Process Control in High-Noise Environments Using A Limited Number Of Measurements

[摘要] The topic of this dissertation is the derivation, development, and evaluation of novel hybrid algorithms for process control that use a limited number of measurements and that are suitable to operate in the presence of large amounts of process noise.As an initial step, affine and neural network statistical process models are developed in order to simulate the steady-state system behavior. Such models are vitally important in the evaluation, testing, and improvement of all other process controllers referred to in this work. Afterwards, fuzzy logic controller rules are assimilated into a mathematical characterization of a model that includes the modes and mode transition rules that define a hybrid hierarchical process control. The main processing entity in such framework is a closed-loop control algorithm that performs global and then local optimizations in order to asymptotically reach minimum bias error; this is done while requiring a minimum number of iterations in order to promptly reach a desired operational window.The results of this research are applied to surface mount technology manufacturing-lines yield optimization. This work achieves a practical degree of control over the solder-paste volume deposition in the Stencil Printing Process (SPP). Results show that it is possible to change the operating point of the process by modifying certain machine parameters and even compensate for the difference in height due to change in print direction.

[发布机构] University:Georgia Institute of Technology;Department:Electrical and Computer Engineering

[效力级别] Algorithm theory

[关键词] Affine estimator;Algorithm theory;Constrained conjugated gradient method;Control law generation;Control values;Function evaluation;High noise environment;Hybrid control algorithm;Least squares;LS;Multiple function evaluations;Noise;Printed circuit board manufacturing;Process control;Stencil printing processes;Windowed smoothed block form