Methods for inertial parameters identification of manipulator based on multibody dynamics and neural networks

In this paper, two methods for directly identifying inertial parameters of manipulator are proposed, which solves the problem that inertial parameters are difficult to identify due to coupling. First, a Model Identification Method (MIM) is proposed based on multibody dynamics. MIM realizes the decoupling of inertial parameters by the mathematical description of graph theory, and has extremely high identification accuracy. Since complex modeling and calculation will affect the real-time performance of the algorithm, MIM is suitable for offline identification. Second, a numerical identification method (NIM) is proposed based on neural networks. NIM avoids complex mechanical modeling process and obtains the approximate value of inertial parameters by numerical fitting method. Compared with MIM, the identification results lose some accuracy. However, NIM has excellent real-time performance and is suitable for online identification. Finally, the effectiveness of the method is verified by simulation experiments. MIM and NIM have complementary characteristics and different application scenarios, which can meet the vast majority of identification needs. The obtained results shed light on achieving precise control of manipulators.