Error Analysis And Compensation Of A Novel 3-RRCPR Parallel Pointing Mechanism

With the development of country’s aviation industry and the needs of military reconnaissance,parallel mechanisms that have the advantages of rapid response,strong carrying capacity,and high-speed movement have attracted much attention.However,the accuracy of the parallel mechanism has always limited its development and application in the pointing field.For this reason,his paper takes 3-RRCPR pointing parallel mechanism as the research object,through error analysis,error model and compensation model is established,with the improved particle swarm optimization(PSO)algorithm for structural parameters optimization,and experimental calibration,to obtain the mathematical model of high precision,Realize the high motion precision of the mechanism.Firstly,the kinematics of the parallel pointing mechanism is analyzed,and the positive and negative kinematics solutions of the parallel pointing mechanism are established.By analyzing the possible error sources of the 3-RRCPR parallel pointing mechanism,the single chain error model of the 3-RRCPR parallel pointing mechanism was established based on the vector method.The mapping relation matrix of the influence of each error factor on the end error of the mechanism was derived by simultaneous equations,which provided a theoretical basis for error analysis and calculation.Secondly,in order to obtain the influence of error on the accuracy of the end of the pointing mechanism,the error and sensitivity caused by each parameter in a given working range were calculated by using the established error model.Using Adams simulation software,the kinematics simulation of 3-RRCPR parallel pointing mechanism with clearance,and several cases including different clearance motion pairs and different clearance sizes are simulated and analyzed,and the influence of each case on the end precision is obtained,which provides a theoretical basis for high precision control.Then,in order to obtain high quality and accurate kinematic parameters,an improved particle swarm optimization(PSO)algorithm is proposed.Aiming at the problem that traditional particle swarm optimization algorithm is easy to fall into local optimum and convergence is too fast,a new particle swarm optimization algorithm based on dynamic adjustment weight is obtained by improving the value method of inertia weight in particle swarm optimization algorithm and flexibly adjusting the transformation between local search and global search for each particle.By selecting 8 groups of test functions,the PSO algorithm with different weights is compared,and the test results show that the improved PSO algorithm proposed in this paper is superior to the existing algorithms in terms of operation speed and convergence speed.Based on the Jacobian condition number,the combination form between the minimum measurement configuration number and it is obtained.The improved particle swarm optimization(PSO)algorithm was used to carry out parameter identification simulation,and the values of each error parameter were randomly given,the fitness function for identification was established,and the values of each error parameter were obtained by calculation.Finally,the 3-RRCPR parallel pointing mechanism was calibrated and simulated,and the ideal motion trajectory was substituted into the modified inverse solution program to obtain the compensation driving Angle.The validity of the compensation method is verified by using the modified forward solution program.The compensation results are based on the kinematics error parameter compensation.