Research On The Optimization Of Fuzzy PID Controller For 6R Robot Trajectory Tracking Control

As one of the most widely used automation equipments in modern industrial fields,joint robot is a complex system with strong coupling and high nonlinearity and its high-precision trajectory tracking control technology has been a hot research topic.Robot trajectory tracking control is realized by applying the control torque to each joint,so that the joint angle,angular velocity and other kinematic parameters can track the given value and the robotic end actuator could complete the assignment successfully.In this paper,the fuzzy PID controller is applied to the trajectory tracking control of 6R robot.In order to improve the control effect of fuzzy PID controller used to trajectory tracking control of joint robots,the optimization for fuzzy PID controller is implemented by Genetic Algorithms based on a hybrid coding method.Further,considering the multi-objective optimization problem of trajectory tracking control,the multi-objective particle swarm optimization algorithm for the fuzzy PID controller is completed by the multi-objective particle swarm optimization algorithm.The main work content and results obtained are as follows:Firstly,the kinematics model of the 6R robots is established based on D-H parameters and its dynamics model is established based on the Newton-Euler equation.The kinematics simulation and dynamics simulation of the joint robot are completed using Matlab software.Secondly,a fuzzy PID controller is designed for the simulation of robot trajectory tracking control based on Matlab/Simulink.The fuzzy PID controller and PID controller are applied to the robot trajectory tracking simulation,respectively.The simulation results show that the fuzzy PID controller has faster response,better stability and higher control accuracy.Then,the Genetic Algorithm based on hybrid coding is designed to optimize the membership functions and fuzzy rules of the fuzzy PID controller.Characteristic parameters are used to characterize design parameters such as membership function and fuzzy rules.The hybrid coding method that composed of characteristic parameters and design parameters is given to implement genetic algorithm for optimization of the fuzzy PID controller.Compared with the genetic algorithm without hybrid coding method,the controllers optimized by the two coding methods are respectively applied to the trajectory tracking simulation.The optimization results illustrate that the genetic algorithm based on hybrid coding method has better optimization efficiency and optimization results.In the case of no disturbance and disturbance,the fuzzy PID controller optimized based on the hybrid coding method has better trajectory tracking control effect and anti-interference ability.Finally,considering the controller output and the trajectory tracking error,the multi-objective particle swarm optimization algorithm with faster convergence speed is implemented to the optimization of fuzzy PID controller.The multi-objective optimization is used the multi-objective genetic algorithm and multi-objective particle swarm algorithm,respectively.And their optimization results are compared and analyzed.The experimental results demonstrate the effectiveness and superiority of the multi-objective particle swarm algorithm in trajectory tracking fuzzy PID controller multi-objective optimization.