Differential Evolution Approach for Identification and Control of Stable and Unstable Systems

Abstract

The purpose of this study is to introduce a robust strategy of identification task application for four typical behaviors, which is created stochastically with a differential evolution method, in order to acquire the optimal model's parameters for each of the four behaviors. This is possible because the problem is represented as an optimization problem, which includes both the objective function and the constraint set of conditions. In addition, a PID controller based on differential evolution has been developed in order to get the most optimal tuning settings for PIDs. A suitable objective function is used to evaluate the overall performance of this process. According to the simulation findings, compared to least-squares identification and the referenced model controller, the differential algorithm gives a higher quality solution in both identification and control. The time-domainstability and convergence features, in particular, are important considerations.