Research On Intelligent Sliding Mode Variable Structure Control Method Of Robot

Trajectory tracking control is very important for robot,because the trajectory tracking control of robot is used in many industrial fields,such as welding,spray painting,assembly,placement,packaging,product testing and testing and so on.In this paper,the combination of intelligent control and traditional sliding mode variable structure control is applied to robot joint trajectory tracking control,which has dynamic modeling error,unknown parameters and uncertain external disturbances.The main contents of this paper are as follows:In this paper,a fuzzy sliding mode variable structure trajectory tracking control method with adaptive compensator is proposed for the robot which has the known dynamic model and is considered the friction force of the joint.The adaptive fuzzy system is mainly used to approximate the friction force of the joints and the sliding mode variable structure control law is adopted.In order to reduce the chattering of control law and improve the tracking precision of the joint,an adaptive compensator is introduced to estimate the upper boundary of the error.Finally,the stability of robot trajectory tracking control system is proved by Lyapunov stability principle.A RBF neural network sliding mode variable structure control method based on harmony search algorithm is studied in this paper for the existence of unknown parameters and the joint friction.The harmony search algorithm is used to optimize the structural parameters of the RBF neural network,and then the RBF neural network approximates the uncertainties in the robot dynamics model online.Using the sliding mode control law,the paper proves the stability of robot trajectory tracking control system by using the Lyapunov stability principle,and the trajectory tracking of the robot joint is realized.The optimized method can make the joint tracking error converge at a faster speed.The simulation of the control method is carried out by Matlab.Finally,experimental results show that the proposed method has better control performance.