bluuebunny/crossref_metadata_2025_split huggingface dataset:
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

AbstractThe 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

AbstractMotivated 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

AbstractTwo 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

AbstractMinimum 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

AbstractChemical 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

AbstractSufficient 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

AbstractMotivated 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

AbstractThis 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

AbstractSpectral 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

AbstractFor 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

AbstractThe 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

AbstractAn 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

AbstractThis 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

AbstractRecently, 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

AbstractThis 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

AbstractA 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

AbstractAdaptive 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.
Exact description of feasible parameter sets and minimax estimation
Authors: Piet‐Lahanier Héléne , Walter Eric 
Published: July 2007

AbstractWhen 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

AbstractIn 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

AbstractThe 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

AbstractThis 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

AbstractA 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

AbstractA 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

AbstractIn 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

AbstractThe 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.