Research On Application Of Parameter Self-tuning Iterative Learning Method Based On Fuzzy Strategy

Nowadays,IC has become the core of the global electronic information industry,and lithography is one of the key equipment.In this paper,the characteristics of repetitive periodic operation of linear motor and the requirements of system performance of lithography are studied,and the precision control problem of motor is studied.A gain self-tuning iterative learning control method based on fuzzy strategy is proposed,which is optimized based on genetic algorithm.And the effectiveness of the algorithm is verified.Firstly,the whole situation of the lithography system and the structure and function of the system of the stages are analyzed.According to the specific scanning exposure process and the functions of each motor in the process,the third-order S-curve is proposed.In addition,the mathematical model of permanent magnet linear motor based on PARK transformation and vector control is studied in this paper.Secondly,according to the periodic characteristics and performance requirements of the motor,Iterative learning control(ILC)is introduced into the control of the motor of stage.After several iterations,the accuracy has been improved,but the convergence speed can be improved.Therefore,this paper proposes a gain self-tuning iterative learning control based on fuzzy strategy,which uses the Mamadani-type fuzzy controller to adjust the gain of iterative learning.This method can greatly improve the convergence speed while not affecting the accuracy.However,there is shock in the simulation of this control method.So there is room for improvement and fuzzy control rules can be further optimized.Thirdly,aiming at above problems,this paper proposes a iterative learning control method on fuzzy strategy based on genetic algorithm optimization.The genetic algorithm is used to optimize the ten parameters in the fuzzy rules.After several generations of evolution,a set of optimal solutions is generated,and the fuzzy rule corresponding to the optimal solution is applied to the Mamadani type fuzzy controller.The output of the fuzzy controller adjusts the gain of the iterative learning.And the method is simulated,of which result shows that the method is very stable and there is no shock in the expert fuzzy iterative learning control.The convergence speed is also close to the fuzzy iterative learning control of the expert experience,and the final control precision is also improved,which shows the effectiveness of the algorithm.In addition,the anti-interference ability of the fuzzy iterative learning control is simulated and analyzed.The optimized fuzzy iterative learning control under the interference can still achieve good control effect.Finally,three sets of experiments,including a general iterative learning control and two sets of iterative learning control based on fuzzy strategy are designed for the X-ray motor.The following conclusions can be drawn by the experiment.Ordinary iterative learning control can guarantee high precision under the condition of very slow convergence speed,but the convergence rate is difficult to be improved.The iterative learning control based on fuzzy strategy can greatly improve the convergence rate,especially the improved fuzzy iterative learning control method based on GA optimization algorithm,which can achieve the perfect combination of convergence speed and control precision.So the algorithm can be applied to control the linear motor which runs a periodical curve,such as the linear motor of the lithography in our laboratory.The algorithm this paper proposed can receive good control effect.