Abstract

Evolution algorithms are optimization methods that mimic a process of the natural evolution. Their stochastic properties result in a huge advantage over other optimization methods, especially regarding solving complex optimization problems. In this paper, several types of evolutionary algorithms are tested regarding a dynamic nonlinear multivariable system modelling and control design. We have defined three problems: the first one is the so-called grey box identification problem where the characteristic of the system’s valve is under investigation, the second one is a black box identification where the goal is a dynamic system’s model development using system’s measurements data, while the third one is a system’s controller design. The efficacy of solving presented problems was compared to the usage of the following optimization methods: genetic algorithms, differential evolution, evolutionary strategies, genetic programming, and a developed approach called AMEBA algorithm. All methods have proven to be very useful for grey box identification and design of a system’s controller, but AMEBA algorithm has also been successfully used in a black box identification, where it generated a corresponding dynamic mathematical model.