For enhancing plasma parameters, stable plasma operation is a critical aspect. Precise control of the poloidal field (PF) coils promotes the exploration of plasma shape and significantly mitigates the damage caused by plasma disruptions to the device. In actual tokamak control, the adjustment of feedback control parameters and the development of new control algorithms are complex and time-consuming tasks that require substantial funding for validation and optimization. Since conventional simulation environments, though highly accurate, involve long computation times, there is an urgent need to construct a reliable RZIP (the displacement in the R direction, the displacement in the Z direction, and plasma current) response model for real-time control. This study utilizes a total of 1100 shots of historical data to develop a deep learning-based RZIP prediction model aimed at predicting RZIP data 10ms in advance. By employing a multi-task learning (MTL) approach, the model’s performance is enhanced to obtain a more accurate RZIP prediction model. Furthermore, by combining PID feedback control with the M-matrix (i.e. the decoupling matrix between RZIP and PF coils), a coil voltage conversion model is constructed, with RZIP as the input and PF coil control voltage as output. This research also derives more suitable PID control parameters for the current IP based on the IP data 10 ms later, leading to more reasonable PF coil control voltage data. The study predicts the RZIP using the RZIP MTL model, and the mean squared error between the predicted RZIP and the actual RZIP experimental data meets the experimental requirements. Additionally, the output voltage control for the PF coils, generated by combining PID feedback control with the M-matrix, satisfies the experimental needs.