Research On Excavation Trajectory Planning And Tracking Simulation Of Working Device Of Hydraulic Excavator

As a kind of engineering machinery equipment used for loading and unloading earth and stone materials,excavator is widely used in mining,rescue and urban construction.With the rapid development of modern construction and the expansion of human activities,the operating environment of excavators has become more severe,the operating tasks have become more complex,the operating time has become more tense,and the demand for operating quality has become more stringent,at the same time because of the development of computer technology,electro-hydraulic proportional technology,robot technology and automatic control technology in recent years,in order to overcome the above challenges and realize autonomous excavation,people have carried out extensive and in-depth research on the intellectualization of excavators.Therefore,the key technologies of intelligent mining-trajectory planning technology and trajectory tracking control technology were studied in this thesis,in order to improve the operating efficiency and trajectory tracking accuracy of hydraulic excavator.The research contents of this thesis are as follows:(1)In order to carry out the following research on trajectory planning and trajectory tracking control,the working device should be modeled mathematically.Therefore,the structure of the working device was introduced,the kinematic model of the working device was established,and the kinematics description of the pose space,joint space and drive space of the working device was obtained by using D-H method and geometric method.In addition,the Lagrange method was used to model the dynamic of the working device,so as to solve the dynamic matrix and the torque of each joint.The dynamic excavation resistance was calculated by using theoretical equation.Finally,the thrust arm and load force of each cylinder were obtained.(2)The planning and optimization analysis of excavation trajectory can effectively improve the operating efficiency.Therefore,the working range of IV hydraulic excavator was solved,and the task decomposition method was proposed for deep pit excavation.In order to reduce the total stroke of the cylinders in the process of excavation,the joint angle at the key points of the excavation was optimized.The trajectory planning of each joint was carried out by using cubic spline interpolation function.In order to improve the operation efficiency,the single operation time was taken as the optimization variable,the kinematic constraints of the operation process were established,and the variable scale chaos optimization algorithm was used to optimize the mining trajectory.(3)In order to improve the trajectory tracking control accuracy under the condition of large uncertainty of control system,the structure of the sub-hydraulic system of boom,stick and bucket was introduced,and the transfer functions of each sub-hydraulic were calculated.Time domain and frequency domain analyses were performed for each subsystem without a controller.With the known upper bound value of the uncertainty,the nominal model control rate was designed by using state feedback method,and the compensation controller was designed based on sliding-mode control method.With the unknown upper bound value of the uncertainty,combined with artificial intelligence technology,the upper bound value of uncertainty was estimated based on the RBF neural network,and a sliding-mode controller with the unknown upper bound value adaptive learning based on the RBF neural network was designed.(4)In order to verify the trajectory tracking performance,the sliding-mode control method with known upper bound value of uncertainty was selected under the condition of no load or light load of hydraulic cylinder.Based on Simulink software,the models of each sub-hydraulic system and the controller were established.The trajectory tracking simulation verification of the optimal mining trajectory was carried out.Under the condition of large load of hydraulic cylinder,the controller,hydraulic system and dynamics model were established based on Simulink and Adams software.Through Simulink-Adams co-simulation,the two control strategies,sliding-mode controllers with known upper bound value of the uncertainty and with unknown upper bound value adaptive learning based on the RBF neural network,were compared for the tracking performance of the optimal excavation trajectory.The simulation results verified that the latter controller had smaller trajectory tracking error and better trajectory tracking control effect,which indicated the effectiveness of the trajectory tracking control strategy designed in this thesis.