Composite Control Of The Robotic Joint Servo System

The robot is a kind of machine that device driven by the motor,and its performance is affected by the performance of the drive motor.However,many traditional studies only consider the dynamics control of the robot,without considering the factors of the motor.In this paper,the model of robot control system research and motor motion control research are combined.Permanent magnet synchronous motor(PMSM)is applied in robot joint servo system because of its advantages such as simple structure,high reliability and convenient maintenance.Robot system has strong nonlinearity and uncertainty,which brings a lot of problems in the research of robot control.In order to solve this problem in this paper,the two-DOF joint robot is taken as the research object and the PMSM is used as the driving motor.In this paper,a PD gravity compensation control method is introduced.This method introduces a gravity compensation term on the basis of PD controller,which can reduce the time to reach the steady state.The sliding mode control method is adopted in the motor controller,which has good control effect on the motor.The simulations results show that the robot control system that based on PD gravity compensation has good response characteristics and tracking ability.In order to improve the control effect of the robot system that is controlled PD gravity compensation,a method of robot control based on fuzzy neural network is proposed.Fuzzy neural network(FNN)is a kind of control method with fuzzy control and neural network control.It can replace the PD gravity compensation control method,and solve the nonlinear and uncertain problems in robot system,because of its good control effect and learning ability.The robot dynamics can be separated,then the uncertainty is separated from robot system.It can be controlled by the fuzzy neural network.At the same time,in order to reduce the jitter caused by some sliding mode control,some improvements are made to the sliding mode control method proposed in the last section.Simulation results show that compared with PD and gravity compensation control,the tracking and response capability of the system is improved significantly.The robot is susceptible to various disturbances in the movement.A servo system that has good performance not only demands the system can quickly and accurately track the input value,but also demands the system can have good anti disturbance ability.To solve this problem,the robot joint control method of robot with load torque observer is studied in this paper.In generally,the external disturbance can be regarded as the change of the load torque of the control system.Therefore,this paper will increase the load torque observation on the basis of fuzzy neural network controller,and improve the anti-interference ability of the system.The simulation results show that the robot joint control system with load torque observer is less affected by torque disturbance and has better anti-interference ability.In conclusion,in order to solve the nonlinear and uncertain problems of robot system,and improve the fast response,tracking ability and anti-interference ability,several different controllers are studied in this paper.By comparing and analyzing the control effects of different controllers,the system is improved accordingly.Finally,a kind of robot servo control system based on fuzzy neural network and sliding mode control with load torque observer is proposed.This method not only has good fast response and tracking ability,but also has good anti-interference ability.