When actuators and sensors on a controlled mechanical structure form naturally passive pairs, which usually requires physical collocation, very robust stabilizing controllers can be constructed. If the number of such ...
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When actuators and sensors on a controlled mechanical structure form naturally passive pairs, which usually requires physical collocation, very robust stabilizing controllers can be constructed. If the number of such naturally passive pairs is small, the closed loop performance may not be satisfactory. This motivates the incorporation of additional sensors in the control design. However, the passivity property no longer holds for these sensors. Some thoughts are presented on extending the passivity based design to incorporate these non-collocated sensors by using some nominal model information to synthesize a fictitious strictly passive output. Simulation and experimental studies are presented to show the merits of this approach.< >
This paper presents a passivity based approach to the stabilization of a flexible beam and its application to a testbed at the Picatinny Arsenal. The basic control design consists of tuning an inner passive loop for t...
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This paper presents a passivity based approach to the stabilization of a flexible beam and its application to a testbed at the Picatinny Arsenal. The basic control design consists of tuning an inner passive loop for the rigidbody response, synthesizing a passive output based on an observer and all the available outputs, and finally filtering the synthesized passive output by an optimal passive filter before closing the loop. Previous simulation and experimental experience in the C La MS laboratory at Rensselaer has shown the efficacy of this approach. Preliminary simulation results based on the Picatinny model are encouraging. Experimental validation on the physical testbed is currently planned.
The main processes involved in the manufacture of integrated circuits are deposition, lithography, etching, and implantation. This paper describes results on identification and real-time control of reactive ion etcher...
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The main processes involved in the manufacture of integrated circuits are deposition, lithography, etching, and implantation. This paper describes results on identification and real-time control of reactive ion etchers. Experimental results show that significant disturbances to the etch rate can be attenuated with real-time feedback control.< >
This paper describes the development of real-time control technology for the improvement of manufacturing characteristics of reactive ion etchers. A general control strategy is presented as well as supporting experime...
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This paper describes the development of real-time control technology for the improvement of manufacturing characteristics of reactive ion etchers. A general control strategy is presented as well as supporting experimental results.
In this paper, we consider the problem of robust pole placement using fixed-order compensation. It is assumed that the coefficients of the numerator and denominator polynomials of the uncertain plant depend muitilinea...
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In this paper, we consider the problem of robust pole placement using fixed-order compensation. It is assumed that the coefficients of the numerator and denominator polynomials of the uncertain plant depend muitilinearly on the "parameters" and that the design specifications are given in the form of a two-pole dominant model. We present a design methodology for choosing a fixed-order compensator which ensures that all the roots of the closed loop polynomial lie in the regions prescribed by the two-pole dominant model.
The Joint Detection/Estimation Filter (JDEF) [1,2] can be utilized to improve the performance of a single Extended Kalmart Filter (EKF) when the problem of estimation involves a large initial variance. In this case th...
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The Joint Detection/Estimation Filter (JDEF) [1,2] can be utilized to improve the performance of a single Extended Kalmart Filter (EKF) when the problem of estimation involves a large initial variance. In this case the JDEF is used to estimate the presence and location of a point target when the received signal is corrupted by multipath. In the formulation of the problem it was assumed that both the target and sensor were stationary and that there was an equal probability of the point target being present or absent along a center look direction. This implementation of the JDEF proved to be quite successful and can be easily expanded to estimate a larger number of unknown parameters.
This paper addresses the problem of multi sensor detection and high resolution signal parameter estimation using joint maximum a posteriori detection and high order nonlinear filtering techniques. The model-based fusi...
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This paper addresses the problem of multi sensor detection and high resolution signal parameter estimation using joint maximum a posteriori detection and high order nonlinear filtering techniques. The model-based fusion approach offers the potential for increased target resolution in range/azimuth space. The technique employs joint detection/estimation filters (JDEF) for target detection and parameter localization. The JDEF approach segments the aggregate nonlinear model over the entire target resolution space into subcells. This partitioning leads to extremely accurtate detection and parameter estimation. The proposed JDEF approach has a built-in capability for automatic data alignment from multiple sensors, and can be used for centralized, noncentralized, and distributed data fusion.
In this paper high order vector filter equations are developed for estimation in non-Gaussian noise. The difference between the filters developed here and the standard Kalman filter is that the filter equation contain...
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In this paper high order vector filter equations are developed for estimation in non-Gaussian noise. The difference between the filters developed here and the standard Kalman filter is that the filter equation contains nonlinear functions of the innovations process. These filters are general in that the initial state covariance, the measurement noise covariance, and the process noise covariance can all have non-Gaussian distributions. Two filter structures are developed. The first filter is designed for systems with asymmetric probability densities. The second is designed for systems with symmetric probability densities. Experimental evaluation of these filters for estimation in non-Gaussian noise, formed from Gaussian sum distributions, shows that these filters perform much better than the standard Kalman filter, and close to the optimal Bayesian estimator.
This paper addresses the problem of multisensor detection and high resolution signal parameter estimation using joint maximum a posteriori detection and high order nonlinear filtering techniques. The model-based fusio...
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This paper addresses the problem of multisensor detection and high resolution signal parameter estimation using joint maximum a posteriori detection and high order nonlinear filtering techniques. The model-based fusion approach offers the potential for increased target resolution in range/azimuth space. The technique employs joint detection/estimation (JDE) filters for target detection and target parameter localization. The JDE approach segments the aggregate nonlinear model over the entire target resolution space into resolution subcells. This partitioning leads to extremely accurtate detection and parameter estimation. The proposed JDE approach has a built-in capability for automatic data alignment from multiple sensors, and can be used for centralized, noncentralized, and distributed data fusion.
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