A review of dynamic parameters identification for manipulator control

Abstract

Due to the important role of the manipulator dynamic model in manipulation control, the identification of the dynamic parameters of manipulators has become a research hotspot once again. In this paper, we present an overview of the modeling of manipulator dynamics, the optimization methods of excitation trajectory, the identification methods for dynamic parameters, and the identification of friction model parameters. First, the process and basic methods of identification of manipulation dynamic parameters are summarized, and the optimization methods for excitation trajectory are analyzed in detail. Further, friction model parameter identification and the physical feasibility of dynamic parameters are discussed. These are research hotspots associated with the identification of dynamic parameters of manipulators. The backgrounds and solutions of the problems of physical feasibility and identification of friction parameters are reviewed in this paper. Finally, neural networks and deep learning methods are discussed. The neural networks and deep learning methods have been used to improve the accuracy of identification. However, deep learning methods and neural networks need more in-depth analysis and experiments. At present, the instrumental variable method with complete physical feasibility constraints is an optimal choice for dynamic parameter identification. Moreover, this review aims to present the important theoretical foundations and research hotspots for the identification of manipulation dynamic parameters and help researchers determine future research areas.