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model reference PARAMETRIC ADAPTIVE ITERATIVE LEARNING control

IFAC Proceedings Volumes 2002年第1期35卷 469-474页

作者： H. Yu M. Deng T.C. Yang D.H. Owens School of Engineering and Computer Science Exeter University Exeter EX4 4QF School of Engineering and Information Technology Sussex University Brighton BN1 9QT Department of Automatic Control and Systems Sheffield University Sheffield S1 3JD

Most of iterative learning control (ILC) methods requires that the relative degree of the plant is less than 2 for a linear system or the plant is passive for a non-linear system. A new model reference parametric adaptive iterative learning control using the command generator tracker (CGT) theory is proposed in this paper. The method can be applied to control a plant with a higher relative degree and it only requires to iteratively adjust n m + 2 parameters for an SISO plant. Therefore, the ILC control system is very simple. The proposed method is in the spirit of simple adaptive control which has received intensive researches during past two decades. Simulation results show the effectiveness and usefulness of the proposed method.

关键词： Iterative learning control Adaptive control Strictly positive real model reference control

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model reference adaptive control of nonlinear systems using RLLM networks

Model reference adaptive control of nonlinear systems using ...

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2000 American control Conference (ACC 2000)

作者： Ali, A Ashfaq, M Schmid, C Ruhr Univ Bochum Dept Elect Engn & Informat Sci Control Engn Lab D-44780 Bochum Germany

ISBN: (纸本)0780355199;0780355202

A model reference adaptive controller for nonlinear systems is implemented using rectangular local linear model (RLLM) network approach. An RLLM network is used to identify the plant as NARX model. Another network of the same kind consisting of linear controllers is tuned to achieve the closed-loop behaviour of a linear reference model. A master slave configuration is realised to decide about the activation of candidate linear controllers in the controller network The implemented control scheme is tested in simulations and real-time control on a hydraulic positioning system.

关键词： adaptive control model reference control local model networks nonlinear systems artificial neural networks

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model reference PI control of a Multivariable System with Saturation - A Case Study

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IFAC Proceedings Volumes 2000年第4期33卷 475-480页

作者： P. Stewart V. Kadirkamanathan Department of Automatic Control and Systems Engineering University of Sheffield Sheffield UK

The permanent magnet AC motor drive (PMAC) is a multivariable, nonlinear, 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 magnitudes of the command signals, operating the system to just within the bound of saturations, allowing the PI controllers to accurately track the command signals and retains control of the current vector. This regime ensures maximum possible dynamic performance of the system. The system and controller is simulated and experimentally verified, controller gains are found by Monte Carlo simulation.

关键词： Saturation Feedback Linearisation model reference control P I control

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model reference control in two degree of freedom control systems: Adaptive min-max approach

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IEE PROCEEDINGS-control THEORY AND APPLICATIONS 1999年第3期146卷 273-281页

作者： Vilanova, R Serra, I Univ Autonoma Barcelona Automat Control & Syst Engn Grp Fac Sci Bellaterra 08193 Spain

The design of a control system, that provides a model reference performance specification for the command response and, at the same time, achieves some prespecified feedback properties, is discussed. The use of a particular configuration of a two degree-of freedom controller will allow the feedback and performance specifications to be tackled independently through the design of a feedback controller for the nominal plant and the model reference part by means of model matching in a min-max sense. A closed-form solution is provided for the optimal min-max controller. This closed-form solution will be very useful when using this controller in an adaptive way. The resulting adaptive controller will consist of a fixed feedback part and an adaptive open-loop part. Only one degree-of-freedom is adapted. An example will show that it is possible to match the model reference performance specification, even near the stability limit.

关键词： performance specifications Self-adjusting control systems stability limit model reference adaptive control systems control system synthesis prespecified feedback properties optimal control model reference control closed-form solution optimal min-max controller model matching adaptive min-max approach Optimal control two degree of freedom control systems command response control system analysis and synthesis methods feedback

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Velocity scheduled driver assisted control

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INTERNATIONAL JOURNAL OF VEHICLE DESIGN 2002年第1-2期29卷 1-22页

作者： Lynch, D Alleyne, A Univ Illinois Dept Mech & Ind Engn Urbana IL 61801 USA

This paper investigates the effectiveness of velocity-scheduled Driver Assisted control (DAC) to control the yaw rate of a front wheel drive, four-wheel steer passenger vehicle. The goal of this research is to be able to impart new handling characteristics to a vehicle through the entire range of operational longitudinal velocities. The DAC uses vehicle yaw rate as input and the rear steering angle as output, thus allowing the driver to maintain a direct line of vehicle control via the front wheels for safety reasons. Multiple DAC controllers are designed for discrete longitudinal velocities within the vehicle's operating range. A transition methodology was then implemented to switch between DAC controllers as the longitudinal velocity changes. Finally, the controller was tested experimentally on a scaled vehicle testbed.

关键词： four-wheel steer driver-assisted control model reference control scaled vehicle gain scheduling

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Simultaneous stabilization with near-optimal model reference tracking

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SYSTEMS & control LETTERS 2002年第1期46卷 31-43页

作者： Miller, D Kennedy, D Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada Ryerson Polytech Univ Dept Elect & Comp Engn Toronto ON M5B 2K3 Canada

In this paper we consider the use of linear time-varying controllers for simultaneous stabilization and performance. We prove that for every finite set of plants, we can design a linear time-varying controller which provides not only closed-loop stability, but also near-optimal model reference tracking. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： simultaneous stabilization optimal control model reference control time-varying control

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An introduction to the use of neural networks in control systems

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR control 2002年第11期12卷 959-985页

作者： Hagan, MT Demuth, HB De Jesús, O Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74075 USA Univ Colorado Dept Elect & Comp Engn Boulder CO 80309 USA

The purpose of this paper is to provide a quick overview of neural networks and to explain how they can be used in control systems. We introduce the multilayer perceptron neural network and describe how it can be used for function approximation. The backpropagation algorithm (including its variations) is the principal procedure for training multilayer perceptrons;it is briefly described here. Care must be taken, when training perceptron networks, to ensure that they do not overfit the training data and then fail to generalize well in new situations. Several techniques for improving generalization are discussed. The paper also presents three control architectures: model reference adaptive control, model predictive control, and feedback linearization control. These controllers demonstrate the variety of ways in which multilayer perceptron neural networks can be used as basic building blocks. We demonstrate the practical implementation of these controllers on three applications: a continuous stirred tank reactor, a robot arm, and a magnetic levitation system. (C) Copyright 2002 John Wiley Sons, Ltd.

关键词： neurocontrol model reference control model predictive control feedback linearization

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model reference robust speed control for induction-motor drive with time delay based on neural network

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS 2001年第6期31卷 746-753页

作者： Chen, TC Sheu, TT Natl Cheng Kung Univ Dept Engn Sci Tainan Taiwan

This paper proposes a novel model reference robu st speed control with load torque estimator and feedforward compensation based on neural network (NN) for induction motor drives with time delay. First, a two-layer neural network torque estimator (NNTE) is used to provide a real-time identification for unknown load torque disturbance. The back-propagation algorithm was used as the learning algorithm. In order to guarantee the system's convergence and to obtain faster NN's learning ability, a Lyapunov function is also employed to find the bounds of the learning rate. Since the performance of the closed-loop controlled induction motor drive is influenced greatly by the presence of the inherent system dead time during wide-range operations, a dead time compensator (DTC) and the model reference following controller (MRFC) using a neural network proportional controller (NNPC) are proposed to enhance the robustness of the PI controller. The theoretical analysis, simulation, and experimental results demonstrate that the proposed model reference robust control scheme can improve the performance of induction motor drive with time delay and reduce its sensitivity to system parameter variations and load torque disturbances.

关键词： dead time compensator (DTC) induction motor model reference control neural network (NN) torque estimator

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A KINEMATIC AND DYNAMIC model-BASED MOTION controlLER FOR MOBILE ROBOTS

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IFAC Proceedings Volumes 2002年第1期35卷 427-432页

作者： J.R. Asensio L. Montano Department of Computer Science and Systems Engineering University of Zaragoza María de Luna 3 E-50015 Zaragoza SPAIN

The paper presents a model for motion generation of differential-drive mobile robots. The parameters of the dynamic model allow adjusting the robot translational and rotational behaviours separately. The model takes into account the robot kinematic and dynamic constraints, making the velocities and accelerations bounded and compatible with those the robot can perform. The main contribution of the paper is to use the model itself as a motion controller: under soft hypothesis on the velocities and accelerations, this approach allows an easy tuning of the controller parameters. A system stability and parameters sensitivity analysis is developed, in order to get guidelines for controller tuning. The clear physical sense of the parameters make this tuning easy and intuitive. Experimental results involving a real mobile robot show the performance of this approach.

关键词： model-Based control model reference control Robot Dynamics Robot Kinematics Robot Navigation

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A robust decentralized model reference adaptive control for non-minimum-phase interconnected systems

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AUTOMATICA 1999年第8期35卷 1499-1508页

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

In this paper, we present a decentralized model reference adaptive control for interconnected subsystems in the sense that no information exchange occurs between the subsystems. The approach is based on the interconnection output estimation using the polynomial series which offers a general solution for interconnected subsystems. The parameter estimation scheme is a combined adaptive data filtering with a recursive least-squares algorithm with parameter projection and normalization. The problem of minimum phased subsystems is handled by an adaptive input-output data filtering. Hence the zeros of each subsystem 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 stability conditions based on weak interconnections are relaxed. Also the robustness of the proposed adaptive control against unmodeled dynamics is stated. Finally, the results are illustrated by numerical examples. (C) 1999 Elsevier Science Ltd. All rights reserved.

关键词： adaptive decentralized control model reference control polynomial approach interconnection estimation nonminimum phase data filtering

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