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.
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