Multiobjective control systems design by genetic algorithms

For multiobjective control systems design, we use genetic algorithms to find the Pareto optimal set of various control system performance indices. We also propose a modified multiobjective selection scheme and the use of the improved rank-based fitness assignment. By combining multiobjective genetic algorithm (MGA) with the pole-zero placement algorithm which can avoid specified pole-zero cancellations, we construct a MATLAB based software package for the computer-aided control system design (CACSD) system for two degree-of-freedom discrete-time control systems. This CACSD system provides a large freedom in the choice of controller structure and in the design specifications. Effectiveness of the proposed CACSD system is illustrated by a design example where the multiobjective optimization by GA is compared with a goal attainment method in MATLAB OPTIMIZATION TOOLBOX.