Research On Feedforward Compound Control And Intelligent Fine-tuning Of Feedforward Parameters For High-speed And High-precision Linear Motor Motion Control System

With the development of high-end manufacturing industry,the traditional drive system composed of rotating motor and direction changing device is unable to meet the requirements of modern motion process due to its slow response and low positioning accuracy.With the advantages of fast response and high precision,linear motor direct drive plays an increasingly important role in high-speed and high-precision application scenarios.In practical engineering applications,the direct drive method of linear motors also has many technical problems,such as end effect,cogging effect,thrust fluctuation,etc.,which seriously affect the control accuracy and stability of linear motors.Research on the composite control scheme and the intelligent fine-tuning of parameters of the high-speed and high-precision linear motor motion control system to improve the performance has important theoretical significance and practical application value to accelerate the development of national high-end manufacturing industry.The main research contents of this thesis are as follows:（1）By consulting relevant domestic and foreign materials,in-depth understanding of the drive control strategy of high-speed and high-precision linear motors is built.After analyzing the structure and principle of the linear motor,the mathematical model of the linear motor servo system is deduced.Under the guidance of feedforward control theory,an innovative composite control strategy of "three-loop PID + third-order feedforward" is proposed.While the PID controller ensures the steady-state performance of the system,the feedforward controller is used to directly compensate the input signal,making up for the shortcomings of feedback control,and realizing the performance requirements of dynamic fast response and static fast tuning of linear motors.（2）Build the motion control system of the motion platform.The high-precision XY motion platform of the laboratory is used in the hardware.According to the experimental requirements,the software uses the DLM function of the GHN motion control card to develop and implement the fourth-order S-curve motion planning algorithm and compound control servo algorithm in the lower computer.The upper computer software is developed to interact with the algorithm parameters of the lower computer,and the feedforward parameter intelligent tuning function module and the curve drawing function module are developed in the upper computer,which is convenient for experimental verification.（3）In order to solve the problems of low accuracy and low efficiency of the traditional manual trial and error method,a feedforward parameter intelligent tuning method based on fuzzy algorithm is proposed in this subject.Through the curve waveform analysis and experimental analysis of the input signal of the feedforward controller,the influence rule of the three feedforward parameters on the system performance is extracted,and the fuzzy control rule table is formulated.The membership uses the triangular membership function,and the fuzzy inference method uses The Mamdani reasoning method is used,and the defuzzification is carried out by the center of gravity method.Finally,the algorithm is integrated into the host computer for experimental verification.Under the specified experimental conditions,the three feedforward parameters can be tuned within 60 iterations,and the relatively optimal feedforward parameter values can be obtained,which can greatly improve the dynamic performance of the system.（4）Aiming at the problem that the fuzzy feedforward tuning method is difficult to obtain the optimal value of the feedforward parameters,an intelligent feedforward parameter tuning method based on the differential evolution algorithm is proposed.Feedforward parameter fine-tuning cannot guarantee that the system obtains the best dynamic and static performance at the same time.According to the contradiction between the dynamic and static performance optimization of feedforward parameters,three different fitness functions are designed to optimize the dynamic and static performance of the system.The differential evolution tuning algorithm of feedforward parameters has been verified by several groups of experiments.First,the comparison experiments of different numbers of feedforward parameters are carried out.The experimental results show that the optimization effect of the system performance is the best after the tuning of three feedforward parameters,which verifies the rationality of using a third-order feedforward controller.Secondly,the comparison experiments of the tuning results of different fitness functions are carried out.The experiments show that the different fitness functions have a targeted optimization effect on the system performance,which verifies the effectiveness of the three fitness function designs.Finally,the robustness of the feedforward tuning algorithm is verified by comparing the tuning results of different motion plans.（5）The two intelligent feedforward tuning algorithms are compared and analyzed.Under the requirement of high-precision positioning,the intelligent tuning method of feedforward parameters based on fuzzy algorithm can quickly complete the tuning and improve the dynamic performance of the system.The intelligent tuning method of feedforward parameters based on differential evolution algorithm can be settled within the error range of 10μm before the end of the planning command,which meets the requirements of high-speed and high-precision operation control system with high positioning accuracy and short settling time.The algorithm has strong versatility,but the disadvantage is that the settling time is long.