model and unites all multivariable, discrete, linear process models in transfer function form. the MUPM uses an output filter which unites all relevant output filters used in predictive controllers. Furthermore, the elements of the output can be predicted over different horizons. the MUPCM and MUPM and the algorithm used to calculate the MUPM are chosen such that the predictions of the outputs can be calculated as fast as possible, not only for the general model, but also for special cases such as ARX, ARIMAX, Box‐Jenkins and FIR models.
Improved performance MRAC of linear and non‐linear systems
Authors: Papadakis Ioannis N. M. , Thomopoulos Stelios C. A.
Published: July 2007

The problem of improved performance adaptive control (IPAC) of a class of linear and non‐linear systems is considered. A method for its solution is presented, the main feature of which lies in augmenting the ‘standard’ model reference adaptive controller by a signal properly designed to compensate for the effect of plant parameter uncertainty on the output error. One of the main performance improvement characteristics of the proposed IPAC is that the zero‐state output error can be made arbitrarily small under standard model reference adaptive control (MRAC) assumptions in the case of linear systems, while a similar result holds true for a class of linearizable systems as well. the structure of the proposed controller is such that several existing MRAC results, such as exponential convergence of output and parameter errors in the presence of sufficiently rich reference inputs, remain valid. the proposed controller also achieves improved performance in the presence of a class of bounded disturbances and/or unmodelled dynamics as well as in the case of an adaptation switch‐off.
Stability, robustness and approximation properties of gradient recurrent high‐order neural networks
Authors: Kosmatopoulos E. B. , Christodoulou M. A.
Published: July 2007

Gradient recurrent high‐order neural networks (GRHONNs) have found great applicability to optimization, associative memory, adaptive signal‐processing and system identification problems. In this paper we show that these neural networks are asymptotically stable dynamical systems and, moreover, that their solutions remain stable under either deterministic or stochastic disturbances. We also prove that GRHONNs are dense in the space of continuous dynamical systems, contrary to the fact that their vector fields do not satisfy the Stone‐Weierstrass theorem. the significance of these theoretical results to associative memory and optimization and to the fabrication of general‐purpose, large‐scale hardware implementations is discussed.
Robust adaptive quantization: A Kalman‐filter‐based approach
Authors: Crisafulli Sam , Bitmead Robert R.
Published: July 2007

Adaptive quantizers/dequantizers are systems that are used to quantize efficiently signals with a large variation in short‐term variance. They are typically found in telecommunication systems where highly non‐stationary signals such as speech need to be represented digitally with the minimum number of bits. Channel errors that are introduced owing to non‐ideal transmission significantly reduce the performance of adaptive dequantizers. In this paper we extend recent Kalman‐filter‐based adaptive quantization techniques to arrive at new dequantization schemes which are more robust to channel errors. This is achieved by utilizing the estimates produced by a Kalman filter based on a linear signal model which embodies the entire encoder/channel combination. Extensions to Kalman smoothing based on the same signal model result in further performance improvement at the expense of a small delay.
A vectorized systolic array for parallel weight extraction of block RLS
Authors: Sakai Hideaki
Published: July 2007

A new vectorized systolic array for extracting weight coefficients in block RLS is proposed. Our array is based on a generalization of the inverse factorization method of Pan and Plemmons and the array for computing residual vectors in block RLS using the Householder transformation of Liu et al. the new array is obtained by first generalizing the inverse factorizations for the block case and then deriving a formula for updating the tap coefficients without matrix operations.
Blind equalizers with simulated annealing optimization for digital communication systems
Authors: Ilow J. , Hatzinakos D. , Venetsanopoulos A. N.
Published: July 2007

New batch‐type methods and their recursive extensions are introduced for blind equalization of digital communication channels. Based on the underlying cost function of the existing Bussgang and cumulantfitting algorithms, simulated annealing (SA) optimization is successfully employed to identify and equalize the communication channels.

Many of the existing blind equalization algorithms utilize a stochastic gradient approach to minimize non‐linear cost functions. Owing to the multimodal nature of these functions, the gradient algorithms might converge to the wrong solution. In contrast, the proposed methods are based on a global optimization algorithm. the SA algorithm tends to avoid the local minima encountered.

The feasibility and convergence behaviour of the proposed algorithms are examined by means of computer simulations. Computational complexity issues in the implementation of the algorithms are discussed.
Reduced Kalman filtering for indirect adaptive control of the induction motor
Authors: Glielmo L. , Marino P. , Setola R. , Vasca F.
Published: July 2007

Parameter variations strongly affect the application of indirect field‐oriented control (FOC) of the induction motor. To estimate those parameters which cannot be obtained by means of a direct measure, an augmented state observer can be constructed; in particular, the presence of noise introduced by the inverter and sensors suggests the use of an extended Kalman filter (EKF). In this paper, by analysing the indirect FOC technique via a two‐time‐scale approach, we formally justify its effectiveness and propose a reduced‐order EKF which is used to construct an adaptive version of the FOC method. The practical drawback of reduced‐order EKFs, i.e. the necessity to differentiate measured quantities, is avoided by exploiting some peculiarities of the field‐oriented controller.
Adaptive tuning of fuzzy logic identifier for unknown non‐linear systems
Authors: Liu Kai , Lewis Frank L.
Published: July 2007

An on‐line approximator using a linear fuzzy logic model with automatic tuning mechanism is presented in this paper. the proposed approximation method can be used to model a class of unknown non‐linear systems as long as they are Caratheodory ones. the structure of the fuzzy model is fixed but the parameters are tuned by the state errors. the fuzzy model is composed of several semi‐closed, continuous, totally ordered and well‐defined fuzzy numbers defined in each state variable dimension and a number of appropriately defined condition‐action rules. Either the min‐inference or product‐inference technique is utilized to generate the weighted average of the linear coefficients which make the whole unknown non‐linear system piecewise linearized. No off‐line preprocessing is needed. the initial values of the parameters of the fuzzy model can be arbitrarily assigned. Then they are tuned to their true values through adaptive update law and therefore it is guaranteed that the unknown non‐linear system is linearized and approximated to any degree of accuracy by the linear fuzzy logic model.
Deconvolution with bounded uncertainty
Authors: Combettes Patrick L. , Trussell H. Joel
Published: July 2007

In deconvolution problems there are two primary sources of uncertainty in the data formation mechanism, namely measurement noise and errors in the model of the system. In this paper we develop an abstract set theoretic deconvolution framework for problems in which the only information available about these sources of uncertainty consists of bounds. Iterative methods based on projections are used to generate solutions consistent with these bounds, the output data signal and a priori knowledge about the input signal. an example of application of this general framework to discrete signal recovery is demonstrated.
Adaptive control in a heating vessel: A comparative study
Authors: Pérez‐Correa J. R. , Sepúlveda F. R. , Duarte M. A.
Published: July 2007

This paper presents experimental results obtained in temperature adaptive control of a heating vessel. A non‐linear model of the process showed that the feed flow rate causes severe variations in time constant and process gain. An extended horizon self‐tuning controller and two different model reference adaptive controllers were tested. The influence of design parameters on control performance for each algorithm was assessed. The control system was disturbed with load and setpoint changes.
Design of adaptive feedforward algorithms using internal model equivalence
Authors: Messner William , Bodson Marc
Published: July 2007

The paper investigates the design of adaptive feedforward cancellation (AFC) algorithms with sinusoidal regressors for repetitive control. Such adaptive algorithms are equivalent to linear controllers based on the internal model principle (IMP). Using this equivalence and root locus rules, the phase advance of the regressor of the adaptive algorithm can be chosen to maximize the phase margin at low gains. A surprising result is that selecting the optimal phase advance is equivalent to placing a zero in the open right half‐plane in certain cases. A complete design and analysis for the compensation of a single‐frequency periodic disturbance is done. A new variation of the AFC algorithm is also developed in which the adaptive portion acts in parallel with a feedthrough term. the IMP equivalent of this algorithm has two zeros instead of one. Analysis and simulation show this method to have superior convergence and robustness properties when compared with the method having no feedthrough term. Discrete time versions of the algorithms are briefly considered.
Self‐oscillating adaptive design of systems with dry friction and significant parameter uncertainty
Authors: Oldak S. , Gutman P. O.
Published: July 2007

Motion control systems with dry friction and high‐order uncertain plants suffer from one or more limit cycles which can be predicted with describing function theory. In this paper quantitative design theory is presented to satisfy given frequency domain tolerances in these systems. The case of linear time‐invariant (LTI) compensation is studied first. Then it is shown how, in those systems with a limit cycle, self‐oscillating design with an adaptive loop (SOAL) can be used to reduce the LTI compensator bandwidth for a large class of problems.
Approximate gradients, convergence and positive realness in recursive identification of a class of non‐linear systems
Authors: Wigren Torbjörn
Published: July 2007

Recursive identification algorithms based on the Wiener model are presented in this paper. They estimate the parameters of a SISO linear dynamic block in cascade with a known static output non‐linearity. Inversion of the non‐linear function is avoided and approximations of gradients are utilized. This allows an exact treatment of output measurement saturation and of situations where output measurements are obtained from sensors with relay‐type characteristics, such as EGO sensors in emission control systems for cars. Exact compensation for coarse quantization of output measurements can also be obtained by the algorithms. Stochastic averaging techniques using associated differential equations prove that local and global convergence of the schemes are tied to positive realness and sector conditions on the non‐linearity. Conditions for local convergence to the correct parameters are established for the case where the output non‐linearity is an arbitrary quantizer.