Aiming at the challenges to accurately simulate complex friction models, link dynamics, and part uncertainty for high-precision robot-based manufacturing considering mechanical deformation and resonance, this study proposes a high-precision dynamic identification method with a double encoder. Considering the influence of the dynamic model of the manipulator on its control accuracy, a three-iterative global parameter identification method based on the least square method and GMM (Gaussian Mixture Model) under the optimized excitation trajectory is proposed. Firstly, a bidirectional friction model is constructed to avoid using residual torque to reduce the identification accuracy. Secondly, the condition number of the block regression matrix is used as the optimization objective. Finally, the joint torque is theoretically identified with the weighted least squares method. A nonlinear model distinguishing between high and low speeds was established to fit the nonlinear friction of the robot. By converting the position and velocity of the motor-side encoder to the linkage side using the deceleration ratio, the deformation quantity could be calculated based on the discrepancy between theoretical and actual values. The GMM algorithm is used to compensate the uncertainty torque that was caused by model inaccuracy. The effectiveness of the proposed method is verified by a simulation and experiment on a 6-DoF industrial robot. Results prove that the proposed method can enhance the online torque estimation performance by up to 20%.

Both the position and force control of robots are needed in industrial manufacturing, such as in assembly and grinding, etc. In this paper, we concentrate on two issues. One is the system oscillation in traditional hybrid force–position control (HFPC) during switching between force and position control because the diagonal elements in the selection matrix are either 0 or 1. Another issue is the poor force-tracking performance of conventional impedance control, which depends on accurate environmental models. To address these issues, a coupled force–position control (CFPC) method is presented in this paper by combining the proposed adaptive impedance control method with a modified HFPC method. The selection matrix $\mathit{S}$ of HFPC is replaced with a weighted matrix ${\mathit{S}}_{\mathrm{w}}$. A weighted matrix regulator is designed to realize smooth switching between position and force control by adjusting the matrix weights in real time, and an adaptive impedance control algorithm is proposed to improve the force-tracking performance in complex environments. To verify the feasibility of the CFPC method proposed in this paper, simulations and physical experiments were conducted. The results show that the CFPC method has the advantages of a better force-tracking performance and a smoother switching between position and force control compared to the traditional HFPC method. A grinding experiment was conducted to further compare the performances of the HFPC and CFPC methods. The roughness values of the ground plates were 0.059 μm for the HFPC method and 0.031 μm for the proposed CFPC method, which demonstrates that the proposed CFPC method has a better performance.