Polishing robot is an automatic system in which the robot controls the end effector to fix the polishing tool and finish the workpiece polishing efficiently. In order to solve the problem of how to maintain the stability of actuator contact force in the robot automatic polishing system, a learning algorithm of robot impedance control parameters based on reinforcement learning is proposed and the impedance control model is established in this paper. The influence parameters (inertia M, damping B, stiffness K) of impedance performance are analyzed by numerical simulation method and the optimized impedance parameters are obtained at last. Due to the small number of iterations and high data utilization rate, reinforcement learning algorithm is more suitable for robot constant force tracking. In the process of applying reinforcement learning algorithm, a combination of dynamic matching method and linearization method is proposed to predict the output distribution of the state, which greatly improves the cost function of the evaluation strategy, and impedance parameters corresponding to the optimal strategy are obtained. Finally, steam turbine blade is taken as polishing test part. The average roughness of the selected points of test part after polishing is only 0.302μm, and much less than 1.151μm before polishing, which verifies the feasibility of the proposed impedance control method.

Refined Oil demand has great influence on the planning, operation and control of Refined Oil systems. A combined forecasting model based on the principle of gray theories was put forward to optimize Refined Oil demand forecasting models. The example shows that combined gray forecasting model can overcome the disadvantages of individual model, raise the precision.