Model free adaptive control of strip temperature in continuous annealing furnace based on quantum-behaved particle swarm optimization

This study develops a novel control scheme to address the challenge of establishing a heat transfer mechanism model for continuous annealing furnaces, which poses obstacles to the implementation of conventional model-based control strategies for regulating strip annealing temperature. The proposed approach involves integrating partial form dynamic linearization with model-free adaptive control (MFAC) using sliding time window technology to enhance adjustability and flexibility. In addition, an energy function penalty term is incorporated into the performance index function to minimize energy loss. Besides, an enhanced quantum-behaved particle swarm optimization algorithm is introduced, addressing the problems associated with parameter tuning in the MFAC algorithm. Finally, the developed method is applied to simulate continuous annealing furnace operations in a cold rolling environment and is compared with conventional MFAC and proportional-integral-derivative control methods. The results indicate that the proposed algorithm is more efficient compared to existing algorithms, with a mean absolute error of 4.85 degrees C and an energy conservation rate of 4.3%.