Paper Type

Master's Thesis


College of Computing, Engineering & Construction

Degree Name

Master of Science in Electrical Engineering (MSEE)



NACO controlled Corporate Body

University of North Florida. School of Engineering

First Advisor

Dr. Chiu Choi

Second Advisor

Dr. Patrick Kreidl

Third Advisor

Dr. Daniel Cox

Department Chair

Dr. Murat Tiryakioglu

College Dean

Dr. Mark Tumeo


This thesis describes a genetic approach for shaping the dynamic responses of linear state space systems through pole placement. This paper makes further comparisons between this approach and an island approach parallel genetic algorithm (PGA) which incorporates orthogonal mutation vectors to increase sub-population specialization and decrease convergence time.

Both approaches generate a gain vector K. The vector K is used in state feedback for altering the poles of the system so as to meet step response requirements such as settling time and percent overshoot. To obtain the gain vector K by the proposed genetic approaches, a pair of ideal, desired poles is calculate first. Those poles serve as the basis by which an initial population is created. In the island approach, those poles serve as a basis for n populations, where n is the dimension of the necessary K vector.

Each member of the population is tested for its fitness (the degree to which it matches the criteria). A new population is created each “generation” from the results of the previous iteration, until the criteria are met, or a certain number of generations have passed. Several case studies are provided in this paper to illustrate that this new approach is working, and also to compare performance of the two approaches.