Adaptive Optimal Admittance Control for Robotic Precision Grinding Based on Improved Normalized Advantage Function

Precision force control technology is central to robotic precision grinding, which presents challenges: optimizing parameters, managing significant contact impacts, and handling uncertain environmental conditions. This article focuses on the robotic precision grinding of blades. To mitigate the pulse impact during initial contact, we devised a post online fastest-tracking differentiator. Furthermore, to enhance force control stability during the grinding, we introduced an adaptive optimal admittance control strategy utilizing the linear quadratic regulator (LQR) to determine optimal control parameters. However, solving the Riccati equation in LQR poses a challenge due to the unknown environment. To tackle this issue, we devised a model-based accelerated learning approach based on the Normalized Advantage Function. This method employs a dual network architecture for policy learning and enhances the reward and acceleration mechanism to improve training speed and convergence. The experiment was performed through robotic precision grinding on aero engine blades. Results indicate that our method enhances force control precision by approximately 210% compared to traditional fixed-parameter grinding methods. Postgrinding, blade roughness improved from Ra0.4 to Ra0.3, and profile deviation was reduced from [0.050, −0.070] to [0.049, −0.030].