Research On Active Compliant Control Of Robot Surface Polishing Adapting To System Stiffness

At present,the polishing of complex curved parts is mainly manual work.With the development of advanced manufacturing,polishing robots are more widely used.The application of polishing robots can solve a series of problems such as low efficiency of manual polishing and poor consistency of processing quality.During robot polishing operations,position control is often difficult to meet polishing quality requirements.Traditional force control methods rely on environmental parameters,of which system stiffness has a greater impact on force control.At present,there are relatively few studies on force control methods that adapt to changes in system stiffness.In order to improve the self-adaptability of the force control algorithm and improve the polishing quality of surface parts,this paper conducts theoretical and experimental research on the compliance force control strategy of surface polishing robot adapting to the change of system stiffness.The main research contents include:(1)The polishing process of curved surface parts and the process system elements that affect the polishing quality of robot curved surfaces are analyzed.The polishing mechanism of the spherical tool is studied,and the polishing scheme of the curved workpiece is optimized and determined according to the material removal characteristics of the spherical tool.The robot polishing hardware system is built and the software function structure of the robot polishing system is determined.(2)The force control dynamic performance and static performance of the position-based impedance control and its influencing factors are studied.Considering that the impedance control does not fully utilize the force feedback information,an improved PD-impedance control strategy is proposed.Aiming at the problem of unknown system stiffness parameters,an impedance control based on recursive least squares stiffness estimation and neural network approximation is proposed.The simulation results show that the two methods have good control effect.(3)The stiffness model of the robot polishing system is established,and the fuzzy adaptive impedance control strategy is proposed based on the impedance control of the outer force loop to indirectly adapt to the changes of the system stiffness parameters.Especially in the case of sudden changes in polishing force and system stiffness,the force overshoot and steady-state force fluctuation errors of fuzzy adaptive impedance control are significantly smaller than those of traditional impedance control.In the inner loop of position control,an adaptive control strategy of RBF neural network compensation is proposed for the uncertainty of robot dynamics model and external interference,and the simulation and comparison with the impedance control based on PD decoupling position control are carried out.The results show that the RBF neural network The network has better performance in both position control and force control.(4)Developed WHUT-CLGenerator V3.0 multi-axis tool position offline generator software and robot polishing control system software,which realized polishing trajectory generation,communication,polishing point import,coordinate system calibration and transformation,constant force polishing control,etc.Function.A typical mold polishing experiment was performed based on the above two softwares.The experimental results show that the improved fuzzy adaptive impedance control strategy proposed in this paper has better force control effect,adaptability to the system stiffness,and better surface quality after polishing.