A comparison of genetic programming and genetic algorithms in the design of a robust, saturated control system

The design of a robust control system for a specified second order plant is considered using three different approaches. Initially, a control system evolved by a genetic programming algorithm is reproduced and analysed in order to identify its advantages and drawbacks. The automatic design technique is compared to a traditional one through the analysis of the constraints and performance indices obtained by simulation. A set of unspecified control constraints explored by the GP search process is found to be the cause of a better performance. Hence, giving a better constraints specification, a genetic algorithm is used to evolve an alternative controller. A PID structure is used by the GA to produce and tune the controller. Simulations show a significant gain in performance thanks to a more aggressive and complete exploration of the search space within the constraints. The effectiveness of the two methods compared to the traditional approach is discussed with regard to performance, complexity of design and computational viability.