Automated synthesis of optimal controller using multi-objective genetic programming for two-mass-spring system

There are much research effort in the literature using genetic programming as an efficient tool for design of controllers for industrial systems. In this paper, multi-objective uniform-diversity genetic programming (MUGP) is used for automated synthesis of both structure and parameter tuning of optimal controllers as a many-objective optimization problem. In the proposed evolutionary design methodology, each candidate controller illustrated by a transfer function, whose optimal structure and parameters, obtained based on performance optimization of each candidate controller. The performance indices of each controller are treated as separate objective functions, and thus solved using the multi-objective method of this work. A two-mass-spring system is considered to show the efficiency of the proposed method using performance optimization of open loop and closed loop control system characteristics. The results show that the proposed method is a computationally efficient framework compared to other methods in the literature for automatically designing both structure and parameter tuning of optimal controllers.