Identification of linear dynamic systems using the artificial bee colony algorithm

This paper presents an investigation into the development of system identification using the artificial bee colony (ABC) algorithm. A system identification task can be formulated as an optimization problem where the objective is to obtain a model and a set of parameters that minimize the prediction error between the measured plant outputs and the model outputs. The most common existing system identification approaches, such as the recursive least squares method and autoregressive exogenous method, are substantially analytical and based on a mathematical derivation of the system's model. Evolutionary computation, which seems to be a very promising approach, is an alternative to these methods because a little knowledge about the problem is sufficient in this approach and it can be easily combined with many other techniques from artificial intelligence, control engineering, machine learning, and so on. In this paper, an evolutionary approach for system identification is considered and attempted to demonstrate how the ABC algorithm can be applied in system identification tasks. Mathematical models of dynamic systems are obtained using difference equations represented in discrete time and the ABC algorithm is used to estimate the unknown parameters of the systems. Simulation results demonstrate that the proposed linear system identification method has good identification performance. Moreover, this method is applied to the identification of a direct current motor in order to show the performance of the ABC algorithm. The obtained results show that the identified and actual plant outputs successfully match each other.