bluuebunny/crossref_metadata_2025_split huggingface dataset:
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

AbstractIn 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

AbstractA 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

AbstractThis 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.
Global partially decentralized adaptive stabilization of discrete‐time systems
Authors: Ortega Romeo , M'Saad Mohammed 
Published: July 2007

AbstractIt 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

AbstractIn 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

AbstractThis 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

AbstractA 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.
Self‐tuning control of robot manipulators
Authors: Nicosia Salvatore , Tomei Patrizio 
Published: July 2007

AbstractWe consider the tracking control problem for robot manipulators whose parameters are not exactly known. It is assumed that some unmodelled friction is acting at the actuated joints. The proposed solution guarantees that the disturbances on the outputs (the joint positions) can be attenuated by an arbitrary factork. For the setpoint regulation problem a self‐tuning controller is proposed. In this case the disturbances (uncertain parameters and unmodelled non‐linearities) on the outputs are completely rejected asymptotically. Some simulation results are included.
Adaptive non‐linear speed control of induction motors
Authors: Raumer Thomas Von , Dion Jean Michel , Dugard Luc 
Published: July 2007

AbstractIn this paper we present two adaptive non‐linear speed control algorithms for induction motors. They employ input‐output linearization techniques for the motor model in the stator fixed reference frame. The first control algorithm directly tracks speed and rotor flux. The second is designed using torque and rotor flux tracking and is extended for speed control. A key point is that this algorithm ensures exact current limitation in the known parameter case. The adaptation based on Lyapunov design can cope with rotor and stator resistance variations. Significant simulations using sampled controllers are presented emphasizing adaptation and current limitation effects.
Adaptive performance enhancement by iterative identification and control design
Authors: Schrama Ruud J. P. , Bosgra Okko H. 
Published: July 2007

AbstractIn this paper we combine adaptation and robust control by an iterative scheme of system identification and control design. A nominal model is identified for robust control design and the controller is applied to the plant to gather new data for an update of the nominal model. Our objective is to improve the plant performance with every iteration right from the start. We tackle this problem with frequency domain identification and a control design method that optimizes robustness against coprime factor perturbations. Our approach is illustrated by simulated and practical examples.
Unifying the landmark developments in optimal bounding ellipsoid identification
Authors: Deller J. R. , Nayeri M. , Liu M. S. 
Published: July 2007

AbstractA general class of optimal bounding elipsoid (OBE) algorithms, including all methods published to date, is unified into a single framework called theunified OBE (UOBE)algorithm. UOBE is based on generalized weighted recursive least squares in which very broad classes of ‘forgetting factors’ and data weights may be employed. Different instances of UOBE are distinguished by their weighting policies and the criteria for determining optimal weight values.A study of existing OBE algorithms, with a particular interest in the trade‐off between algorithm performance interpretability and convergence properties, is presented. Results suggest that an interpretable, converging UOBE algorithm will be found. In this context a new UOBE technique, theset membership stochastic approximation (SM‐SA)algorithm, is introduced. SM‐SA possesses interpretable optimization measures and known conditions under which its pointestimatorconverges.
On‐line parameter interval estimation using recursive least squares
Authors: Gutman Per‐Olof 
Published: July 2007

AbstractA bank of recursive least‐squares (RLS) estimators is proposed for the estimation of the uncertainty intervals of the parameters of an equation error model (or RLS model) where the equation error is assumed to lie between known upper and lower bounds. It is shown that the off‐line least‐squares method gives the maximum and minimum parameter values that could have produced the recorded input‐output sequence. By modifying the RLS estimator in two ways, it is possible to recursively compute inner and outer bounds of the uncertainty intergals. It is shown that the inner bound is asymptotically tight.
Possible bursting phenomena in self‐tuning control
Authors: Radenkovic Miloje S. , Michel Anthony N. 
Published: July 2007

AbstractIn this paper, in the case when there are no modelling errors, bursting phenomena in extended‐least‐squares‐ (ELS‐) based self‐tuning control are analysed. It is shown that in the absence of external excitation and without imposing structural conditions on the system model, bursts of the tracking error and consequently drift of the parameter estimates are possible. By analysing the possible occurrence of bursting, an upper bound on the convergence rate of the tracking error is established. It is demonstrated that it is the self‐excitation mechanism which provides global stability of the adaptive system. In other words, the possible instability of the adaptive loop generates a bounded condition number of the covariance matrix. This in turn implies stabilization of the adaptive system. In this paper we also discuss a stochastic gradient algorithm with a convergence rate similar to that presently established for the ELS‐based self‐tuning controller.
Adaptive estimation of hidden nearly completely decomposable Markov chains with applications in blind equalization
Authors: Krishnamurthy Vikram 
Published: July 2007

AbstractThis paper proposes maximum likelihood (ML) estimation schemes for nearly completely decomposable Markov chains (NCDMCs) in white Gaussian Noise. Aggregation techniques based on stochastic complementation are applied to significantly reduce the dimension of the resulting hidden Markov model (HMM) and hence substantially reduce the computational requirements of the estimation algorithms. Stochastic complementation results in exact aggregation in that no approximations are involved in the steady state probability distribution of the Markov chain.We then present anoff‐lineestimation algorithm for the parameters and states of the HMM based on the estimation of the aggregated HMM. This off‐line algorithm is an ML estimation scheme and is based on the expectation maximization (EM) algorithm. It has a significantly reduced computational complexity compared with the standard (full‐order) EM‐based HMM estimation scheme.Finally we present an application of our techniques. We show that hidden NCDMCs can be used to formulate the blind equalization problem for noisy FIR channels with Markov inputs, e.g. phase‐shiftkeyed (PSK) signals. We then propose recursive EM and gradient estimation techniques for the aggregated HMM resulting inon‐lineestimates of the channel coefficients and signal estimate.For anNa‐state Markov chain our aggregate‐based estimation scheme has a computational complexityO(N−2a), whereas standard algorithms have a complexityO(Na−L+ 1) at each time instant, whereLis the length of the FIR channel.
Model reference adaptive control for robotic manipulators without velocity measurements
Authors: Schwartz Howard M. 
Published: July 2007

AbstractThis paper presents a non‐linear model reference adaptive controller for robot manipulators. Although the motivating application for this algorithm is in the field of robotics, the algorithm could be applied to non‐linear systems that are feedback‐linearizable. The algorithm developed does not require the measurement nor the estimation of manipulator joint velocities or accelerations. Simulation results verify the performance of the method.
A new adaptive LQG control algorithm
Authors: Birmiwal Kailash 
Published: July 2007

AbstractA new control algorithm is developed for the discrete time, finite horizon adaptive LQG problem. In the new algorithm, randomization over linear control functions is performed at future stages to find a deterministic desired control U(k) at the current stage. This randomized linear control policy, which is U(k)‐dependent and non‐linear in measurements, incorporates explicitly the knowledge available about the unknown parameter at future stages in terms of the posterior distribution. At the same time it enables us to obtain a closed form expression of an approximation of the optimal control‐cost function. Numerical examples comparing the new solution with the optimal are also provided.
Lowa prioristatistical information model for optimal smoothing and differentiation of noisy signals
Authors: Fioretti S. , Jetto L. 
Published: July 2007

AbstractA novel approach is proposed to the optimal smoothing and differentiation problem of unknown one‐dimensional signals corrupted by additive white Gaussian noise. State space techniques are used. the main feature of the method is that very littlea prioristatistical information about the signal generation process is required. Starting from the basic assumption that the actual waveform does not show relevant discontinuities, a state space representation is derived by defining a state vector composed of the signal and its derivatives. All parameters of this representation are analytically derived except two: a multiplicative scalar of the input noise covariance matrix and the variance of the measurement noise. A procedure for the optimal estimation of these parameters from noisy data is proposed. Application of a fixed‐lag Kalman smoother provides the simultaneous estimate of the signal and its derivatives. Numerical results confirm the validity of the approach.
Discontinuous pole assignment control of uncertain systems using only input/output measurements
Authors: Bartolini Giorgio , Ferrara Antonella 
Published: July 2007

AbstractThis paper deals with the combined use of continuous and discontinuous adaptation mechanisms in order to control uncertain linear time‐invariant systems described by I/O relationships. the complexity of the combined schemes proposed in the literature up to now relies on both the complexity of the underlying linear structure, i.e. the structure of the controller designed under the assumption of perfect knowledge of the plant parameters in order to attain the prescribed control objective, and the complexity of the additional scheme which needs to be introduced in the uncertain case so as to guarantee the stability of the relevant error equation as well as the convergence of the adjusted parameters. In the present paper it is shown how, by choosing a pole assignment control objective, it is possible to conceive a new combined variable structure‐adaptive control scheme which on the whole results in being simpler than those previously proposed in the literature.
Predictive algorithm for the roll control autopilot of a jet fighter aircraft
Authors: Kassapakis E. G. , Warwick K. 
Published: July 2007

AbstractPredictive controllers are often only applicable for open‐loop stable systems. In this paper two such controllers are designed to operate on open‐loop critically stable systems, each of which is used to find the control inputs for the roll control autopilot of a jet fighter aircraft. It is shown how it is quite possible for good predictive control to be achieved on open‐loop critically stable systems.
Membership function shape and the dynamic behaviour of fuzzy systems
Authors: Baglio S. , Fortuna L. , Graziani S. , Muscato G. 
Published: July 2007

AbstractIn this paper the influence of the shape of the membership functions on the dynamic behaviour of a fuzzy controlled system is investigated. Although several works on fuzzy logic have been presented in the literature, few efforts have been made to characterize the dynamic behaviour of a fuzzy system through the shape of the membership functions. A parametrization for a class of membership functions is introduced in this work, resulting in a variety of shapes. Various system steady state dynamics are analysed as a consequence of this change in shape and chaotic dynamics is shown to arise for some membership function shapes.
Adaptive dead‐beat control law for trajectory tracking of robotic manipulators
Authors: Jetto L. , Longhi S. , Papini A. 
Published: July 2007

AbstractThe purpose of this paper is to propose an adaptive dead‐beat controller for the trajectory tracking of a robotic manipulator. the dead‐beat compensator is self‐tuned to a linearized discretized model whose parameters are identified on‐line through a Kalman‐like estimator. to improve the convergence of the estimator and to obtain good control performances even in the case of time‐varying parameters, the state covariance matrix of the Kalman filter is adapted to the observed statistics of the innovation process. Numerical results have been obtained in a simulation context and refer to various operating conditions. They show that very good control performances in terms of maximum error are really obtainable. A comparison with minimum variance control is also reported.
Optimality properties in finite sample liidentification with bounded noise
Authors: Kacewicz B. , Milanese M. 
Published: July 2007

AbstractIn this paper we investigate finite sample optimality properties for worst‐case l2identification of the impulse response of discrete time, linear, time‐invariant systems. the experimental conditions we consider consist ofmexperiments of lengthN.the measured outputs are corrupted by component‐wise bounded additive disturbances with known bounds. the quantification of the identification error is given by the maximum l1‐norm of the difference between the true impulse response samples and the estimated ones, where the maximum is taken with respect to all admissible plants and all admissible disturbances.First we show that for any given experimental condition, almost‐optimal (within a factor of two) estimates can be obtained by solving suitable linear programmes.Then we study how experimental conditions affect the identification error. Optimality of the experimental data is measured by the diameter of information, a quantity which is at most twice as large as the minimal worst‐case error.We show that the minimum number of experiments allowing us to minimize the diameter of information is m−= 2−N. the values of the diameter of information and the corresponding optimal inputs are derived for the two extreme experimental conditions m = 1 and m = 2−N.
Uncertainty evaluation for estimates from recursive projection algorithms
Authors: Belforte G. 
Published: July 2007

AbstractIn this paper we consider parameter estimation of linear systems described byyi=aθ +ei, where the ith measurementyiis linearly dependent on the parameter vector θ ε ℝ−pthrough the regressor vectoraε ℝ−pand the measurement erroreiis unknown but bounded.Some properties of previously presented algorithms for recursive parameter identification in the unknown but bounded error (UBBE) context are discussed. In particular it is analysed how different levels of information on the error structure can influence the choice of the identification algorithms and the possibility of evaluating the reliability of the estimates. Attention is also focused on the influence that forgetting schemes have on the estimates and on their confidence evaluation.
An extension of Nyquist's theorem to non‐uniformly sampled finite length data
Authors: Belmont M. R. 
Published: July 2007

AbstractThis paper examines non‐uniformly sampled functions on a finite interval. the aim is to investigate what conditions must be satisfied in order to recover the baseband spectrum from such data. It is shown that the concept of band limitation inherent in Nyquist's theorem must be generalized into a quantity termed primary interval bandwidth limitation. This property is explored and various algorithms are developed, including an extension of the classical band‐limited interpolation formula. Measures are obtained that provide guidelines for assessing where approximate techniques can be employed and the use of these in adaptive scenarios is considered. the key results are illustrated by a set of numerical examples. the findings are presented in the context of time variables, but the approach is applicable to any type of sample domain. the treatment is one‐dimensional, as are the examples discussed, but the extension to multiple dimensions is immediate and straightforward.
Technical note self‐tuning ziegler‐nichols pid controller
Authors: Bobál Vladimír 
Published: July 2007

AbstractA new digital self‐tuning PID controller (STC PID) algorithm has been derived in this paper. the parameters of this controller are designed on the basis of a modified Ziegler‐Nichols criterion for digital control loops. the process is modelled as a controlled regression and its parameters are estimated using the recursive least squares method (RLSM) with LD decomposition and with directional forgetting applied. the algorithm is suitable for automatic setting of digital PID controllers for the control of deterministic processes or the adaptive control of stochastic processes.
A new indirect adaptive algorithm for feedforward control with application to active noise cancellation
Authors: Wang Angela Kuo , Ren Wei 
Published: July 2007

AbstractThis paper considers the deterministic adaptive feedforward control problem for disturbance rejection. A new indirect adaptive control algorithm is presented as an alternative to the well‐known ‘filtered‐X LMS’ algorithm when the disturbance is periodic with unknown frequency and the plant, possibly non‐minumum phase, is unknown. This new algorithm requires the solution of a Diophantine equation and a simple scheme to recursively solve this type of equation is suggested. the new algorithm is shown to converge without the use of a dither signal for plant modelling. Simulations demonstrate that the proposed algorithm has a substantially better transient response and convergence speed than the filtered‐X LMS algorithm. the applicability of the algorithm to the adaptive active‐noise‐cancelling problem is demonstrated using an identified acoustic model.
Amplitude‐constrained adaptive lqg control of first‐order systems
Authors: Królikowski A. 
Published: July 2007

AbstractThis paper considers the problem of adaptive LQG control design when the system control input is subject to an amplitude constraint. A first‐order stochastic system is considered mainly because its output can be satisfactorily described by a Gaussian probability density function (PDF) or can be calculated numerically. In this light, two algorithms are proposed. In the first algorithm an approach given by Toivonen has been adopted where the PDF of the output is approximated by the Gaussian density in order to develop a suboptimal adaptive feedback controller. the approach in the second algorithm is based on the numerically calculated PDF of the system output yielding a near‐optimal feedback gain. the proposed algorithms are compared with existing ones via simulations.