Genetic algorithm-based modeling and optimization of control parameters of an air motor

Challenging optimization problems, which elude acceptable solutions via conventional methods, arise regularly in control systems engineering. Genetic algorithms are applied here to optimize the control gains for a controller of a pneumatic drive. We show that, by using minimum information specific to the system, near optimal values of the control gains can be obtained within 10 generations. Two main motivating factors are behind this kind of study; namely, the response of pneumatic drives is very slow, which leads to its inability to attain set points due to high hysteresis. Moreover, the dynamic model of the system is highly nonlinear, which greatly complicates controller design and development. To address these problem areas, two streams of research efforts have evolved—using conventional methods and adopting a strategy that does not require a mathematical model of the system