Genetic algorithm application to controller optimization problems with non-analytic solutions

Abstract

Genetic algorithms (GAs) offer a numerical search method which does not require a statement of the mathematical relationship between the performance criteria and the parameter update rule. The objective of this study is to demonstrate that GAs provide a method of optimizing control system problems with analytically intractable constraints. A linear missile airframe and actuator state space model is developed, and a reduced order linear feedback controller is implemented. A genetic algorithm is constructed to optimize the controller parameters, first with respect to a weighted linear quadratic performance index. Penalty functions are then developed to introduce performance constraints on the maximum rise time, allowable settling error, and peak actuator effort.