Research On Synchronous Control Strategy Of Multi-Joint Manipulator

With the continuous advancement of technology,the multi-joint manipulator has developed rapidly and has been widely used in various fields.Not only is the stability of the manipulator required,but also the control accuracy,rapidity,anti-interference,and synchronization performance have corresponding requirements.Therefore,the thesis designs a corresponding control strategy for the position tracking control of a single joint,aiming at the control accuracy and rapidity of the manipulator system.According to the control requirements of the multi-joint manipulator,different synchronization control strategies are designed,which can effectively improve the position tracking control accuracy of a single joint and the synchronization control accuracy between joints.This thesis takes the multi-joint manipulator as the research object,and designs the single joint position tracking control strategy and synchronous control strategy.The main work of the thesis is as follows:(1)This thesis introduces the development history,background,and current status of control strategies for manipulator.The thesis describes the establishment process of the dynamic model of the manipulator,which provides the theoretical basis for the following.(2)The control performance of each joint of the manipulator will have an impact on the overall performance of the manipulator.Therefore,in order to solve the problem of the uncertainty of the manipulator model information and the influence of external interference on the position control accuracy of each joint,the thesis designs the iterative sliding mode control strategy based on the RBF neural network.To a certain extent,it can effectively improve the tracking accuracy and accelerate the tracking speed of the single joint of the manipulator.But it exacerbates the chattering phenomenon of the control torque of the manipulator.Therefore,in view of the chattering phenomenon of the manipulator,this thesis introduces fractional calculus and designs the fractional-order reaching law iterative sliding mode control strategy based on the RBF neural network,which can effectively suppress the chattering problem of the system and reduce the size of the system control torque.Moreover,the position tracking and the tracking speed accuracy of the joint are improved again.(3)The single joint position control strategy of the manipulator cannot effectively solve the problem of coupling error between joints.And the fractional-order reaching law sliding mode synchronization control strategy based on the RBF neural network is proposed.Firstly,the definition of synchronization error is proposed based on the position error of the single joint,and the deviation coupling error is further defined.The RBF neural network fractional-order reaching law sliding mode synchronization deviation coupling control strategy is designed and compared with other synchronization control strategies.The size of the control torque of the system is reduced and the chattering of the control torque is suppressed.It not only improves the position tracking accuracy and tracking speed of the single joint of the manipulator,but also reduces the synchronization error.