Trajectory Planning And Trajectory Tracking Control For Industrial Robots

As a highly coupled and complex time-varying nonlinear system,the trajectory planning and trajectory tracking control of the industrial robot is a very complicated process.The actual existence of various complex uncertainties will also have a great influence on the control process of the robot.Therefore,the trajectory planning and trajectory tracking of the industrial robot is carried out.The research of control method is a traditional and very important topic.This paper takes industrial robot as the research object,and studies its inverse kinematics,trajectory planning and trajectory tracking control in three aspects.The main contents are as follows:The inverse kinematics of the robot is the basis for the study of the motion of the robot.First,an analytic solution algorithm for the inverse kinematics of the six joint general type robot is given and its limitation is illustrated.The Newton iteration method is applied to the robot inverse transport.The numerical solution of the inverse kinematics is obtained by the kinetic solution.A simulated annealing adaptive genetic algorithm is proposed by combining the two optimization algorithms,simulated annealing and genetic algorithm,and is applied to the inverse kinematics of the Guangzhou numerical control GSK RB03A1 type handling robot provided by the laboratory,and the robot in some singular positions is solved.The problem of inverse kinematics solution can not be obtained.The joint angle obtained from inverse kinematics is used as the boundary condition of robot trajectories,and the trajectory planning of robot is studied.The velocity parameter representation of the space line is obtained at the Cartesian space level,and the sinusoidal curve is applied to the velocity planning.At the same time,the attitude matrix of the end of the robot is expressed with four elements,and the original complex matrix trajectory planning is simplified as a trajectory planning for a corner parameter,and the space arc attitude planning is optimized.Three interpolation functions based on acceleration constraints-five polynomial interpolation functions,asymmetrical S type interpolation functions and exponential interpolation functions are used for trajectory planning at the joint space level,and the numbers of five polynomial interpolation functions are obtained by using particle swarm optimization algorithm,and the acceleration constraint of the robot is realized.Smooth motion trajectory planning.After trajectory planning for robots,precise trajectory tracking control is needed to enable them to perform high-quality tasks.The existing methods for trajectory tracking control are analyzed: PD + forward control and iterative learning control.By using the dynamic characteristics of the robot dynamic equation and Li Yapu's function analysis method,the convergence of these control methods is testified respectively.An adaptive iterative learning is proposed.The control algorithm combines the iterative learning control theory with the traditional PD control,uses the theory of adaptive control,uses the error information to adjust the control torque,so as to improve the track tracking precision and improve the control quality of the system.Finally,using the SCARA robot as the experimental platform,we use the traditional PD iterative learning control algorithm and the adaptive iterative learning PD control algorithm in this paper to carry out the trajectory tracking control comparison experiment.The experiment is divided into two types of line trajectory and arc trajectory.Through the analysis of the experimental results,the feasibility and superiority of the algorithm is verified.