Investigation into the applications of genetic algorithms to control engineering

Bibliography: pages 117-120.This thesis report presents the results of a study carried out to determine possible uses of genetic algorithms to problems in control engineering. This thesis reviewed the literature on the subject of genetics and genetic algorithms and applied the algorithms to the problems of systems parameter identification and Pl/D controller tuning. More specifically, the study had the following objectives: To investigate possible uses of genetic algorithms to the task of system identification and Pl/D controller tuning. To do an in depth comparison of the proposed uses with orthodox traditional engineering thinking which is based on mathematical optimisation and empirical studies. To draw conclusions and present the findings in the form of a thesis. Genetic algorithms are a class of artificial intelligence methods inspired by the Darwinian principles of natural selection and survival of the fittest. The algorithm encodes potential solutions into chromosome-like data structures that. are evolved using genetic ·operators to determine the optimal solution of the problem. Fundamentally, the evolutionary nature of the algorithm is introduced through the operators called crossover and mutation. Crossover fundamentally takes two strings, selects a crossing point randomly and swaps segments of the strings on either side of the crossover point to create two new individuals. There are three variations of crossover which were considered in this thesis: single point crossover, two point crossover and uniform crossover. It was important that these be given careful consideration since much of the outcome of the algorithm is influenced by both the choice and the amount with which they are applied