Iterative Learning Control And Its Applications In Gene Sequencing Stage Systems

Gene sequencing is one of the most important techniques in life science research.With the rapid development of gene sequencing technology in the fields of medicine,life science and drug research and development,the existing sequencing instruments have been unable to meet the demand for sequencing flux,it is necessary to develop a new generation of high-throughput gene sequencer.Stage system is one of the core subsystems of high throughput gene sequencer.Its motion speed is related to the flux of the sequencer,and the control precision is related to the quality of sequencing data.Therefore,the performance of high throughput gene sequencer stage system with large stroke,fast speed and high trajectory tracking accuracy is required.This paper is based on the application background of high-throughput gene sequencer,the high precision control system of ironless permanent magnet synchronous linear motor(ILPMSLM)is studied by using iterative learning control(ILC).By overcoming the disadvantages of ILC,the performance can be improved in the task with non-repetitive disturbance and variable trajectory tracking.It improved the tracking accuracy,reduced the adjustment time,and enhanced the anti-disturbance ability of the system.The paper research content is as follows:Firstly,the structural characteristics of the ILPMSLM are analyzed,and the mathematical model of the motor is established.Then,the current loop is designed and simulated by using vector control and space vector pulse width modulation.The model of the stage is established according to the two-mass spring damping system.On this basis,the model parameters are identified based on the closed-loop current loop,and the influence of parameter variation and external disturbance on the system are analyzed,which provided a theoretical basis for disturbance suppression in the subsequent design of the control algorithm.Aiming at the problem that ILC is easily affected by non-repetitive disturbances,an adaptive nonlinear composite ILC algorithm based on output data is proposed,and the stability of the algorithm is proved.Firstly,the multiple servo cycles second-order ILC is designed by multiple repeated motions to eliminate the non-repetitive signals in the tracking error that changed with iteration,and a forgetting factor is adjusted adaptively according to the positioning errors of two successive iterations to enhance the robustness.Secondly,an adaptive iterative estimation law is designed to compensate the disturbance in the iterative domain,and the influence of initial positioning error is overcome by introducing a time-varying boundary layer.Finally,based on the motion characteristics of the stage,nonlinear functions are introduced to adjust the gain of the controller adaptively in different phases of the motion,a larger gain is used in the actuation phase to improve the dynamic response capability of the system and accelerate the convergence speed of iterative learning.In dwell phase,small gain is used to suppress the influence of high frequency disturbance.The study shows that this algorithm can effectively suppress the influence of time-varying disturbance on ILC,improve the trajectory tracking accuracy and robustness of the system,and effectively shorten the adjustment time.ILC can only be used for a fixed reference trajectory,and it needs to be learned again when the trajectory changes.To solve this problem,a feedforward control algorithm based on second-order nonlinear iterative parameter tuning is proposed in this paper.The controller is parameterized by introducing the basis function that reflects the system dynamics behavior,and the parameters are optimized by iterative learning.The obtained controller can be suitable for different reference trajectories.In order to suppress the influence of disturbance such as force ripple on speed of linear motor,a composite control strategy of feedforward control and disturbance rejection control based on iterative parameter tuning is proposed.The advantages of feedforward control and disturbance rejection control are combined through iterative learning.The feedforward control is used to improve the response speed of the system and make the stage move into a uniform state quickly,and the disturbance rejection control is used to improve the anti-interference ability of the system.When switching different working speeds,the control system can always maintain high control accuracy and robustness.In addition,the controller design does not need the model or sensitivity function of the plant,and the whole design process is data-driven.Finally,the trajectory tracking experiments are carried out in a stage system driven by a ILPMSLM.Through the comparation experiment,the effectiveness and superiority of the proposed adaptive nonlinear composite iterative learning control and the feedforward control and anti-interference control algorithm based on iterative parameter tuning are verified.