Robot Dynamic Parameter Identification Based on Nonlinear Friction and Hierarchical Iterative Optimization

Accurate identification of manipulator dynamic parameters is a fundamental prerequisite for high-precision robotic control and optimized operational performance. Conventional mainstream identification schemes primarily adopt ordinary least squares (OLS) and weighted least squares (WLS) algorithms to solve dynamic parameters, yet these methods fail to fully incorporate nonlinear friction models and cannot satisfy physical feasibility constraints (PFCs). To address such limitations, this paper proposes a hierarchical iterative optimization identification framework for robot dynamic parameters integrated with nonlinear friction modeling. First, nonlinear joint friction parameters are pre-identified to lay a solid foundation for physically feasible base inertial parameter estimation. Second, the inertial torque and friction torque of the manipulator are decoupled and modeled separately. The robot dynamic equation is linearized while preserving the inherent nonlinear characteristics of joint friction, upon which a closed-loop iterative identification architecture is constructed to achieve high-precision dynamic parameter solving. The proposed algorithm strictly enforces physical feasibility constraints and supports compatibility with multiple nonlinear friction models, which substantially improves identification accuracy and ensures the identified parameters align closely with the actual physical properties of the robotic system. On this basis, a physics-informed long short-term memory recurrent neural network (PI-LSTM) is introduced to further suppress residual identification errors. Multiple groups of verification experiments are conducted on a JAKA Mini Cobo six degrees of freedom (6-DOF) collaborative manipulator platform. Comparative analyses against state-of-the-art identification algorithms and friction modeling strategies validate the practicality and superior performance of the proposed method.