This paper presents a new identification searching method for system parameters of a nonlinear LCD glass-handling robot system by using an Improved Real-coded Genetic Algorithm (IRGA). It is well known that the Real-coded Genetic Algorithm (RGA) method is an optimal or near optimal searching technique, which borrows the concepts from biological evolutionary theory. Furthermore, how to select operators in the RGA is an important step to affect the final identified results. In this paper, three algorithm operators including the Self-Learning Genetic Algorithm (SLGA), Adaptive Real-coded Genetic Algorithm (ARGA) and Terminal Condition (TC) are combined to develop a new algorithm for system identification. The proposed algorithm is compared with other genetic algorithms, and the merits from numerical simulations and experimental results demonstrate that the proposed IRGA identification method is the best one and feasible for the glass-handling robot system driven by a Permanent Magnet Synchronous Motor (PMSM).