bluuebunny/crossref_metadata_2025_split huggingface dataset:
Performance enhancement of two‐degree‐of‐freedom controllers via adaptive techniques
Authors: Tay T. T. , Moore J. B. 
Published: July 2007

AbstractThis paper generalizes earlier results on adaptive disturbance estimate (innovations) feedback regulation to the case of adaptive tracking, refining some of the techniques and formulations in the process. The adaptations proposed search within the class of all stabilizing two‐degree‐of‐freedom controllers for a nominal plant and minimize a reference signal/disturbance rejection measure. The emphasis in the paper is on issues relevant to the tracking task and simulation results to demonstrate the effectiveness of the approach.The adaptive scheme is based on results concerning the convenient characterization of the class of all stabilizing two‐degree‐of‐freedom controllers in terms of an arbitrary filter Q with a stable proper transfer function matrix Q(s). The fact that the closed‐loop transfer matrices are affine in Q(s) permits a straightforward on‐line least‐squares update of the parameters of the filter Q on‐line. The theory for the class of stabilizing controllers is used to set up appropriate signal preprocessing.The direct adaptive schemes proposed turn out to be recursive prediction error schemes which have local convergence properties. When convergent, they ensure enhancement of the performance of a fixed controller in other than the nominal plant case.
A new algorithm for constrained adaptive array processing
Authors: Zhu Y. M. , Yin G. 
Published: July 2007

AbstractA new approach for algorithms of linearly constrained adaptive array processing is developed in this paper. The new algorithm is the product of an effort to provide a more efficient procedure for real‐time implementation of adaptive array processing. The essence of our approach is to improve the efficiency by utilizing a number of processors updating the same array element. A delay is introduced in the computation for each processor. The structure (delay and multiprocessors) of our algorithm requires all processors to operate asynchronously and in a pipeline manner so that the recursively computed data flow in a rhythmic fashion, passing through each processor periodically. The almost sure convergence of the algorithm is proved under the assumption of non‐stationary and correlated signals.
On the adaptive control of remotely operated underwater vehicles
Authors: Goheen Kevin R. , Jefferys E. Richard 
Published: July 2007

AbstractConventional autopilots for remotely operated underwater vehicles (ROVs) are difficult to design because the vessels' dynamics are strongly coupled, significantly non‐linear and vary according to the operating configuration. In addition, the derivation of the open‐loop dynamics requires the use of expensive, specialized testing equipment and is prone to error. A solution is to implement adaptive controllers. Three such schemes are presented here: the first two use a robust control law based on a simplified model of the open‐loop dynamics and off‐ and on‐line identification, while the third is an implicit linear quadratic on‐line adaptive controller. These controllers are evaluated by examining their performance when controlling a comprehensive non‐linear ROV simulation. The navigational hardware needed to realize an adaptive ROV auotopilot is also discussed.
Extended self‐tuning predictors, smoothers and filters for two‐dimensional data fields
Authors: Wagner G. R. , Wellstead P. E. 
Published: July 2007

AbstractThe paper describes new self‐tuning algorithms for two‐dimensional signal processing. Specifically, extended algorithms are developed for filtering, smoothing and prediction of two‐dimensional data fields. These algorithms provide a transfer function approach to the problems of smoothing and prediction in two‐dimensional space with arbitrary lag and lead. As such, the fully two‐dimensional self‐tuning smoother and predictor derived in this paper represent an important generalization of the previous work which concerned prediction and filtering with arbitrary lead/lag on the current line. The generalization is in two parts. The first concerns the formal extension of the theory. The second concerns the development of efficient algorithms for calculation of the extended predictors and filters, with special attention being paid to the approximations required in order to realize the algorithms.
New results on the structure of non‐linear systems: Application to the (dynamic) block‐decoupling problem
Authors: Descusse J. 
Published: July 2007

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

AbstractThe objective of this paper is to explore several robust adaptive control schemes for a class of partially known robot systems described by the equationM(q)q̈ + F(q, q̇)q̇ + G(q) = f(t) wheref(t) is the control input. First we take advantage of any known system dynamics to simplify the adaptive control problem for the unknown portion of the dynamics. Then a model‐reference adaptive control based on the Lyapunov stability criterion is designed for the remaining unknown portion of the plant. Using the Lyapunov approach, under a slowly time‐varying assumption we derive a magnitude bound and amaximum achievable convergence ratefor the error. The magnitude bound as well as the convergence rate are functions of the reference model degree of stability α (‐ α is the maximum real part of model poles).We show that there is an optimal location for the reference model poles that isindependent of the robot dynamics.This value of α is well defined, and if the feedback gains are further increased, making the model more stable,the performance of the adaptive system is degraded.It is also shown that the gains in the adaptive portion of the algorithm may be made as high as desired in order to make the actual performance approach the maximum achievable convergence rate. The effect of these adaptive gainsdoesdepend on the unknown dynamics. Simulation results will demonstrate the improved performance of the adaptive controller and the detrimental effects of high gains on the system performance.Next the slowly time‐varying assumption is relaxed to obtain more realistic bounds on the error. Formulation into a more structured system also allows us to derive a control law that does not require accelerations.
Merging ‘reasoning’ and filtering in a bayesian framework—some sensitivity and optimality aspects
Authors: Forsman K. , Ljung L. , Millnert M. , Skeppstedt A. 
Published: July 2007

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

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

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

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

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

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

AbstractAn adaptive blood pressure control system based on the long‐range or finite‐time‐horizon prediction strategy known as generalized predictive control has been examined. The system was designed to run as a real‐time, multitasking operating system providing real‐time monitoring and control of mean arterial pressure. The ability to control in the presence of constraints was also incorporated into the system.Results from both unconstrained and constrained control runs were obtained from experiments on aneasthetized dogs. The problems caused by model‐plant mismatch, varying time delays and non‐linearity were handled satisfactorily by the system. The disturbance rejection of the constrained algorithm is better than the unconstrained one. The results demonstrate the applicability of both the constrained and unconstrained generalized predictive control algorithm for regulation of mean arterial pressure.
Passive least‐squares‐type estimation algorithm for direct adaptive control
Authors: Brogliato Bernard , Lozano Rogelio 
Published: July 2007

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

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

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

AbstractThe evolution of adaptive control systems is considered from the viewpoint of the use of informatics and artificial intelligence (AI) tools. Some general principles of intellectronics (AI tools) are discussed taking into consideration adaptive control systems with non‐linear dynamics operating under conditions of uncertainty. The principles are illustrated by an analytical review of adaptive‐intelligent robot control and logical‐probabilistic decision rules as applied to expert systems of adaptive control. Example of the use of intellectronics in adaptive systems are given.
Preliminary results about robust lagrange stability in adaptive non‐linear regulation
Authors: Jiang Z. P. , Praly L. 
Published: July 2007

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

AbstractThe structure underlying most adaptive algorithms is a linear parametrized set. This set either represents the universe of controllers the adaptive algorithm can choose from in order to achieve the control task or it may represent a class of models the adaptive algorithm may exploit to approximate the open‐loop plant behaviour. In the latter approach the controller used to close the loop is selected via some non‐linear map of the identified model parameters. The emphasis is on approximating the vector field that generates the trajectories of the system.Alternatively we propose to predict the trajectories over a short period of time directly, not indirectly involving a representation of the underlying vector fields. The feasibility of such an approach using a one‐step‐ahead‐type algorithm for both prediction and control is analysed. The scheme is hybrid in that the plant is continuous‐time, whilst the control action is implemented in discrete time. The control action is of the model reference type. The algorithm is applied to a class of (non)‐linear time‐varying systems of a given structure (known relative degree) and possessing a stable inverse. Given input/output measurements only, the algorithm can enforce a desired response within guaranteed error bounds. Robustness properties with respect to partial state measurement (e.g. neglecting parasitic dynamics) and violation of the stable inverse assumption are investigated.
Identification of a class of dynamic errors‐in‐variables models
Authors: Zheng Wei‐Xing , Feng Chun‐Bo 
Published: July 2007

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

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

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

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

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

AbstractIn this paper we study new relationships between a class of Lyapunov functions and the passivity theorem. It is proved that under some (sufficient) conditions a Lyapunov‐stable system can be analysed as the feedback connection of two (strictly) passive subsystems. It is also shown that very recent adaptive schemes for linear plants of any relative degree can in a certain sense be unified through a passivity point of view.