Active control of sound and vibration has been the subject of a lot of research in recent years, and examples of applications are now numerous. However, few practical implementations of nonlinear active controllers have been realized. Nonlinear active controllers may be required in cases where the actuators used in active control systems exhibit nonlinear characteristics, or in cases when the structure to be controlled exhibits a nonlinear behavior. A multilayer perceptron neural-network based control structure was previously introduced as a nonlinear active controller, with a training algorithm based on an extended backpropagation scheme. This paper introduces new heuristical training algorithms for the same neural-network control structure. The objective is to develop new algorithms with faster convergence speed (by using nonlinear recursive-least-squares algorithms) and/or lower computational loads (by using an alternative approach to compute the instantaneous gradient of the cost function). Experimental results of active sound control using a nonlinear actuator with linear and nonlinear controllers are presented. The results show that some of the new algorithms can greatly improve the learning rate of the neural-network control structure, and that for the considered experimental setup a neural-network controller can outperform linear controllers.
This paper describes a flexible, completely digital, scanning tunnelling microscope
developed around a fixed-point (TMS320C542) digital signal processor. During
the development special attention has been paid to the cost of the instrument,
without limiting its performance, and in some regards enhancing it. The
instrument has been developed and tested in the air, at room temperature, and
atomic resolution has been achieved. Its software provides a maximum of
support to the user. The tip approach is completely automated. The control
parameters can be adjusted based on an on-line identification and off-line (in
simulation) optimization. This technique is completely integrated to the control
software. It greatly simplifies the parameter optimization, and completely
eliminates the risk of collision between the tip and the sample during the
optimization. The scanning of the image and control of the tunnelling
current are implemented in software by the DSP. This allows the precise
identification and real-time compensation of the capacitive coupling between the
scan tube electrodes and the current detector. The image analysis and
processing software allows slope compensation, as well as the presentation of
differential image, two-dimensional FFT and three-dimensional image.
This article describes the development and test of a digital control loop, to control the tip-to-specimen distance in a scanning tunneling microscope. This digital controller performs a frequency-independent linearization of the gap-to-current relationship, as well as the compensation of the undesirable capacitive coupling between the electrodes of the scan tube and the input of the current-to-voltage converter, two difficulties normally associated with analog controllers. In the described work, the control loop is implemented on an inexpensive fixed-point DSP, processing the signals at a 25 kHz sampling rate.
The main drawback of the multichannel filtered-X LMS (FX-LMS) algorithm for the active noise control (ANC) of broadband disturbances is its low convergence speed when the filtered reference signals are strongly correlated, producing a large eigenvalue spread in the global correlation matrix. This correlation can be caused either by autocorrelation of the signals of the reference sensors, or by coupling between the ‘‘error paths’’ which introduces intercorrelation in the filtered reference signals. Multichannel versions of fast convergence monochannel algorithms exist (Newton-LMS, RLS, fast Kalman), but these algorithms either require too many computations for practical implementations, or they require the optimization of the controller to be performed at each sample, which can be a serious constraint. The purpose of this paper is to introduce a multichannel algorithm that has a high convergence speed and a low computational load, close to the FX-LMS. It is called the cosine transform filtered-X LMS (CTFX-LMS) because it uses a discrete cosine transform to eliminate the correlation that slows down the convergence process. The fundamental differences between this algorithm and many previously published frequency domain algorithms will be explained. Results of active noise control experiments in ducts will validate the convergence behavior of the new algorithm.
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