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American Society for Testing and Materials

Most of the signal processing that we will study in this course involves local operations on a signal, namely transforming the signal by applying linear combinations of values in the neighborhood of each sample point. You are familiar with such operations from Calculus, namely, taking derivatives and you are also familiar with this from optics namely blurring a signal. We will be looking at sampled signals only. Let's start with a few basic examples. Local difference Suppose we have a 1D image and we take the local difference of intensities, DI(x) = 1 2 (I(x + 1) − I(x − 1)) which give a discrete approximation to a partial derivative. (We compute this for each x in the image.) What is the effect of such a transformation? One key idea is that such a derivative would be useful for marking positions where the intensity changes. Such a change is called an edge. It is important to detect edges in images because they often mark locations at which object properties change. These can include changes in illumination along a surface due to a shadow boundary, or a material (pigment) change, or a change in depth as when one object ends and another begins. The computational problem of finding intensity edges in images is called edge detection. We could look for positions at which DI(x) has a large negative or positive value. Large positive values indicate an edge that goes from low to high intensity, and large negative values indicate an edge that goes from high to low intensity. Example Suppose the image consists of a single (slightly sloped) edge:

http://ieeexplore.ieee.org/iel5/5/31307/01456462.pdf

Linear systems

This book, written by two major figures in adaptive control, provides a wealth of material for researchers, practitioners, and students. While some researchers in adaptive control may note the absence of a particular topic, the book‘s scope represents a high-gain instrument. It can be used by designers of control systems to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs. The book is strongly recommended to anyone interested in adaptive control.

Adaptive Control

Medical Image Computing and Computer-Assisted Intervention

Hydrogels for biomedical applications.

Shape memory alloys (SMAs) offer a high power-to-weight ratio, large recovery strain, and low driving voltages, and have thus attracted considerable research attention. The difficulty of controlling SMA actuators arises from their highly nonlinear hysteresis and temperature dependence. This paper describes a combination of self-sensing and model-based control, where the model includes both the major and minor hysteresis loops as well as the thermodynamics effects. The self-sensing algorithm uses only the power width modulation (PWM) signal and requires no heavy equipment. The method can achieve high-accuracy servo control and is especially suitable for miniaturized applications.

https://www.mdpi.com/1424-8220/10/1/112/pdf

Tracking control of shape-memory-alloy actuators based on self-sensing feedback and inverse hysteresis compensation.

Mechanical devices usually come with undesirable nonlinearities, such as friction, backlashes, and saturations. Under the assumption of linear systems, the commonly seen identification schemes utilize sinusoidal excitation signals for parameter identification. However, the data needed for identification are unavoidably distorted by the aforementioned nonlinearities and the identification result may not be satisfactory. In the paper, binary test signals are used to perform identification, thus simplifying the behavior of friction. An identification method based on the difference of binary multifrequency excitation signals is proposed. The modified identification algorithm does not suffer from the problem of nonlinear distortions in the signal shape and is able to determine the nonlinear friction such that an accurate servo system model can be derived. A high-precision ball-screw table with asymmetric friction is identified as a test plant for this approach. The results prove that the method can be used very successfully.

Frequency-domain identification algorithms for servo systems with friction

This paper proposes a variable-sampling variable structure controller (VSC) for brushless dc (BLDC) motor drives with low-resolution position sensors (Hall sensors). The variable-sampling characteristics arising from the situation where measurements depend on the path-embedded sensors will result in the uncertainty of the discrete-time models. To circumvent this issue, a modification of the conventional discrete-time VSC control law for BLDC motor drives with Hall sensors is derived to achieve the robustness of speed control. Three measurement error-mitigation methods are also presented to reduce the errors due to low-resolution position feedback. Both simulated and experimental results confirm the effectiveness of the proposed controller and error-mitigation techniques.

A Variable-Sampling Controller for Brushless DC Motor Drives With Low-Resolution Position Sensors

http://ntur.lib.ntu.edu.tw/bitstream/246246/64129/1/2008_JPS.pdf

Jia-Yush Yen

Papers

Tracking control of shape-memory-alloy actuators based on self-sensing feedback and inverse hysteresis compensation.

Frequency-domain identification algorithms for servo systems with friction

A Variable-Sampling Controller for Brushless DC Motor Drives With Low-Resolution Position Sensors

Multivariable robust control of a proton exchange membrane fuel cell system

Adaptive nonlinear control of repulsive maglev suspension systems