bluuebunny/crossref_metadata_2025 huggingface dataset:
Application of robust adaptive control using combined direct and indirect methods
Authors: Narendra Kumpati S. , Duarte Manuel A.
Published: July 2007

A new approach, combining direct and indirect control methods, recently proposed by the authors is used to adaptively control plants with unknown parameters. The plant parameters are known to lie in a specified compact set in parameter space and the output of the plant has to follow a desired output in the presence of a piecewise‐constant input disturbance. Simulation results indicate that the combined method performs better than either the direct or the indirect method.
Example applications of an integrated indirect adaptive design technique
Authors: Goodwin Graham C. , Salgado Mario E. , Middleton Richard H.
Published: July 2007

This paper illustrates an integrated indirect adaptive design technique using a case study example. Key features of this approach are the separate optimization of procedures for parameter estimation and control law synthesis as well as a mechanism for integrating robust and adaptive control design.
Performance analysis of parameter estimation algorithms based on high‐order moments
Authors: Porat Boaz , Friedlander Benjamin
Published: July 2007

Recently, there has been a considerable interest in parametric estimation of non‐Gaussian processes, based on high‐order moments. Several researchers have proposed algorithms for estimating the parameters of AR, MA and ARMA processes, based on the third‐order and fourth‐order cumulants. These algorithms are capable of handling non‐minimum phase processes, and some of them provide a good trade‐off between computational complexity and statistical efficiency.

This paper presents some results about the performance of algorithms based on high‐order moments. A general lower bound is derived for the variance of estimates based on high‐order sample moments. This bound, which is shown to be asymptotically tight, is neither the Cramer‐Rao bound nor a trivial extension thereof. The performance of weighted least squares estimates of the type recently proposed in the literature is investigated. An expression for the variance of such estimates is derived and the existence of an optimal weight matrix is proven. The general formulae are specialized to MA and ARMA processes and used to analyse the performance of some algorithms in detail. The analytic results are verified by Monte Carlo simulations for some specific test cases.

A by‐product of this paper is the derivation of asymptotic formulae for the variances and covariances of the sample third‐order moments of a certain class of processes.
Adaptive beamforming
Authors: Steinhardt Allan O. , Van Veen Barry D.
Published: July 2007

An adaptive beamformer is a system devised to isolate a desired signal from interfering signals which are spatially disparate but which occupy the same spectral band. Traditionally these systems have been employed primarily in sonar and radar. However, because of the rise in traffic throughout the electromagnetic spectrum, coupled with the availability of high‐speed real‐time computational resources, adaptive beamforming is now increasingly of interest in wireless communications as well. In this overview we present the principles underlying these devices with an emphasis on estimation and system theoretic issues.
Adaptive control of a coupled tank apparatus
Authors: Fernandes José M. , De Souza Carlos E. , Goodwin Graham C.
Published: July 2007

This paper describes an experimental investigation of adaptive control applied to a coupled tank apparatus. The study is intended to explore several practical considerations which arise in the application of adaptive control to realistic processes. In the experiments, the apparatus was configured so as to have several difficulties found in practical systems, including unmodelled time delay, unmodelled high‐frequency lag and unmodelled non‐linearities. These features are studied both theoretically and experimentally. The adaptive controller is shown to perform satisfactorily in the presence of these practical difficulties. Also a comparison with other strategies for control system design is given.
Boundedness conjecture for an output error adaptive algorithm
Authors: Schoenwald D. A. , Kokotovic P. V.
Published: July 2007

This paper compares conditions for instability with conditions for global asymptotic stability of a discretetime output error adaptive estimation algorithm. This comparison leads to a boundedness conjecture which states that all signals within this adaptive system as well as the output error and parameter estimates remain globally bounded for all time despite any unstable behaviour. The investigation into the stability properties of this algorithm begins with a global stability criterion applied to a transfer function which is not strictly positive real. This criterion states that for large enough adaptation gain and/or input signal magnitude the system will be asymptotically stable. Then a local result is applied which states that for small enough adaptation gain and/or input signal magnitude the same system is unstable. The result is a bifurcation due to the decrease of the adaptation gain and/or input signal magnitude as the system goes from asymptotic stability to instability which remains bounded. The results suggested by the computer simulations are verified by an exact analysis of a linearized periodic version of the adaptive system.
An advanced showcase of adaptive controller designs
Authors: Masten Michael K. , Cohen Herbert E.
Published: July 2007

The adaptive control literature contains numerous theories for deriving adaptive controllers. However, practising engineers who are not specialists in adaptive theory often have difficulty selecting which approach to use in a given application. The papers in this special issue address this situation by defining a set of standard problems which are then solved by several distinct adaptive control approaches. This introductory paper provides the motivation for the issue (why it is important, who is the target audience, what is the goal of the process), defines the standard problems to be considered and explains the rationale for their formulation, and describes the guidelines which were established for the participants. In addition, this paper defines the ‘ideal’ closed‐loop responses for each standard problem, from which the results of the subsequent papers can be judged.
Application of robust and adaptive pole placement
Authors: Åström Karl Johan
Published: July 2007

The previous investigation showed the usefulness of a two‐degree‐of‐freedom configuration. Such a controller can also be designed using pole placement. An adaptive controller based on this concept is developed and applied to the problem with good results. Particular features, such as selection of model structure, design parameters and excitation, are also discussed.
Robust adaptive control using a combined approach
Authors: Narendra Kumpati S. , Boškovič Jovan D.
Published: July 2007

Direct control, indirect control and variable structure methods are combined with high‐gain feedback in this paper for the robust adaptive control of first‐, second‐ and third‐order plants with increasing levels of uncertainty. The latter include external disturbance, unmodelled dynamics, time delay and sensor noise. A judicious combination of the methods is found to result in satisfactory response in most cases.
On the implementation of predictive adaptive control by adaptive predictors
Authors: Chisci L. , Zappa G. , Mosca E.
Published: July 2007

A convenient parametrization of LQ predictive control laws in terms of predictions only is stressed. This has two desirable consequences in predictive adaptive control. First, it makes the control computation independent of the identified plant representation (be it transversal, lattice or any other). Secondly, it allows modular VLSI implementation by means of standard RLS adaptive predictor modules. In this connection it is shown that control strategies based on the separate identification of multistep‐ahead predictors are more favourable than strategies based on computing multistep‐ahead predictor parameters by analytically extrapolating the identified one‐step‐ahead predictor.
Frequency‐dependent bursting in adaptive echo cancellation and its prevention using double‐talk detectors
Authors: Ding Z. , Johnson C. R. , Sethares W. A.
Published: July 2007

Echo cancellation in telephone communications can often be accomplished by using adaptive network echo cancellers. However, under certain circumstances, these generally effective echo cancellers can cause an undesirable bursting phenomenon. In this paper the essential driving force behind bursting is attributed to the correlation between the signal the near‐end is to transmit and the signal the near‐end receives from the far‐end. This correlation and the subsequent potential for temporary destabilization arise as a result of the feedback loop structure of the four‐wire telephone circuit. A new test signal which approximately measures this correlation is proposed for use in the double‐talk detector scheme that is commonly used to halt adaptation before a mishap such as bursting occurs.
A counter example to the exponential convergence of the directional forgetting algorithm
Authors: Bittanti S. , Bolzern P. , Campi M.
Published: July 2007

In this paper it is shown that the so‐called directional forgetting algorithm is not exponentially convergent. This is a major drawback since it implies poor tracking performance in the case of time‐varying parameters.
Improved method for designing linear distributed feedback systems
Authors: Kelemen M. , Kannai Y. , Horowitz I.
Published: July 2007

A new BIBO‐type stability criterion is proven for linear systems described by partial differential operators with constant coefficients. The class of inputs contains functions which are step‐like in time and which decay appropriately in the space variables. On the basis of this result, a frequency domain synthesis procedure was developed leading to an ‘economic’ feedback loop (i.e. significant noise reduction achieved). The procedure is illustrated by means of an example.
A bayes‐closed approximation of recursive non‐linear estimation
Authors: Kulhavý Rudolf
Published: July 2007

To implement the Bayes estimation in a recursive manner means to cope with the need for storing a too large data statistic. We prove here that under certain assumptions, it is possible to construct a reduced description of posterior densities (a reduced statistic) which is closed with respect to the Bayes rule. Thus the optimal Bayes inference can be realized in terms of transitions between equivalence classes of densities matching the respective values of the description. Next we attempt to design a specific approximation of the recursive Bayes estimation by projecting the true posterior density orthogonally, along the appropriate equivalence class, onto a prespecified approximation family. We find that to ensure orthogonal projection globally, the approximation family must be of a mixture type and the description vector determined by the values of the Kullback‐Leibler distance between the ‘base’ densities of the mixture approximation family and the true density. A simple simulation example compares the Bayes‐closed approximation with some competitive methods.
Indirect adaptive state feedback control of linearly parametrized non‐linear systems
Authors: Campion G. , Bastin G.
Published: July 2007

Two indirect adaptive linearizing controllers are proposed, in continuous time, for the class of non‐linear systems which are linearly parametrized and which can be linearized by state feedback through a parametrized diffeomorphism. In each case the stability of the closed loop is analysed in detail. It is also shown how these control laws can be extended to related situations, i.e. input‐output feedback linearization and non‐linear parametrizations.
Robust stability of multi‐loop continuous time self‐tuning controllers
Authors: Gawthrop P. J.
Published: July 2007

The results of an earlier paper concerning global robustness of single‐loop self‐tuning controllers are extended to include multiple self‐tuning controllers operating in a multiple‐interacting‐loop environment. The robust stability is shown to depend on the singular value loci of a certain transfer function matrix. The need for control weighting to ensure global robust stability is emphasized.
On the persistency of excitation for systems having purely deterministic disturbances using the RLS algorithm
Authors: Cristi Roberto , Feuer Arie
Published: July 2007

A system with purely deterministic disturbances can be represented by a non‐minimal model. In this paper we establish conditions on the input to the system so that when the RLS algorithm is applied to identify its parameters, convergence is guaranteed. It should be pointed out that no boundedness conditions on this input are required.
On the stability of decentralized discrete adaptive control systems
Authors: Wiemer P. , Unbehauen H.
Published: July 2007

In this paper some results of direct decentralized adaptive control based on input/output methods are presented. The plant is assumed to have several local adaptive controllers, each of which can observe only local system outputs and control only local inputs. Conditions for the stability of the decentralized control scheme are derived using an extended I/O sector stability theorem of Safonov.20 The associated error model of the adaptive scheme can be represented in the form of an equivalent feedback configuration comprising a non‐linear time‐varying feedforward block describing the adaptation law and a linear time‐invariant feedback block which depends on the interactions of the different control loops. Stability is obtained if the blocks verify certain sector conditions. It will be demonstrated that conventional adaptation algorithms have to be modified. The proposed RLS parameter adaptation law differs from standard algorithms by the introduction of signal normalization, interlaced parameter adaptation and a variable forgetting factor.

The allowable class of interactions is restricted by the sector condition for the linear feedback block. It will be shown that less conservative conditions can be found when a parallel filter, the so‐called correction network, is introduced. Such filters make direct adaptive control possible even for non‐minimum phase systems. In addition, the correction network makes the adaptive controller robust to uncertainties in the structure of the plant.
An H∞ approach to learning control systems
Authors: Padieu Francois , Su Renjeng
Published: July 2007

In this paper we study a control methodology now often referred to as learning control in the recent control literature. Our primary aim is to establish a mathematical framework which will allow systematic and continuing development of learning control theory. The framework is based on the functional spaces which have been heavily used in H∞ control theory. It is then applied to the study of the convergence problems associated with learning control. Three theorems on general convergence conditions are presented.
A unified perspective on robot control: The energy lyapunov function approach
Authors: Wen John T.
Published: July 2007

A unified framework for the stability analysis of robot tracking control is presented. By using an energy‐motivated Lyapunov function candidate, the closed‐loop stability is shown for a large family of control laws sharing a common structure of proportional and derivative feedback and a model‐based feedforward. The feedforward can be zero, partial or complete linearized dynamics, partial or complete non‐linear dynamics, or linearized or non‐linear dynamics with parameter adaptation. As a result, the dichotomous approaches to the robot control problem based on the open‐loop linearization and non‐linear Lyapunov analysis are both included in this treatment. Furthermore, quantitative estimates of the trade‐offs between different schemes in terms of the tracking performance, steady state error, domain of convergence, realtime computation load and required a priori model information are derived.
Learning control theory for robotic motion
Authors: Arimoto Suguru
Published: July 2007

A new concept of learning control for the improvement of robot motions is proposed, which can be referred to a mathematical modelling of learning and generation of motor programmes in the central nervous system. It differs from conventional classical and modern control techniques. It stands for the repeatability of operating a given robotic system and the possibility of improving the command input on the basis of actual measurement data acquired at the previous operation. Hence adequate conditions on the repeatability and invariance of the system dynamics are assumed, but no precise description of the dynamics is required for construction of the learning algorithms. Two types of iterative learning algorithm are proposed: one uses a PD‐type (proportional and differential) update of input commands and the other a PI‐type (proportional and integral) update where velocity signals are regarded as outputs. It is shown that in both types a better performance is realized at every attempt of operation, provided a desired motion is givena prioriand the actual motion (velocity signals) can be measured at every operation. Further, the robustness of such learning control algorithms with respect to the existence of perturbed errors of initialization of the robot, disturbances and measurement noise during operation is analysed in detail. It is shown that in PD‐type learning laws such errors are neither amplified nor aggregated in later consecutive trials of operation. In the case of PI‐type learning laws it is shown that such a robustness property is assured if a forgetting factor is adequately introduced into the repetitive learning law.
Controllers which guarantee robustness with respect to unmodelled flexibilities for a class of uncertain mechanical systems
Authors: Corless Martin
Published: July 2007

We consider a class of uncertain mechanical systems containing flexible elements and subject to memoryless output‐feedback controllers. The damping and stiffness properties of some of the flexible elements are parametrized linearly in μ−1 and μ−2 respectively, where μ > 0 and these components become more rigid as μ approaches zero. We propose a class of ‘stabilizing’ controllers for a system model in which the above components are rigid. Subject to a ‘linear growth condition’, the controllers also stabilize the model in which the components are flexible provided μ > 0 is sufficiently small.
An autosynthesizing non‐linear control system using a rule‐based expert system
Authors: Taylor James H. , Stringer Philip G.
Published: July 2007

An advanced non‐linear control system is obtained by combining recent developments in non‐linear control system synthesis with a rule‐based system approach to real‐time control. The basic problem to be addressed is the control of a non‐linear plant which is sufficiently sensitive to both operating point and input amplitude that the desired control system performance can be obtained only with a non‐linear controller that is ‘retuned’ or ‘resynthesized’ whenever the operating point changes significantly. (The term ‘changes significantly’ in this context signifies an operating point change that causes the non‐linear control system input/output behaviour to change substantially in an undesirable way.) This is accomplished by a hierarchically organized intelligent control system with a conventional reprogrammable controller under the direction of a rule‐based expert system. The function of the expert system is to

(i) monitor the behaviour of the non‐linear control system to determine when retuning or resynthesis is required; if the behaviour is satisfactory, then continue passive monitoring; else

(ii) when retuning or resynthesis is required: set up and execute experiments to derive the model information required to tune or synthesize a new non‐linear controller in terms of a given structure and parametrization; execute the retuning/resynthesis procedure; reprogramme the controller (download the parametrization); and recommission the updated non‐linear control system and return to normal operation.

In essence, the rule‐based system provides supervisory control (‘meta‐control’) for a conventional reprogrammable non‐linear controller that will autotune or autosynthesize as required. Autosynthesis takes place to accommodate variability in plant behaviour due to operating point changes, and the nonlinear controller thus synthesized corrects for amplitude sensitivity. This concept represents one way to combine artificial intelligence with control; it will be discussed and illustrated by example below.
An expert system for interaction analysis of multivariable systems
Authors: Lieslehto J. , Koivo H. N.
Published: July 2007

In this paper a demonstration expert system for interaction analysis of time‐invariant, linear, multivariable systems has been developed. The principle is to choose an area that is significant in control design but still limited to be implementable. To our knowledge no such systems have been reported in the literature. The paper first briefly describes the general idea of the developed expert system. Next the software structure of the expert system is given. A short description of the interaction analysis and conclusions finish the paper.
Adaptive lqg controller with loop transfer recovery
Authors: Tay T. T. , Moore J. B.
Published: July 2007

In this paper we propose for scalar plants an adaptive LQG controller with adaptive input sensitivity function/loop transfer recovery of an associated adaptive LQ design. The sensitivity recovery can be viewed as a frequency‐shaped loop recovery where the weights involve a sensitivity function. The adaptive loop/sensitivity recovery is achieved by feeding back the estimation residuals to the control through a stable bounded input, bounded output (BIBO) adaptive filter Qk. For simplicity we consider fixed but uncertain plants in the model set and identification schemes where there are consistent parameter estimates. For non‐minimum phase plants an asymptotic partial recovery is achieved via a recursive least squares update of the BIBO filter Qk. The degree of recovery can be prescribed a priori between zero and the maximum possible. For the case of minimum phase plant estimates, full loop recovery may be achieved asymptotically by prescribing a maximum degree of recovery.

The motivation for proposing the new adaptive control algorithm is to enhance robustness of adaptive LQG designs, taking advantage of the robustness enhancement properties of sensitivity/loop recovery for off‐line designs. The robustness properties of the new algorithm are demonstrated by simulation results.
Lp‐stability of estimation errors of kalman filter for tracking time‐varying parameters
Authors: Zhang J. F. , Guo L. , Chen H. F.
Published: July 2007

The Kalman filtering algorithm, owing to its optimality in some sense, is widely used in systems and control, signal processing and many other fields. This paper presents a detailed analysis for theLp‐stability of tracking errors when the Kalman filter is used for tracking unknown time‐varying parameters. The results of this paper differ from the previous ones in that the regression vector (in a linear regression model) or the output matrix (in state space terminology) is random rather than deterministic. The context is kept general so that, in particular, the time‐varying parameter is allowed to be unbounded, and no assumption of stationarity or independence for signals is made.
A long‐range adaptive controller for time‐varying plants with sudden changes
Authors: Costa B. M. , Lemos J. M.
Published: July 2007

This paper is concerned with fault detection for adaptive control. Experimental results of the long‐range adaptive controller MUSMAR coupled with a fault detection mechanism are presented. The experiments were performed on pilot systems in real time. The fault detection mechanism was used to improve the performance of the MUSMAR controller during the occurrence of faults.
Decentralized indirect adaptive control of interconnected systems
Authors: Datta A. , Ioannou P.
Published: July 2007

Continuous time decentralized indirect adaptive controllers are proposed for a large‐scale system composed of N interconnected linear subsystems with unknown parameters. Each local adaptive law utilizes a normalizing signal which is generated using the local input and the outputs of all the subsystems. This requires the exchange of output signals between the different subsystems, so that the scheme is no longer totally decentralized. Nevertheless, the degree of computational complexity is about the same as that of a totally decentralized scheme and this makes the scheme very attractive.
A retrospective self‐tuning controller
Authors: Goodhart S. G. , Burnham K. J. , James D. J. G.
Published: July 2007

A new self‐tuning control algorithm appropriate for industrial systems which exhibit non‐linear characteristics and fast dynamic behaviour is proposed. The resulting self‐tuning controller makes use of a weighted sum of the control input generated from a standard self‐tuning control algorithm combined with a set of control input suggestions evaluated retrospectively. Use is made of a past sequence of steady state gains evaluated over a prespecified retrospective horizon together with a filtered prediction of the current system output which is obtained from a steady state Kalman filter. Adopting such an approach provides an element of corrective action based on information which would otherwise be disregarded by a standard self‐tuning scheme.

Simulation studies involving a number of identified linear models of an industrial hydraulic servo system are presented. In terms of reduced control input variance and increased accuracy of set‐point following, the effectiveness of the new retrospective self‐tuning controller is compared to that of a standard self‐tuning pole placement controller and results indicate that significant improvements in overall performance are possible.
Modelling and self‐adjusting control of an absorption column
Authors: Najim K.
Published: July 2007

This paper describes the model and solution of the constrained optimal control problem associated with a packed absorption column. The control problem is solved using a learning automaton operating in a random environment. On the basis of physical and chemical laws, a model has been developed. It consists of three hyperbolic partial non‐linear differential equations. A solution of diethanolamine (the absorbent) is used to absorb the CO2 contained in a gas mixture. The primary manipulated variables are the flow rate of the absorbent and the concentration of CO2 in the gas mixture. The control objective is to maintain the concentration of CO2 close to a desired value, subject to control limit restriction, in order to avoid the flooding of the column. It leads to a stochastic programming problem, the solution of which is closely associated with the behaviour of an automaton in a random environment corresponding to the column. Detailed computer simulation results which demonstrate the performance of this automaton controller are presented
Adaptive predictive control of a continuous fermentation process
Authors: Dahhou B. , Chamilothoris G. , Roux G.
Published: July 2007

This work is concerned with the automatic control of biotechnological processes, in particular the regulation of a biomass concentration inside a continuous fermentation process. To overcome the difficulties often encountered with classical regulators, we use adaptive scheme techniques. We give a predictive control scheme using on‐line estimation of the specific growth rate (considered as a time‐varying parameter) which takes into account the feed effect on this parameter and calculate the dilution rate at each step.
Linear adaptive control of a single‐tether system
Authors: Greene M. E. , Carter J. T. , Walls J. L.
Published: July 2007

A control law for a single‐tether orbiting satellite system based on a reduced order linear adaptive control technique is presented. The main advantages of this technique are its design simplicity and the facts that specific system parameters and model linearization are not required when disigning the conroller. Two controllers are developed: one which uses only tension in the tether as control actuation and one which uses both tension and in‐plane thrusters as control actaution. Both a sixth‐order non‐linear and an 11th‐order bead model of a tethered satellite system are used for simulation purposes, demonstrating the ability of the controller to manage an uncertain system. Retrieval and stationkeeping results using these non‐linear models and the linear adaptive controller demonstrate the feasibility of the method. The robustness of the controller with respect to parameter uncertainties is also demonstrated by changing the non‐linear model and parameters whithin the model without redesigning the controller.
Model reference adaptive control applied to electrical machines
Authors: Unbehauen H. , Keuchel U.
Published: July 2007

This contribution reports about a discrete‐time robust adaptive control strategy for single‐input/single‐output systems with arbitrary zeros. The adaptive algorithm is mainly based on a direct model reference method. The basic idea presented here is to divide the feedback law into a direct adaptive and a fixed part. By a certain choice of the non‐adaptive controller parameters the robustness of the adaptive control loop with respect to unmodelled dynamics of the plant may be increased. The non‐adaptive part of the controller can be reformulated as a bypass to the plant and will be denoted as the ‘correction network’ owing to its action on the open‐loop zeros of the augmented plant. The new adaptive control strategy removes the major drawbacks of model reference control and is investigated for speed control of a DC motor and voltage control of a synchronous generator.
Robust adaptive control of robotic manipulators without the regressor matrix
Authors: Yuan Jing , Stepanenko Yury
Published: July 2007

A new adaptive controller is presented here for rigid‐body robotic manipulators. It is stable and robust with respect to a class of external disturbances. The robustness of the adaptive controller is established without the ‘slow‐varying’ assumption and the computationally demanding regressor matrix. The control law consists of a non‐adaptive PD control part and an adaptive control part. It uses two adaptive matrices to compensate two uniformly bounded coefficient matrices derived from the original dynamics. A α σ|q̇|‐modified adaptive law is designed to adjust the adaptive matrices. A Lyapunov‐type stability analysis indicates that the closed‐loop system is uniformly ultimately bounded. The tracking error and compensation error will eventually converge into a closed region, which can be made arbitrarily small by adjusting the controller parameters. Simulation results are included to demonstrate the performance of the proposed controller.
The method of recursive aim inequalities in adaptive control theory
Authors: Bondarko V. A. , Yakubovich V. A.
Published: July 2007

The method of recursive aim inequalities has been used in the theory of adaptive control since the late 1960s by a group of Leningrad mathematicians. The problem is reduced to an infinite system of recursive inequalities on the vectors of unknown parameters. These inequalities can be solved by using specially developed, finitely convergent algorithms. The method is illustrated by the solution of adaptive control problems for linear discrete systems under different assumptions on the plant and disturbances.
Adaptation problems in expert systems
Authors: Gorodetsky V. I.
Published: July 2007

Expert system (ES) theory problems have been traditionally considered without any essential connection with common control problems. However, nowadays, because of progress in ES theory development in practice and because of its wide use for solving complex control problems, new items in this field of research are being stressed, many of which are connected with traditional automation control.

The combined study of adaptation and ES problems makes it possible to draw significant practical conclusions for systems of both types. In this work an analysis of general problems and of possible approaches to solving them in some special types of ESs based on Bayes inference is carried out. The main attention is focused on such ES characteristics as: (i) inference stability (with respect to variations in probabilistic approximation measures of rule truth); (ii) knowledge base adaptation (rule modification and new rule creation or approximation measure rule tranformation); (iii) knowledge base consistency maintenance.
The Kalman‐Yakubovich Lemma for non‐stationary systems and its applications
Authors: Savkin A. V.
Published: July 2007

An existence problem for non‐stationary Lyapunov functions is considered. This problem is connected according to a certain rule with the linear Hamiltonian system that is called adjoint. We apply this method to obtain conditions of absolute stability and adaptive stabilization for non‐linear non‐stationary systems.
Adaptive control of a biped robot walking across a horizontal plane
Authors: Shishkin S. L.
Published: July 2007

The problem of biped robot walk control is considered. Intertial robot parameters are assumed unknown and control actions are applied only twice during one step. For adaptation the recurrent aim inequalities method is used. The control algorithm is designed and theoretically justified. Computer simulation results are presented.
A comparative study of normalized and unnormalized tuning errors in parameter adaptive control
Authors: Morse A. S.
Published: July 2007

In the past year several advances in the development of adaptive control for non‐linear systems have made it clear that algorithms not using error normalization have several significant advantages over algorithms which do, in spite of their much greater complexity. In this paper simple examples are used to give a detailed account of the essential differences in structure between adaptive algorithms of the normalized and unnormalized types. It is explained why only the latter readily generalize to non‐linear process models with non‐global Lipschitz non‐linearities. It is shown that one of these algorithms is capable of stabilizing linear process models with different relative degrees.
Error‐based adaptive non‐linear control and regions of feasibility
Authors: Teel Andrew R.
Published: July 2007

We recast a recently developed adaptive stabilization algorithm for pure‐feedback form non‐linear systems into an error‐based algorithm. This enlarges the subset of pure‐feedback form non‐linear systems that can be stabilized globally (with respect to the state of the system.)
Adaptive regulation: Lyapunov design with a growth condition
Authors: Praly Laurent
Published: July 2007

We propose a new Lyapunov design of an adaptive regulator under some restriction on the dependence of a Lyapunov function on the parameters.

This restriction has been introduced by Praly et al. Its interest is to involve only a Lyapunov function and not explicitly the system non‐linearities. We show it is satisfied by strict pure feedback systems with polynomial growth non‐linearities and some other non‐feedback linearizable systems.

Our new Lyapunov design leads to an adaptive regulator where the adapted parameter vector is transformed before being used in the control law; namely, the so‐called certainty equivalence principle is not applied. Unfortunately, the implementation of this regulator needs the explicit solution of a fixed point problem, so in a second stage we propose a more practical solution obtained by replacing the fixed point static equation by a dynamical system with this fixed point as equilibrium.
Adaptive instrumental variable methods for frequency estimation
Authors: Kangas Ari , Stoica Petre , Söderström Torsten
Published: July 2007

This paper considers adaptive instrumental variable methods (IVMs) for estimating the frequencies of noise‐corrupted sinusoidal signals. Adaptive implementations of both the unweighted and the optimally weighted IVMs are given. Since the optimal weighting matrix depends on the (unknown) signal parameters, we examine ways to estimate this quantity on‐line. A theoretical and an experimental study show that the optimally weighted IVM gives more accurate and less biased estimates than the unweighted IVM. The improved accuracy is obtained at the expense of increasing computational complexity.

The IVMs determine the AR part of an exactly parametrized ARMA description of the sinusoids‐in‐noise process. A fast, adaptive algorithm to estimate the frequencies from the AR coefficients is proposed. The method is based on simultaneous estimation of all roots of the AR polynomial.
The α‐adaptive performance in pole assignment adaptive controller design
Authors: Wang L. , Owens D. H.
Published: July 2007

Choice of the desired closed‐loop polynomial plays an important role in pole assignment adaptive controller design, but it is difficult to specify a ‘good’ closed‐loop polynomial before the process is known. This paper suggests the desired closed‐loop polynomial to be chosen as a function of the identified model so that the gain of the adaptive controller becomes ‘controllable’ by the designer. Simulation examples are used to show that the proposed strategy has advantages over the conventionally fixed closed‐loop polynomial in both design procedure and performance achievement.
Discretization of analogue filters via H∞ model‐matching theory
Authors: Toivonen H. T.
Published: July 2007

Approximation of analogue filters by digital filters is performed using H∞ model‐matching theory. In this approach the input signal is assumed to belong to a frequency‐weighted ball in the Lebesgue space L2 of continuous square‐integrable signals and a digital filter is designed so as to minimize the norm of the worst error between the outputs of the digital and analogue filters. An analysis of the frequency response shows that if the set of input signals is sufficiently band‐limited, the procedure corresponds to the minimization of a weighted minimax frequency response error criterion. Numerical examples show that the approach offers an efficient procedure for discretizing general multivariable systems.
Hybrid adaptive‐robust control for a robot manipulator
Authors: Dawson D. M. , Qu Z. , Lewis F. L.
Published: July 2007

In this paper we present a hybrid adaptive‐robust controller to achieve trajectory following for a robot manipulator. The controller consists of two parts: a proportional‐derivative (PD) feedback loop and an adaptive‐robust law for the manipulator dynamics. An advantage of this method is that our controller takes advantage of the manipulator dynamic structure to allow the designer to select a controller that is a combination of an adaptive approach and a robust‐adaptive approach.
Adaptive control of non‐completely controlled mechanical systems using dynamic feedback linearization and estimation design
Authors: Boustany F. , D'Andréa‐Novel B.
Published: July 2007

We propose here an indirect adaptive scheme for a class of non‐completely controlled mechanical systems comprising a classical manipulator terminated by a simple pendulum. In the case of known parameters a dynamic state feedback produces full linearization. An adaptive version is obtained by a simple estimation method together with a certainty equivalence law. Global stability is proved without growth conditions. Simulation results for an overhead crane are given.
A skid‐to‐turn missile autopilot design: The generalized predictive adaptive control approach
Authors: Bendotti P. , M'Saad M.
Published: July 2007

This paper deals with an adaptive control application involving a skid‐to‐turn (STT) missile. More specifically, the generalized predictive adaptive control approach is used to accommodate the yaw channel control problem. The time domain behaviour and stability margins are shown to demonstrate the adaptive control performances vis‐a‐vis those of the underlying fixed parameter control.
Implementation of self‐tuning generalized predictive controllers for the process industry
Authors: Camacho Eduardo F. , Bordons Carlos
Published: July 2007

This paper presents a set of simple Ziegler‐Nichols‐type functions for implementing generalized predictive controllers (GPCs). By using this set of functions, the implementation of GPCs is simplified considerably and the heavy computational requirements needed are avoided. The functions have been obtained for a CARIMA model that is valid for most processes in industry, as is the case of plants whose behaviour can be described in terms of a static gain, time constant and effective dead time.
Non‐linear learning control of robot manipulators without requiring acceleration measurement
Authors: Qu Zhihua , Zhuang Hanqi
Published: July 2007

A new class of non‐linear learning control laws is introduced for a robot manipulator to track a given trajectory in performing a series of tasks. The learning control scheme is applicable to robots with both resolute and prismatic joints, requires only position and velocity feedback, and removes the acceleration measurement required by the existing results. It has been shown that under the proposed learning control the tracking errors are always guaranteed to be asymptotically stable with respect to the number of trials. The proposed control is robust in the sense that exact knowledge about the non‐linear dynamics is not required except for bounding functions on the magnitudes. In addition, the new learning scheme can be used without assumptions such as repeatability of robot motion, repeatability of tasks and resetting of initial tracking errors.
Multi‐window spectrum estimation — A linear algebraic approach
Authors: Onn Ruth , Steinhardt Allan O.
Published: July 2007

The purpose of this paper is to elucidate the multi‐window method for spectral estimation and sinusoid detection. The multi‐window method, devised by D. J. Thomson, has many features that distinguish it from other spectrum estimation techniques. It has an elegant derivation, starting from first principles and using optimality arguments. It lends itself to performance analysis and provides a built‐in measure of its reliability. In contrast to Thomson's integral equation derivation, we use linear algebraic methods both to treat the problem and to develop the solution. In addition to the tutorial, we present an overview of published generalizations and analyses of the method.
Adaptive feedback linearization for position control of a switched reluctance motor: Analysis and simulation
Authors: Amor Lotfi Ben , Dessaint Louis‐A. , Akhrif Ouassima , Olivier Guy
Published: July 2007

In this paper a non‐linear adaptive feedback‐linearizing control is designed for a fifth‐order model of a three‐phase switched reluctance motor (SRM) which includes both electrical and mechanical dynamics. This non‐linear adaptive control structure compensates for all the non‐linearities between inputs and outputs, allows the use of a linear controller for motion tracking and improves the performance by reducing torque ripple of the SRM.

A validated non‐linear model of the SRM is used for the system simulation, while the control algorithm contains an adaptive scheme based on the parametrized model. Simulation results are given to demonstrate the effectiveness of the control method.
A new approach to adaptive robust control
Authors: Lee Wee Sit , Anderson Brian D. O. , Kosut Robert L. , Mareels Iven M. Y.
Published: July 2007

A new approach is given for the design of adaptive robust control in the frequency domain. Starting with an initial model of a stable plant and a robust stabilizing controller, the new (windsurfer) approach allows the bandwidth of the closed‐loop system to be increased progressively through an iterative control‐relevant system identification and control design procedure. The method deals with both undermodelling and measurement noise issues. Encouraging results are obtained in the simulations that illustrate the new idea.
Model‐system parameter mismatch in GPC control
Authors: Ordys Andrzej W.
Published: July 2007

This paper considers a linear discrete‐time system controlled by a GPC regulator. However the parameters and dynamic order of the system assumed for the regulator design differ from the true parameters. Hence there is a model‐system parameter mismatch. For this case it is shown how to determine the stability of the system and then how to calculate the mean values and variances of input and output signals. The formulae presented give some insight to the robustness of the stochastic GPC controller.
Asymptotic stability of a class of adaptive systems
Authors: Ortega Romeo , Fradkov Alexander
Published: July 2007

In this short paper we give conditions for uniform asymptotic stability of the equilibrium of a class of non‐linear non‐homogeneous differential equations. This result is of interest in adaptive systems theory, where this strong stability allows us to establish some robustness properties.
Global partially decentralized adaptive stabilization of discrete‐time systems
Authors: Ortega Romeo , M'Saad Mohammed
Published: July 2007

It is shown in this paper that the inclusion of a ‘centralized’ normalization signal in an otherwise decentralized adaptive control scheme is sufficient to ensure global convergence for a well‐defined class of interconnections. The latter is characterized by a frequency domain condition on the best attainable transfer matrix for the given controller structure. Our main contributions are that we circumvent the need for ‘sufficiently weak’ interconnection assumptions that prevails in the available literature and provide for the first time rigorous conditions for global convergence instead of just signal boundedness.
Transient bounds of dynamic certainty equivalent adaptive controllers
Authors: Ortega Romeo
Published: July 2007

In this paper we show that the transient bounds of Morse's dynamic certainty equivalent adaptive controller established previously by us can be considerably strengthened. Specifically, we derive a computable bound on the ℒ︁∞‐norm of the tracking error which, in contrast with the local bound obtained previously by us, holds for all system initial conditions. Also, we add an ‘adaptation gain’ in the high‐order estimator to prove that, by increasing this gain, the ℒ︁∞‐norm of the tracking error can be made arbitrarily small without increasing its ℒ︁∞ norm. This is an improvement over the results obtained with backstepping designs, where these performance measures must be traded off in the selection of the adaptation speed.
Non‐linear system identification and control based on neural and self‐tuning control
Authors: Abdulaziz A. , Farsi M.
Published: July 2007

This paper deals with identification and control of non‐linear systems that contain linear parameters and a non‐linear quasi‐input function of the real input signal. Least squares have been used to estimate the linear parametric part, while a neural feedforward net is employed to estimate the remaining non‐linearity. Performance considerations are illustrated by hybrid algorithm simulations.
Non‐linear adaptive control via laguerre expansion of volterra kernels
Authors: Dumont Guy A. , Fu Ye
Published: July 2007

A truncated Wiener‐Volterra functional series is used to represent the input‐output relation of a non‐linear dynamic system. By expanding Volterra kernels using orthonormal Laguerre functions, a Wiener‐type non‐linear model consisting of a Laguerre network followed by a memoryless Taylor expansion function is obtained. When functional series and Laguerre series truncations are allowed, an appropriate choice of the Laguerre filter pole permits a description of the process dynamics with a small number of parameters. Because the resulting model is linear in the parameters, those parameters can be estimated using a simple recursive least‐squares (RLS) algorithm. Furthermore, a predictive control law is derived on the basis of the model and an industrial plant adaptive control simulation is performed. This method can be used for the modelling and control of a large class of non‐linear dynamic systems.