Motion Control And Simulation Of Redundant Freedom Manipulator

With the gradual promotion and improvement of the made in China 2025 plan,robot technology has become a key driving force for social development and progress.In this paper,the redundant DOF manipulator is analyzed and studied.The Gamma 300 manipulator is taken as an object to study its kinematic modeling,inverse kinematic solution,dynamic analysis and sliding mode motion control.The main research results are as follows.(1)Aiming at the non-uniqueness problem of the inverse kinematic solution of redundant DOF manipulator,an optimized BP neural network based on multi-population improved genetic algorithm is adopted to solve the problem.BP neural network can approach the nonlinear system infinitely to get the optimal solution,and select a variety of group improved genetic algorithm to optimize the structural parameters of BP neural network in order to speed up the network training speed and improve the training accuracy.Matlab simulation results show that the proposed method has higher accuracy and better effect in solving the inverse motion of redundant manipulator.Monte carlo method was used to analyze the workspace of the manipulator,and the point cloud diagram of the workspace obtained by MATLAB simulation showed that the manipulator was compact in structure and reasonable in design.(2)Aiming at the inevitable chatty phenomenon when sliding mode control manipulator moves in accordance with planned trajectory,a new exponential approach law of double powers based on saturation function is adopted in this paper.First,the existence and accessibility of the new approach law are proved.Then combined with the dynamic model of the manipulator,a motion controller based on the new approach law is designed to control the manipulator motion.Finally,compared with the motion controller designed based on exponential law and power law,Simulink simulation in MATLAB verified that the new approach law was better than the other two approaches law in terms of convergence speed and chattering suppression.(3)Aiming at the problem of artificial setting of some important parameters in sliding mode control based on the new approach law,an improved differential evolutionary algorithm is adopted to optimize sliding mode parameters and sliding mode thickness parameters.Compared with the process of the standard differential evolutionary algorithm,this algorithm adds a screening operation,which mutates the population according to the results of the screening operation,and designs the evaluation function with the error of trajectory tracking and control input as independent variables.MATLAB simulation results show that the improved differential evolutionary algorithm has a good optimization ability for the optimized parameters,and the optimized parameters can improve the convergence speed of the system and further suppress chattering phenomenon in the movement of the manipulator.