Self-tuning regulatory controller of cyclical disturbances using data-driven frequency estimator based on fuzzy logic

This paper proposes two learning-type regulatory controllers: adaptive fuzzy iterative learning controller (AFILC) and adaptive fuzzy repetitive generalized predictive controller (AFR-GPC). The proposed controllers can reject the cyclical disturbances with small range frequency variations present in many control loops of industrial processes. Moreover, they can estimate the disturbance cycle using fuzzy logic with rules based on the previous values of the integral of the absolute error between the setpoint and output. The estimated period of the disturbance cycle is sent to the regulatory controllers to define the specific control actions required to reduce the oscillations in the process output. The advantage of this technique is that adaptive controllers are automatically adjusted by a frequency estimator, which only depends on process output data. The performance levels of the developed adaptive controllers are compared in the computational simulator of a nonlinear mold level of a continuous casting process employed in the steel industry. This process counteracts the periodic oscillations in the mold level caused by bulging disturbance. The numerical results show that both AF-ILC and AFR-GPC can reduce the oscillations in the output when the disturbance frequency changes by up to 5.2% and 13%, respectively.

Automated summary

This paper proposes two learning-type regulatory controllers, which use fuzzy logic and adaptive techniques to counteract cyclical disturbances in industrial processes and automatically adjust the controllers to reduce output oscillations. Numerical results verify the effectiveness of these two controllers.