Because friction-induced nonlinearities in positioning systems an generally range of motion-dependent, dual-model or dual-stage strategies are often adopted to deal with the inconsistencies encountered when a system m...
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Because friction-induced nonlinearities in positioning systems an generally range of motion-dependent, dual-model or dual-stage strategies are often adopted to deal with the inconsistencies encountered when a system moves from submicrometer steps (micro mode) to larger scale strokes (macro mode). Although good performance is usually obtained when each model/stage operates in its designed range of motion, a system frequently performs less satisfactorally when operating near the switching point between models or stages. An air-lubricated capstan drive was used in this study to minimize the discrepancy between macro and micro modes, and a single-mode MRAC was developed to control the capstan drive system for precision positioning. Accuracy better than +/-15 nm with no overshooting was achieved in all conditions tested (including 50 nm, 500 nm and 10 mum steps). Disturbance resistance of the system also proved to be satisfactory. (C) 2000 Elsevier Science inc. All rights reserved.
In this paper is presented the application of a control strategy that interpolates the model reference adaptive control (MRAC) and the Variable Structure model reference adaptive control (VS-MRAC) applied to an induct...
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In this paper is presented the application of a control strategy that interpolates the model reference adaptive control (MRAC) and the Variable Structure model reference adaptive control (VS-MRAC) applied to an induction motor. The combination of these strategies is obtained with concepts from Fuzzy Logic, where is used the idea of error magnitude for activating each controller in correct time. We used the Takagi-Sugeno Inference model to obtain an analytic expression for the adaptation law. The application to the induction motor shows that this new strategy adaptation incorporates the advantages of both MRAC and VS-MRAC.
A nonlinear adaptivecontrol strategy based on radial. basis function networks and principal component analysis is presented. The proposed method is well suited for low dimensional nonlinear systems that are difficult...
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A nonlinear adaptivecontrol strategy based on radial. basis function networks and principal component analysis is presented. The proposed method is well suited for low dimensional nonlinear systems that are difficult to model and control via conventional means. The effective system dimension is reduced by applying nonlinear principal component analysis to state variable data obtained from open-loop tests. This allows the radial basis functions to be placed in a lower dimensional space than the original state space. The total number of basis functions is specified a priori, and an algorithm which adjusts the location of the basis function centers to surround the current operating point is presented. The basis function weights are adapted on-line such that the plant output asymptotically tracks a linear referencemodel. A highly nonlinear polymerization reactor is used to compare the nonlinear adaptivecontroller to a linear state feedback controller that utilizes the same amount of plant information. (C) 2000 Elsevier Science Ltd. All rights reserved.
Recent results on the adaptivecontrol of linear time-varying systems have considered mostly the case in which the range or rate of parameter variations is small. In this paper, a new state feed-back modelreference a...
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Recent results on the adaptivecontrol of linear time-varying systems have considered mostly the case in which the range or rate of parameter variations is small. In this paper, a new state feed-back model reference adaptive control is developed for systems with bounded arbitrary parameter variations. The important feature of the proposed adaptivecontrol is an uncertainty estimation algorithm, which guarantees almost zero tracking error. Note that the conventional parameter estimation algorithm in the adaptivecontrol guarantees only bounded tracking error. Copyright (C) 2000 John Wiley & Sons, Ltd.
This paper presents a single phase AC power source which is able to generate sinusoidal waveforms with adjustable amplitudes and frequencies over a wide range, as well as several arbitrary waveforms. The setup is base...
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The paper proposes a reconfigurable flight-control system for the tracking of altitude, heading, sideslip, and velocity commands. The control law can serve for the command of unmanned air vehicles or as an autopilot f...
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The paper proposes a reconfigurable flight-control system for the tracking of altitude, heading, sideslip, and velocity commands. The control law can serve for the command of unmanned air vehicles or as an autopilot for piloted aircraft. The inner core of the algorithm consists of a reconfigurable control system providing tracking of pitch-, roll-, and yaw-rate commands. It is based on a modelreferencecontrol law and a stabilized recursive least-squares algorithm. The outer loop is based on a linear design, with compensation for the nonlinear couplings arising,from flight dynamics. Some parameters of the outer loop are identified in real time in order to adapt to varying flight conditions. The algorithm is evaluated using a nonlinear F-16 simulation model. The results demonstrate the consistent performance of the algorithm through various flight conditions, as well as its turn coordination capabilities, its reconfiguration after a floating left elevator failure, its ability to move across the power curve, and its tolerance to measurement noise and-turbulence.
A novel nonlinear adaptive neural control methodology is presented for the challenging problem of deep-space spacecraft formation flying. When the framework of the circular restricted three-body problem with the sun a...
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A novel nonlinear adaptive neural control methodology is presented for the challenging problem of deep-space spacecraft formation flying. When the framework of the circular restricted three-body problem with the sun and Earth as the primary gravitational bodies is utilized, a nonlinear model is developed that describes the relative formation dynamics. This model is not confined to the vicinity of the Lagrangian libration points but rather constitutes the most general nonlinear formulation. Then, a relative position controller is designed that consists of an approximate dynamic model inversion, linear compensation of the ideal feedback linearized model, and an adaptive neural-network-based element designed to compensate for the model inversion errors. The nominal dynamic inversion includes the gravitational forces, whereas the model inversion errors are assumed to stem from disturbances such as fourth-body gravitational effects and solar radiation pressure. The approach is illustrated by simulations, which confirm that the suggested methodology yields excellent tracking and disturbance rejection, thus, permitting submillimeter formation keeping precision.
In this paper, a new modelreference decentralized adaptive output feedback controller is proposed for load-frequency control (LFC) of large-scale power systems with unknown parameters. The main problem with a decentr...
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In this paper, a new modelreference decentralized adaptive output feedback controller is proposed for load-frequency control (LFC) of large-scale power systems with unknown parameters. The main problem with a decentralized robust LFC is that the interactions are treated as disturbances. This results in a conservative control action to maintain stability in the worst-case scenario. Furthermore, to improve the performance of the decentralized LFC, the proposed method estimates the interactions from other subsystems to modify the adaptivecontroller so that the interactions are effectively neutralized. The other important features of the proposed controller are: (1) no prior information about the system parameters is required, (2) random changes in the operating conditions are traced, (3) only the local input-output data are needed, (4) the robustness of the overall system against the system parameter uncertainties is guaranteed. To show the effectiveness of the proposed controller, a three-area power system is studied. The simulation results are promising and highlight the remarkable performance of the controller even in the presence of both plant parameter changes and high interactions.
In this paper we address the adaptive and nonadaptivemodelreferencecontrol problem for a class of multivariable Linear time-varying plants, namely index-invariant ones. We show that, under appropriate controllabili...
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In this paper we address the adaptive and nonadaptivemodelreferencecontrol problem for a class of multivariable Linear time-varying plants, namely index-invariant ones. We show that, under appropriate controllability and observability conditions, this class of plants admits a fractional description in terms of polynomial differential operators and, as such, allows for a polynomial equation-based controller design. We also show that, for a modelreferencecontrol objective, the controller can be designed by solving a set of algebraic equations. Further, when the plant parameters are only partially known, we employ a gradient-based adaptive law with projection and normalization to update the controller parameters and establish the stability and tracking properties of adaptive closed-loop plant. Finally, we present a simple example to illustrate the design and realization of both the adaptive and nonadaptivecontrol laws.
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 modelreference parametric adap...
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