A benchmark study of two self-organizing artificial neural network models, ART2 and DIGNET, is conducted. The architecture differences and learning procedures between these two models are compared. The performance of ...
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In this paper, an adaptive controller with improved transient and steady-state performance using parameter mismatch compensation and high-order tuning is presented. As in previous designs, the use of a properly design...
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The extended kalman filter is applied to localize a target in a plane from range and bearing measurements and from bearing only measurements received at an observer. The model is a constant bearing between and the tar...
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In this paper, a new method for the computation of the optimal step in gradient algorithms for a class of linear quadratic optimal control problems is presented. This method improves the convergence of the gradient al...
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In this paper, a new method for the computation of the optimal step in gradient algorithms for a class of linear quadratic optimal control problems is presented. This method improves the convergence of the gradient algorithms and is optimal, i.e., it out performs any other step searching scheme in the above class of problems.
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...
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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作者：
P.C. BreedveldElectrical Engineering Department
Control Systems and Computer Engineering Group Control Laboratory and Mechatronics Research Centre Twente University of Twente Enschede Netherlands
The author identifies incentives for the use of computer-aids in modelling and design of physical systems and discusses possible solutions.
The author identifies incentives for the use of computer-aids in modelling and design of physical systems and discusses possible solutions.< >
In this paper, an adaptive controller with improved transient and steady-state performance using parameter mismatch compensation and high-order tuning is presented. As in previous designs, the use of a properly design...
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In this paper, an adaptive controller with improved transient and steady-state performance using parameter mismatch compensation and high-order tuning is presented. As in previous designs, the use of a properly designed signal, compensating for the effects of parameter mismatch on the output error, leads to "arbitrary" performance improvement. However, unlike previous designs based on first-order (classical) tuners, high-order tuning is necessary in order to avoid the effect of terms due to initial conditions on the output error. This constitutes a strong motivation for the use of high-order tuners in adaptive control which so far, were considered as more complicated alternatives to the classical first-order tuners. The proposed controller also provides robustness enhancement in the presence of bounded disturbances and/or a class of unmodeled dynamics or in the event of an adaptation switch-off end, as in classical model reference adaptive control, guarantees the exponential convergence of all errors to zero in the presence of sufficiently exciting reference inputs.
The problem of improved performance adaptive control (IPAC) of a class of linear and nonlinear systems is considered. A method for its solution is presented, the main feature of which lies in augmenting the "stan...
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The problem of improved performance adaptive control (IPAC) of a class of linear and nonlinear systems is considered. A method for its solution is presented, the main feature of which lies in augmenting the "standard" model reference adaptive controller by a properly designed signal compensating for the effect of plant parameter uncertainty on the output error. One of the main performance improvement characteristics of the proposed IPAC is that the zero-state output error can be made arbitrarily small under standard model reference adaptive control (MRAC) assumptions in the case of linear systems while a similar result holds for a class of linearizable systems as well. The exponential convergence of the output and parameter errors in the presence of sufficiently rich reference inputs, remains a valid property of this controller which also achieves improved robustness in the presence of bounded disturbances and/or unmodeled dynamics as well as in the case of an adaptation switch-off.
A benchmark study of two self-organizing artificial neural network models, ART2 and DIGNET, is conducted. The architecture differences and learning procedures between these two models are compared. The performance of ...
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A benchmark study of two self-organizing artificial neural network models, ART2 and DIGNET, is conducted. The architecture differences and learning procedures between these two models are compared. The performance of ART2 and DIGNET on data clustering and signal detection problems with noise or interference is investigated by comparative simulations. It is shown that DIGNET generally has faster learning and better clustering performance on the statistical pattern recognition problems. DIGNET has a simpler architecture, and the system parameters can be analytically determined from the self-organizing process. The threshold value used in DIGNET can be specifically determined from a given lower bound on the desirable signal-to-noise ratio (SNR). The networks discussed in this paper are applied and benchmarked against clustering and signal detection problems.
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