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IFAC Proceedings Volumes 2005年第1期38卷 97-102页

作者： Domenico Bellomo David Naso Biagio Turchiano Robert Babuška Dipartimento di Elettronica ed Elettrotecnica Politecnico di Bari Via Re David 200 70125 Bari Italy Delft Center for Systems and Control Delft University of Technology Mekelweg 2 2628 CD Delft the Netherlands fax: +31 15 2786679

Adaptive fuzzy control has been a topic of active research over the last decade. However, most efforts have been directed toward one goal: achieving asymptotic stability and tracking. Little attention has been paid to the accuracy of the identified fuzzy models and to their transparency and interpretability whereas these should be the key aspects motivating the use of fuzzy models in adaptive control. The main contribution of this paper is to present an adaptive fuzzy controller with composite adaptive laws based on both tracking and prediction error. Compared to other adaptive fuzzy controllers, the proposed controller achieves smoother parameter adaptation, better accuracy and improved performance. It overcomes some of the drawbacks of similar schemes described in the literature on adaptive fuzzy control. The limitations of the proposed approach are also discussed.

关键词： Adaptive control fuzzy systems model reference control feedback linearization Lyapunov stability parameter estimation

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Robust model reference control for multivariable linear systems subject to parameter uncertainties

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND control ENGINEERING 2001年第I6期215卷 599-610页

作者： Duan, GR Liu, WQ Liu, GP Queens Univ Belfast Sch Mech & Mfg Engn Belfast BT9 5AH Antrim North Ireland Curtin Univ Technol Sch Comp Perth WA 6001 Australia Univ Nottingham Sch Mech Mat Mfg Engn & Managment Nottingham NG7 2RD England Harbin Inst Technol Dept Control Sci & Engn Harbin 150006 Peoples R China

Robust model reference control for multivariable linear systems with structural parameter uncertainties is considered. It is shown that the problem can be decomposed into two subproblems: a robust state feedback stabilization problem for multivariable linear systems subject to parameter uncertainties and a robust compensation problem. The latter concerns solution of three coefficient matrices such that four matrix equations are met and, simultaneously, the effect of the uncertainties to the tracking error is minimized. Based on a complete parametric solution to a class of generalized Sylvester matrix equations, the robust compensation problem is turned into a minimization problem with quadratic cost and linear constraints. A set of linear equations is derived that determines the optimal solution to the minimization. An example illustrates the application of the proposed approach.

关键词： linear systems model reference control parameter uncertainties robust stabilization robust compensation Sylvester matrix equations

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Dynamic model reference PI control of permanent magnet AC motor drives

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control ENGINEERING PRACTICE 2001年第11期9卷 1255-1263页

作者： Stewart, P Kadirkamanathan, V Univ Sheffield Dept Automat Control & Syst Engn Sheffield S1 3JD S Yorkshire England

The permanent magnet AC motor drive (PMAC) is a multivariable, non-linear, closely coupled system subject to saturation due to finite DC supply voltage and hard current limits for protection of the drive hardware. model following controls can be applied to this class of motor with PI current controllers enabling tracking of quadrature current command values. The presence of a finite supply voltage constraint results in reduced system performance when the current regulators saturate. A dynamic model reference controller is presented which includes the Currents and voltage limits, constraining the magnitude of the command signals, operating the system to just within the bound of saturation, allowing the PI controllers to accurately track the commanded values and retain control of the current vectors. This regime ensures maximum possible dynamic performance of the system. The system and controller is simulated and experimentally verified, controller gain being found by Monte Carlo simulation. (C) 2001 Elsevier Science Ltd. All rights reserved.

关键词： saturation feedback linearisation model reference control PI control

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model reference adaptive control using a low-order controller

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INTERNATIONAL JOURNAL OF ADAPTIVE control AND SIGNAL PROCESSING 2001年第3期15卷 239-263页

作者： Miller, DE Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada

In the model reference adaptive control problem, the goal is to force the error between the plant output and the reference model out put asymptotically to zero. The classical assumptions on a single-input-single-output (SISO) plant is that it is minimum phase, and that the plant relative degree, the sign of the high-frequency gain, and an upper bound on the plant order are known. Here we consider a modified problem in which the objective is weakened slightly to that of requiring that the asymptotic value of the error be less than a (arbitrarily small) pre-specified constant. Using recent results on the design of generalized holds for model reference tracking, here we present a new switching adaptive controller of dimension two which achieves this new objective for every minimum phase SISO system;no structural information is required. Copyright (C) 2001 John Wiley & Sons, Ltd.

关键词： adaptive control model reference control switching control

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An Adaptive Tuning Strategy of PID Attitude control System

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IFAC Proceedings Volumes 2004年第6期37卷 1009-1014页

作者： Yasser M. Abdallah Waldemar C. Leite Filho Instituto Nacional de Pesquisas Espaeiais Slio Jose dos Campos Brasil Instituto de Aeronáutiea e Espaço Slio Jose dos Campos Brasil

This paper describes a procedure for tuning PID attitude control system of a launch vehicle. The key idea is a method based on a model reference adaptive system to estimate PID control gains. The launch vehicle is considered a rigid body. Since the performance specification is considered as a second order system and the close loop of the launch vehicle PID attitude control system has fourth order with two zeros, there is no matching between model reference and launch vehicle control system. The simulation results of the proposed tuning method are compared with a LQR method.

关键词： PID control model reference control Adaptive system Attitude control

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Monopoli's model reference adaptive controller is incorrect

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IFAC Proceedings Volumes 2004年第12期37卷 1-12页

作者： Romeo Ortega Nikita Barabanov Alessandro Astolfi Laboratoire des Signaux et Systémes Supelec Plateau du Moulon 91192 Gif-sur-Yvette France Department of Mathematics North Dakota State University Fargo ND USA Electrical Engineering Department Imperial College Exhibition Road London SW7 2BT UK

One of the longest standing open questions in adaptive control concerns the correctness of the stability claim of the un–normalized model reference scheme proposed by R. V. Monopoli in 1974. Although provably correct solutions to the problem now abound, in particular, it is well–know that adding a normalization to Monopoli’s original scheme ensures global convergence, it is interesting to know whether this technique– driven modification is really necessary or only required to complete the stability proof in the absence of more elaborate arguments. In this paper we construct a counterexample that provides a definite—unfortunately, negative—answer to the claim. Instrumental for the establishment of this result is a technical lemma that shows that, under some conditions on the regressor that may appear in Monopoli’s scheme, the parameter error freezes as the adaptation gain goes to infinity. On the lighter side, we also prove that the counterexample can be “fixed”, in the sense of achieving semiglobal stability, adjusting some tuning parameters.

关键词： Adaptive control model reference control stability of adaptive systems nonlinear control

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A new approach to adaptive control: no nonlinearities

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SYSTEMS & control LETTERS 2003年第1期49卷 67-79页

作者： Miller, DE Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada

In adaptive control the goal is to design a controller to control an uncertain system whose parameters may be changing with time. Typically the controller consists of an identifier (or tuner) which is used to adjust the parameters of a linear time-invariant (LTI) compensator, and under suitable assumptions on the plant model uncertainty it is proven that good asymptotic behaviour is achieved, such as model matching (for minimum phase systems) or stability. However, a typical adaptive controller does not track time-varying parameters very well, and it is often highly nonlinear, which can result in undesirable behaviour, such as large transients or a large control signal. Furthermore, most adaptive controllers provide only asymptotic tracking, with no ability to design for a pre-specified settling time. Here we propose an alternative approach, which yields a linear periodic controller. Rather than estimating the plant or compensator parameters, instead we estimate what the control signal would be if the plant parameters were known. In this paper we argue the utility of this approach and then examine the first order case in detail, including a simulation. We also explore the benefits and limitations of the approach. (C) 2003 Elsevier Science B.V. All rights reserved.

关键词： model reference control linear time-varying control adaptive control

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Robust model reference control for Multivariable Linear Systems: A Parametric Approach

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IFAC Proceedings Volumes 2000年第14期33卷 83-88页

作者： G.R. Duan W.Q. Liu G.P. Liu School of Mechanical and Manufacturing Engineering The Queen's Univ. of Belfast Stranmillis Road Belfast BT9 5AH UK Harbin Institute of Technology Harbin 150001 P. R. China School of Computing Cunin Univ. of Tech. Perth. GPO 1987 WA 6845 Australia School of Mechanical Materials Manufacturing Eng. and Management University of Nottingham University Park Nottingham NG7 2RD U.K.

The problem of robust model reference control for multi variable linear systems with structural parameter uncertainties is considered. It is shown that the problem can be decomposed into two sub-problems. One is a robust state feedback stabilization problem for multi variable linear systems subject to parameter uncertainties; the other is the so-called robust compensation problem which concerns solution of three coefficient matrices such that four matrix equations are met and the effect of the uncertainties to the tracking error is minimized simultaneously. Based on a complete parametric solution of a class of generalized Sylvester matrix equations, the robust compensation problem is turned into a minimization problem with a quadratic objective and a set of linear constraints.

关键词： Linear systems model reference control parameter uncertainties robust stabilization robust compensation Sylvester matrix equations

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Biochemical bone fracture healing process model

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IFAC Proceedings Volumes 2003年第15期36卷 335-340页

作者： Mark A. Johnson University of New Mexico Albuquerque NM USA

Presented are an abbreviated background, architecture, and computer simulation results of the biochemical component of an integrated bioelectrical, biochemical, and biomechanical bone fracture healing process model (Johnson, 2002a, b). Background information gleaned from clinical and medical research literature is used to develop model architecture and processes. Fuzzy logic, chaotic systems, and artificial neural networks are the principle abstract soft computing methods used to identify and realize modeled processes. A model reference control architecture is used to implement the model. Simulation results versus literary evidence provide model verification and validation. More complete treatments are found in references Johnson (2002a, c).

关键词： Biomedical control oriented models Fuzzy logic Neural networks model model reference control Integration Process models

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A robust model reference adaptive control for non-minimum phase systems with unknown or time-varying delay

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AUTOMATICA 2000年第7期36卷 1057-1065页

作者： Makoudi, M Radouane, L LESSI Fac Sci Dept Phys MC-30000 Fes Morocco

In this paper, we present an indirect model reference adaptive control for SISO non-minimum phase systems with unknown or time-varying time delay. The parameter estimation scheme is a combined adaptive data filtering with a recursive least-squares algorithm with parameter projection and signal normalization. The problem of minimum phase of the plant is handled by an adaptive input-output data filtering. Hence, the zeros of the system-estimated model are relocated inside the unit circle. This estimated model, which is minimum phased, is then used for the control synthesis. It is shown that the adaptive input-output data filtering permits also to solve the difficult problem of model reference adaptive control in the case of unknown time-varying delay. The scheme robustness with respect to unmodelled dynamics and additive noise is also simultaneously improved. Finally, the results are illustrated by umerical examples. (C) 2000 Elsevier Science Ltd. All rights reserved.

关键词： model reference control non-minimum phase systems data filtering time-varying delay unmodelled dynamics robustness

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