An enhanced GA to improve the search process reliability in tuning of control systems

Abstract

Evolutionary Algorithms (EAs) have been largely applied to optimisation and synthesis of controllers. In spite of several successful applications and competitive solutions, the stochastic nature of EAs and the uncertainty of the results have considerably hindered their use in industrial applications. In this paper we propose a Genetic Algorithm (GA) for tuning controllers for classical first and second order plants with actuator nonlinearities. To increase the robustness of the algorithm we introduce two features: 1) genetic operators that perform directional mutations, 2) selection tournaments organized by genome vicinity. The experiment results show that the proposed GA is able to guarantee high performance and low variance in the results from different runs. The increased reliability, compared to the results from a classical GA, seems to favour particularly the application of Evolutionary Computation (EC) in tuning of control systems, where, thanks to this approach, a large search space can be searched repeatedly with high consistency in the solutions.