Research On Track Tracking Control Of Industrial Robot Based On Six Degrees Of Freedom

With the constant development of modern industry,the social productions have been experiencing ever increasing demand for ultra-high-precision industrial robots.The six degree of freedom robot system has the dynamic features of time-varying,nonlinearity,and strong coupling,and there exist many sophisticated and uncertain factors that seriously affect the control performance of the robot.Therefore,the study on trajectory tracking control of industrial robots has important practical significance to improve the stability,safety and accuracy of the robot.In order to achieve the trajectory tracking of industrial robots,the models on kinematics and dynamics of the robot were firstly constructed.After establishing the connecting rod coordinate system,the forward kinematics of the robot was analyzed and solved,and then the dynamical equations of the robot were derived in the framework of the Lagrange method.Robotics Toolbox in MATLAB was used to build a robot simulation model where the movement of the robot could be directly observed in the corresponding simulation results.Concerning with the trajectory planning of robots,the trajectory planning methods in joint space and Cartesian space were introduced respectively.The methods of cubic polynomial interpolation and quintic polynomial interpolation were then compared by simulation,which demonstrated that the quintic polynomial interpolation could produce a more stable,smoother,and continuous trajectory curve,thereby,ensuring the smooth operation of the robot.Since the traditional control algorithm is limited by the uncertainty of the dynamic model,the intelligent control algorithm has been integrated into our study.To ensure the stability of the system,the radial basis neural network was used to approximate the uncertain part of the dynamic model and the Lyapunov method was used to design the robust controller.The particle swarm optimization algorithm has been used to adjust the parameters of the activation function of the neural network online.The simulation demonstrated that the tracking accuracy of the end effector could meet the requirements.In order to take into account of both the uncertainty of the system model and the existence of disturbance,a sliding mode controller based on nonlinear disturbance observer was designed.The disturbance was estimated and compensated by using a nonlinear disturbance observer.As far as the problem of chattering in sliding mode was concerned,the switching gain for sliding-mode was adjusted in real-time by using a radial basis neural network whilst introducing an attenuation factor to improve the double-power law and its approximation.The simulation results showed that the control algorithm has good position tracking performance and can guarantee the stability and accuracy of the system when the presence of compound interference exists.