Research on force control compensation optimization algorithm for robotic grinding system
A force control compensation method for robot polishing system based on algorithm is proposed in order to solve the problems that currently polishing robots cannot achieve both accuracy and compliance in complex environment.First of all,the mechanical characteristics of the robot polishing system and the principle of force control optimization algorithm are explained.Then the experimental system is established to perform the allowable response range and active soft and constant force polishing experiment.Finally,the Extended Kalman filter algorithm,least squares fitting algorithm and particle filter algorithm are used to optimize the real-time compensation value of the polishing force and the compensation effects of each algorithm are compared.The experimental results show that 100% compensation for system structure errors can be achieved within 20 mm through the force control compensation function.Compared with the setting expectation,the average relative error is 5.44% .After optimization using Extended Kalman filter algorithm,least squares fitting algorithm and particle filter algorithm,the average error is reduced to 1.20%,1.24% and 1.64% respectively.Expanding and optimizing the real-time/bit compensation function of the robot collaborative control system will help improve the accuracy and stability of the robot's polishing system,which provide theoretical basis and technical support for the development of robot technology.
robot polishingextended kalman filteringforce control compensation
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