Iterative ESO-Based Data-Driven Active Disturbance Rejection Learning Control of Czochralski Silicon Single Crystal Growth Process

Aiming at the problem of unstable control and low precision of key variables in the repeated operation of Czochralski silicon single crystal (Cz-SSC), this paper proposes a data-driven active disturbance rejection learning control (ADRLC) method based on iterative extended state observer (ESO). Firstly, the iterative dynamic linearization method transform the Cz-SSC growth system into an affine form, and the system uncertainty including disturbance is merged into a total term. Then, by designing ESO for iterative estimation of the nonlinear uncertainty. Finally, based on the ADRC strategy, an ADRLC controller with iterative parameter updating is designed and the convergence of tracking control error is proved theoretically. The entire learning control scheme does not require additional model information, except for the input and output data of the system. In addition, the effectiveness of the method is verified by the batch control results of crystal diameter. Compared with the traditional iterative learning control method, the proposed ADRLC method can estimate the uncertainty of the system along the iterative axis, and overcome the disturbance through the ADRLC controller to obtain accurate crystal diameter variable batch control results.