Tuning discrete PI controllers by fuzzy fitness based genetic algorithms

Different methods and schemes have been proposed in literature for tuning continuous and discrete PI (ProportionalIntegral) controllers. This paper proposes a scheme in which, this controller structure is explored in a different way, by looking its behavior as a lag compensator and tuning it by genetic algorithms. A difference with conventional approaches is the manner to evaluate every individual generated by the evolutionary algorithm. That evaluation is achieved by a set of measurements which becomes the input of a fuzzy inference system that models the expert's knowledge. This scheme is simulated and tested over two nonlinear dynamical systems. Results show that a widely variety of discrete PI controllers can be obtained for one dynamical system, based on the same tuning criterion and having high performance levels in comparison with conventional methods.