Articulated Unmanned Agricultural Machinery Trajectory Tracking Control And Simulation

Path tracking control is an important part of the implementation of driverless technology.The important premise of the application of articulated unmanned agricultural machinery in precision agriculture and smart agriculture in the future is to study the unmanned agricultural machinery trajectory tracking control system,so that the unmanned agricultural machinery can operate accurately in the actual operation,improve work efficiency,and ensure that unmanned agricultural machinery operates stably and safely in high speed and complex road conditions.This thesis firstly derives the kinematics model and dynamics model of articulated unmanned agricultural machinery.Considering the stability problem of the vehicle,the tire model is introduced into the dynamic model to establish the state space model basis for the control algorithm.Secondly,reduce the cost of measuring tools for unmanned agricultural machinery,comprehensively analyze the estimation accuracy and time overhead of various filtering algorithms,combine the filtering algorithm with the tracking model for state estimation,and obtain the estimation result close to the real state.Then,based on the model predictive control theory,the control algorithm is formulated,and the performance objective function transforms the solution of the objective function into a constrained optimization problem.Combined with the vehicle dynamics characteristics,the control quantity limit constraint is added to the vehicle control,the tire side deviation and the centroid side deviation constraint,and the front and rear body angle constraints are used to ensure the vehicle stability in the control process.Finally,based on the linear quadratic regulator control algorithm and model predictive control algorithm,the simulation experiment and result analysis of the target path tracking of articulated unmanned agricultural machinery under different steering conditions are carried out.The experimental results show that the state estimation scheme adopted in this thesis compares the original measurement model data with the real state value.The controller based on the model predictive control algorithm has good adaptability to the changes of different road conditions,different driving speeds,and the curvature of the target path.The control quantity and state quantity of the vehicle are all constrained within a certain range,which ensures the stability of the system,the overall performance of the controller is better.