Synchronous Control Of H-Type Motion Platform Based On Supervisory Fuzzy Sliding Mode Control

The level of precision machining technology is one of the most important criteria for measuring the level of a country’s manufacturing industry.Direct-drive H-motion stages are widely used in the field of precision machining due to their high rigidity,fast acceleration and high thrust.A direct-drive H-motion stage consists of one permanent magnet linear synchronous motor（PMLSM）in the X-axis and two in the Y-axis.The tracking error of the PMLSM in the Y-axis direction for a given signal and the synchronisation error between the two PMLSMs are two important indicators of the machining accuracy of a direct-drive H-motion stage.However,in practice,random factors such as variations in PMLSM parameters,non-linear friction and external uncertainty disturbances can affect the tracking error and synchronisation error,which in turn affects the stage machining accuracy.Therefore,the aim of this thesis is to improve the machining accuracy of direct-drive H-motion stages by improving the tracking accuracy of the single-axis PMLSM and the synchronous motion accuracy of the two linear motors using cutting-edge control strategies.The main work of this thesis is as follows.Firstly,this thesis briefly describes the current research on direct-drive H-motion platforms at home and abroad,and introduces various control strategies commonly used for H-motion platforms.The structure and working mechanism of PMLSM are analysed,and a mathematical model including uncertainties such as parameter variations,non-linear friction and external disturbance forces is established.Based on this model,the structure and forces of the direct-drive H-type linear motion stage are analysed and its mathematical model is derived.Secondly,a supervised fuzzy sliding-mode controller is designed to improve the tracking performance and system robustness of the direct-drive H-motion platform single-axis PMLSM under non-linear disturbances.The fuzzy adaptive sliding mode controller is designed to address the jitter phenomenon of sliding mode control.Since both the learning process of the fuzzy system and the self-adaptation to the system parameters prolong the response time of the system,a traditional sliding-mode controller is introduced and a supervised fuzzy sliding-mode controller based on both controllers is designed.The stability of the designed control system is demonstrated using the stability criterion and is verified in simulations through MATLAB.The simulation results show that the supervised fuzzy sliding mode controller is superior in improving the tracking accuracy and weakening the jitter.Finally,a composite synchronous controller based on a neural network sliding mode is designed for the synchronous motion error of the PMLSM on two parallel guides of the H-shaped motion platform.The composite synchronous control based on the compensation principle is designed by combining the series synchronous control and parallel synchronous control methods.Based on this,the RBF neural network sliding mode controller is designed,using the neural network function to replace the switching term of the sliding mode control,thus eliminating jitter and ensuring the robustness of the system.The simulation results show that the designed complex synchronous controller can effectively reduce the synchronous motion error of the system and improve the overall positioning accuracy and stability of the direct-drive H-shaped motion platform compared with the simulation results of the traditional master-slave synchronous control system.