Study On The Optimal Control Methods For Robotic Arm Joint Motors

With the development and progress of science and technology and intelligence,the current robot technology and its related applications are developing rapidly at home and abroad.China’s "Made in China 2025" strategic plan makes it clear that it will vigorously promote the development of robot technology and its industrialization.As the carrier of science and technology,the robot has become an important part of intelligent manufacturing for its unique function,which solves artificial restrictions and constraints.The robotic arm is the most typical robot in industrial application and theoretical research,the joint motor of the robotic arm must operate with high precision to complete the execution tasks such as trajectory tracking and path planning.The joint motor is the actuator of the robotic arm and provides power output for the robotic arm.Its excellent performance completely determines the stability,dynamic performance,and execution accuracy of the manipulator.Therefore,scholars and experts pay much attention to the control problem of robotic arm joint motors.The joint motor of the robotic arm is mostly driven by the brushless direct current motor,which meets the technical requirements of the motion speed,dynamic response,and position accuracy of each joint.Therefore,the drive control of the brushless direct current motor is one of the main factors affecting the performance of the robotic arm.For every single joint motor of the robotic arm,its motion state needs to be controlled with high accuracy to meet the basic requirements of controlling the end effector.For multi-joint motors of the robotic arm,while ensuring the precise control of a single joint motor,it is also necessary to meet the coordination requirements of each joint motor.Therefore,the high-precision control of single-joint motors of the robotic arm and the synchronous control of multi-joint motors have become the focus of scholars’ research.At present,the control algorithm based on intelligent optimization has been successfully applied in the control of the single-joint motor of the robotic arm,but the problem of long iteration time,slow response speed,and low control precision is common.Most of the multi-motor synchronization control strategies for the joint drive of the robotic arm also have some problems,such as complex structure,the large amount of feedback calculation,poor synchronization performance,and so on.Therefore,this paper mainly conducts intelligent optimization research on precision speed-tracking control of the single joint motor,speed-tracking synchronous control of multi-joint motors,and position-tracking synchronous control of multi-joint motors of the robotic arm,aiming to achieve high-precision,high-performance and intelligent control of the robotic arm.The specific research contents are as follows:(1)The system structure of the brushless direct current motor for the joint drive of the robotic arm is analyzed,mainly including the motor body structure,motor commutation circuit,position sensor,and motor working principle.The mathematical models of the motor in the three-phase natural coordinate system and the two-phase rotating coordinate system are established.Based on Lagrange’s theorem,the dynamics analysis of the multi-joint motors drive system of the robotic arm is carried out.Combined with the mathematical model of the motor,the state space model of the joint motor based on position and angular velocity is obtained,which lays a foundation for the subsequent research on the control method of the joint motor of the robotic arm.(2)Aiming at the problem of high precision speed control of the single joint motor of the robotic arm,a back propagation neural networks(BPNN)proportional integration differentiation(PID)control method based on Q-learning algorithm optimization is proposed.The method uses the self-learning performance of BPNN to obtain the PID control parameters under the optimal control law and introduces the Q-learning algorithm to optimize the BPNN network weight based on the system tracking error so that it can obtain smooth and fast approximation ability,and improve the self-learning capability and convergence speed of the algorithm.At the same time,based on the tracking algorithm of the probability distribution,a dynamic selection strategy of the Q-learning algorithm is constructed to improve the global search performance of the algorithm.Finally,the optimal control signal is input into the joint motor system of the robotic arm to realize the speed control.Through simulation tests,the speed control performances of this method are verified.(3)Aiming at the problem of multi motors speed synchronization control for robotic arm joint driven,a method of multi-joint motor speed tracking synchronization control based on harmony search algorithm(HSA)optimization is proposed.Based on the traditional ring coupling control structure,the feedback compensation mechanism and the synchronous control compensator between motors are designed to reduce the synchronous error between motors.At the same time,the method uses HSA to optimize the fuzzy PID tracking controller.HSA defines the cost function based on the problem of minimizing the system tracking error,finds the optimal harmony target,improves the adaptive robustness of the tracking controller,and realizes the adaptive ability and speed tracking performance of the multi-motor control system.The simulation results show that the control method effectively reduces the speed synchronization error between multi-joint motors,and ensures the high-precision speed tracking performance of the joint motor.(4)Aiming at the position synchronization control problem of multi-joint motors driven by robotic arm joints,a position tracking synchronization control method of multi-joint motors based on adaptive neuro-fuzzy inference system(ANFIS)optimization is proposed.In this method,the ADRC controller and the new multi-motor ring coupling synchronization control strategy are designed to ensure the fast synchronization of multi-joint motors and reduce the synchronization error.At the same time,considering the influence of the external environment disturbance on the system motion state,a new type of ANFIS-optimized sliding mode controller(SMC)and the disturbance observer are proposed to ensure the accurate tracking performance of the joint motor load position.The simulation results show that the control method effectively reduces the position synchronization error between multi-joint motors,and ensures the performance of high-precision position tracking for the joint motor.(5)To test the practical application effect of the proposed control method,the experimental platform for the multi-joint motors drive system of the robotic arm is built.The platform can quickly build the control algorithm model of the joint motor of the robotic arm based on MATLAB/Simulink,automatically compile the executable code,and directly drive the motion of each joint motor of the robotic arm through the real-time simulator.For the three control methods proposed in this paper,the control performances under the changing external environment are tested respectively.The tests result to verify the effectiveness and feasibility of the optimal control methods proposed in this paper for the joint motor of the robotic arm under practical application conditions.Its show that the control methods proposed have good practical application prospect as well as theoretical guidance for the joint drive of the robotic arm.