A comparison of fitness functions for identifying an LCD Glass-handling robot system

In this paper, system identification with variable fitness functions (FFs) by using the real-coded genetic algorithm (RGA) is proposed for a liquid crystal display (LCD) glass-handling robot system. The FFs including the state errors and energy balance equation are compared for the system. Firstly, the mechatronic modeling including mechanical and electrical dynamic equations are formulated. Secondly, the FFs including state errors and energy balance equations by using the RGA method are employed to maximize the FFs values, and to identify the unknown system parameters. From numerical simulations, it is found that the identified performance with energy balance equation FF by using the RGA method has the best identified performance among the other FFs. Finally, the RGA method with FFs are experimentally performed to identify the parameters for a real robot system. From the comparisons of the experimental results, it is also found that the energy balance equation FF has the best identified performance among the other FFs. Therefore, the unknown parameters of the real robot system can be correctly identified by the RGA method with the energy balance equation FF. The contribution of this paper is to propose an energetics FF to be implemented in system identification for the mechatronic system. It can be concluded that the more system states are used in the FF, the more correct system parameters are identified for the robot system.