Adaptive Online Parameter Tuning Using Genetic Algorithms

A common practice is to design a controller by plant observations (i.e. experiments) and to optimize some of its parameters by trial-and-error. This paper proposes a genetic algorithm for the automation of the search procedure and its implementation on a programmable logic controller. The details of this implementation will be discussed along with an example one carried out for the control of a plant simulation problem introduced by Eastman Chemical Co. The advantage of such an approach consists of automating the search for a good solution. Moreover the genetic algorithm can be easily programmed in the PLC and reused for different plants with the only need of string encoding and fitness evaluation reprogramming.