Adaptive Control And Stability Analysis Of Piezoelectric Actuator

With the rapid development of micro-nano technology,ultra-precision positioning technology has become an important part of cutting-edge technology in various related fields,and the improvement of its positioning accuracy is enough to affect the development of related fields.Piezoelectric actuator is one of the most ideal actuator components in the field of ultra-precision positioning.It overcomes some of the shortcomings of traditional actuators.It has the advantages of small volume,lighting weight,great force,high accuracy,running fast and less energy consumption,etc.It has become one of the most widely used drive components.It has been widely used in high-tech fields such as semiconductor manufacturing,biomedicine,aerospace and aviation.However,the nonlinear features of creep and hysteresis which always exist in piezoelectric materials.It leads to the positioning accuracy of piezoelectric ceramic actuator is reduced and slowing down the instantaneous response speed which has become a major problem in the application of piezoelectric actuator.Therefore,by designing a suitable controller to overcome the adverse effects caused by hysteresis and nonlinearity,and to better exert the positioning performance of the piezoelectric ceramic driver,it has important theoretical significance and practical value.In this paper,we take piezoelectric actuator as the research object,based on the existing mathematical model and related theoretical knowledge,a new control scheme of piezoelectric actuator is proposed to reduce the impact of hysteresis characteristics,so as to achieve high-precision positioning of the piezoelectric ceramic actuator the goal.The main research work of this paper is as follows:(1)Based on the existing Lu Gre friction model,we proposed a composite control approach which is based on RBF neural network.First,we design the feedforward controller,PID controller and RBF neural network controller,and inferred an adaptive law.Secondly,it is proved that the compound control system based on radial basis function neural network is progressively stable.Finally,a simulation experiment is carried out on the compound control based on the radial basis function neural network.The simulation results show that the compound control scheme is effective.(2)We have proposed an adaptive control method with adaptive gain based on the existing Bouc-Wen model.After getting the adjustable gain,the nonlinear hysteresis effect is linearized,and then the model reference adaptive control is used for the precision positioning platform.Secondly,it is proved that the control system is progressively stable,combined with robustness analysis,to ensure that the control system is stable for a limited time.In addition,an iterative adjustment rule is also given to select the parameters of the gain.Finally,the effectiveness of the model reference adaptive control system is verified by simulation,and the iterative parameter tuning method can enhance the stability and convergence speed of the system.Numerical experiments show that,compared with PID control schemes,the model reference adaptive control scheme has faster convergence speed and smaller tracking error range.(3)We have proposed an adaptive control scheme based on RBF neural network which is based on the nonlinear system of Bouc-Wen model.After creating a virtual controller,we can use the RBF to fit the unknown nonlinear function and design the adaptive controller.Prove that the control system is stable within a limited time.