Hysteresis Modeling And Control Of Piezoelectric Micropositioning Stage

With the rapid development of high technology,the control of micro field has become a popular topic,and the micro-nano positioning technology has attracted much attention.In order to meet the requirements of high precision,intelligent materials have entered the research horizon,among which,the micro positioning platform driven by piezoelectric ceramic materials is particularly widely used in positioning engineering because of its advantages of fast frequency response,good dynamic performance and high resolution,however,the inherent However,the inherent hysteresis characteristics of piezoelectric ceramic materials can affect the positioning accuracy and system stability.In order to solve this problem and give full play to its excellent performance,this paper conducts a modeling and compensation study on the hysteresis characteristics of piezoelectric ceramic micro-positioning platforms.Firstly,the composition and experimental procedures of the piezoelectric ceramic micro positioning platform are introduced,and the current status of domestic and international research on the modeling and control of hysteresis characteristics is summarized.Then the Hammerstein model is established to accurately characterize the hysteresis behavior of the platform,in which the Prandl-IshIinskii operator model is chosen for the nonlinear part of the hysteresis,and the least squares method and the particle swarm optimization algorithm are used to identify the unknown parameters respectively,and the identification method with less error is chosen for the comparative analysis.The second-order transfer function model is obtained,and the Hammerstein model is obtained by cascading the two.Finally,the piezoelectric actuator is driven by input signals of different frequencies,and the output displacement is collected and compared with the output displacement of the proposed model to verify the validity of the proposed model.In order to eliminate the influence of the hysteresis characteristics,five control schemes are proposed in this paper: First,the Hammerstein inverse model is established as a feedforward controller for compensation,and the experiments show that the feedforward inverse compensation effectively reduces the influence of the rate-dependent hysteresis characteristics on the system,but this scheme is open-loop control,and its anti-interference ability is poor.For this reason,PID feedback is introduced on the basis of open-loop control for compound control to enhance its antiinterference capability.In order to avoid the complexity of solving the inverse model of hysteresis,an adaptive RBF neural network controller without inverse model is designed to eliminate the hysteresis characteristics,approximate the inaccurate parameters,and strictly ensure the stability of the system,and the effectiveness of the proposed scheme is verified through experiments.In order to further improve the positioning accuracy,the sliding mode adaptive control and fast terminal sliding mode adaptive control are proposed based on the estimated hysteresis inverse model by combining the advantages of sliding mode control,and the stability of the control system is analyzed by the Lyapunov method.The experimental results reflect the superiority of the sliding mode control scheme,and the fast terminal sliding mode controller can ensure the global fast convergence and avoid singularity occurrence,and its control accuracy is the highest.