Research And Design Of Robot Control System For Pepper Pick In G Man Ipu Lator

In recent years,with the development of Industry 4.0 and the rapid development of computers and automatic control technology,robots have gradually applied to the field of agricultural.Picking robots are important parts of agricultural robots,and their role is to reduce the labor intensity and production costs of workers,improve labor productivity and product quality,and ensure timely harvesting of fruits.Therefore,they have great development potential,but in practical applications,The control system of robotic arm has the characteristics of high coupling and non-linearity,and there are many uncertain factors such as external t ime-varying disturbances,load time-varying,drive saturation,and internal friction,which leads to the problem of how to achieve high-precision trajectory tracking performance which is a hot pot in current robotics research.Therefore,this paper designs an intelligent algorithm with high accuracy,speed and stability to meet the control performance of the robotic arm.The specific research content is as follows:First,the mathematical model of the Pepper picking robotic arm model was modeled by the D-H parameter method,and the positive kinematics equation was deduced,and its inverse solution was obtained by algebraic method.Based on Robotics Toolbox of MATLAB,the numerical calculation of forward and inverse kinematics of pepper picking manipulator and the simulation of the working space were realized,and the conclusions of forward and inverse kinematics analysis were verified.Then the dynamic modeling and trajectory analysis of the pepper picking manipulator were performed.Based on the Lagrangian dynamics algorithm,the establishment of the dynamics equation of the pepper picking robot arm is completed,and its general form is obtained.Finally,the trajectory planning of the joint space and Cartesian space of the manipulator was discussed,and the simulation was verified by Robotics Toolbox of MATLAB.Aiming at the problem of self-adaptation and uncertainty of mathematical model for picking manipulator of Prickly Pear,an adaptive robust compensation algorithm based on RBF neural network was proposed.The RBF neural network controller is designed to approximate compensation for uncertain terms such as friction and external disturbances in the control system;in order to reduce the time-varying disturbance and the approximation errors of the RBF neura l network controller,a robust term is introduced in the controller control law;Stability analysis of the control system is performed by constructing Lyapunov functions;simulation experiments verify the RBF neural network adaptive robust compensation a lgorithm for the compensation of the uncertainty term and the anti-interference performance.Aiming at the shortcomings of the RBF neural network controller such as its time lag and weak anti-interference when a disturbance jump occurs,this paper introduces fuzzy control based on the adaptive control of the RBF neural network,and designs a fuzzy RBF The intelligent controller of the pepper picking robotic arm of the neural network uses the characteristics of the RBF neural network in the controller to approximate compensation for uncertain terms such as friction and external disturbances in the control system;in order to reduce the time delay and anti-interference of the control system,The fuzzy logic control is used;in order to further stabilize the system,a robust term is introduced;the stability of the control system is analyzed by constructing a Lyapunov function;the validity of the algorithm is verified by simulation experiments.