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Research On Trajectory Tracking Control Method Of Six-Axis Manipulator Based On Dynamic Model

Posted on:2024-05-22Degree:MasterType:Thesis
Country:ChinaCandidate:T T SongFull Text:PDF
GTID:2568307076976809Subject:Control Science and Engineering
Abstract/Summary:PDF Full Text Request
With the intelligent development of industrial manufacturing,six-axis manipulator have been widely used in medical,logistics,agriculture and manufacturing industries due to their high flexibility.The study of trajectory tracking of manipulator can guarantee the efficiency and stability of practical applications,and thus the control technology related to it has been one of the directions of academic research.In this thesis,a six-axis manipulator is taken as the research object,and its dynamic model is established for analysis.Considering that the six-axis manipulator is a control system with strong coupling and non-linear characteristics,its trajectory tracking effect is difficult to guarantee effectively.In this thesis,the trajectory tracking strategy of the six-axis manipulator is investigated under the influence of modeling errors,external disturbances and uncertainty terms,which enables the manipulator to accurately track the given desired trajectory and is verified on the manipulator simulation platform.The major research tasks of this thesis are as follows:(1)The dynamic model of the six-axis manipulator is established.Firstly,the motion degree pose and coordinate transformation of the manipulator in Cartesian coordinate system were analyzed,and then the kinetic equation of the six-axis manipulator was established using Lagrange’s equation by selecting generalized coordinates to differentiate it according to the difference between kinetic energy and potential energy.To verify the effect of the algorithm,the3D manipulator model is exported as a URDF(Unified Robot Description Format)file to Matlab/Simulink,and the tracking of the manipulator after a given target trajectory is simulated in the simulation environment.(2)To address the problem of large trajectory tracking error and unstable motion during the movement of the manipulator,an improved Gray Wolf algorithm is proposed to optimize the parameter selection process of the PID(Proportional Integral Differential)controller.An adaptive convergence factor a method based on the quadratic cosine law and a proportional weight method to enhanceαwolf fitness value f_αare designed for the gray wolf algorithm,which enhances the global search ability in the first iteration of the algorithm and the convergence speed in the later stage.Through experimental simulation and comparison with self-tuning PID and traditional Gray Wolf PID control algorithm,it is concluded that the improved gray wolf PID control algorithm can significantly decrease the tracking error of the manipulator,achieve the expected trajectory faster,and make the control effect more ideal.(3)In order to enhance the trajectory tracking accuracy and interference resistance of the manipulator,a non-singular fast terminal sliding mode control method based on a six-axis manipulator is adopted.By analyzing the characteristics of the terminal sliding mode and the non-singular terminal sliding mode control method,the nonlinear term is introduced on the basis of the original sliding mode surface,and the saturation function is used instead of the switching function.The Lyapunov method is used to demonstrate the stability of the system and the convergence of the system state in a finite time.And the external disturbance is added in the experiment process.From the simulation results,the non-singular fast terminal sliding mode control method can effectively improve the convergence speed of the system,and has good anti-interference ability and trajectory tracking accuracy.(4)Considering the possible influence of modeling errors and uncertainty terms in the process of modeling the manipulator.This thesis proposes a sliding mode control based on RBFNN(Radial Basis Function Neural Network)to adaptively approximate the unknown part of the model on the basis of non-singular fast terminal sliding mode control.In the switching control section,an improved variable structure approach law is introduced to optimize the speed of the approaching motion process.The adaptive law of the neural network is derived by using Lyapunov method,and the weight is adjusted adaptively to ensure the global stability and convergence of the whole system.Compared with the effect of non-singular fast terminal sliding mode control method on trajectory tracking under the influence of disturbance and uncertain items,it shows that this method can effectively compensate for modeling uncertainty and improve trajectory tracking performance.
Keywords/Search Tags:six-axis manipulator, dynamic model, trajectory tracking, grey wolf algorithm, non-singular fast terminal sliding mode control, RBF neural network
PDF Full Text Request
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