Dynamic Identification of Industrial Robot Based on Nonlinear Friction Model and LS-SOS Algorithm

This paper is concerned with the identification of industrial robot dynamics parameters, and an identification method based on a nonlinear friction model, least squares (LS) with symbiotic organisms search (SOS) algorithm is proposed. Firstly, according to the friction characteristics of robot joints, for the nonlinear Tustin friction model, two parameters are introduced to describe the Stribeck characteristics of Coulomb friction and static friction, respectively. Then, the joint velocity trajectory that can offset the non-friction torque during the forward and reverse rotation are designed, and friction parameters are estimated by using LS. Based on the optimal criterion constructed by Hadamard's inequality, the excitation trajectory represented by the Fourier series is designed, and the combined inertial parameter set is estimated by using LS. Secondly, select the minimum inertial parameters and friction parameters (minimum dynamic parameters) as the center points to search the upper and lower bounds; design the fitness function with adjustable weights of each joint, and use the SOS algorithm to estimate the minimum dynamic parameter set as a whole. Finally, a variety of parameter identification algorithms are applied to the Stäubli TX-90 robot under the same excitation trajectory. The experiment results show that the root-mean-square (RMS) error of the joint torque estimated by the proposed method is the smallest (compared to the literature [1], the RMS error is reduced by 18.267%); by executing the verification trajectory, the experiment results show that the RMS error of the joint torque is small and the correlation coefficient is close to 1, thereby verifying the effectiveness of the estimated model for different motion trajectories.