Application of AFFDE-RBF-PID-PI Cascade Control in Heating Furnace Temperature Control System

Abstract

Given the large hysteresis in the material outlet temperature of the heating furnace, a differential evolution algorithm with adaptive adjustment factors (AAFDE)-radial basis function (RBF)-proportional integral derivative (PID)-PI cascade is proposed. First, we introduce an adaptive mutation factor into the differential evolution (DE) algorithm and define the individual merit coefficient to incorporate a self-adaptive crossover probability factor. Second, the initial parameters of RBF are optimized by the AFFDE algorithm, and RBF neural network model is established. Then, we obtain gradient information by RBF online identification. Finally, we perform online adjustments to the three parameters of PID based on the gradient information. The three parameters are applied to the adjustment of the main controller, while the sub-controller employs PI control. Experimental results show that the anti-disturbance performance of the AFFDE-RBF-PID-PI cascade control outperforms the improved differential evolution (IDE)-RBF-PID-PI cascade control, generalized opposition-based differential evolution (GODE)-RBF-PID-PI cascade control, and MCOBDE-RBF-PID-PI cascade control respectively by 16%, 11%, and 7%, with the response speed improving by 18%, 12%, and 9%, and the stability improving by 19%, 13%, and 8%. These results show that AFFDE-RBF-PID-PI cascade control exhibits enhanced anti-disturbance performance, faster response speed, improved stability, and superior control effect.

Keywords

• adaptive adjustment factor
• differential evolution algorithm
• radial basis function neural network
• cascade control
• heating furnace system