Optimal control of continuous annealing process using PSO

Abstract

The continuous annealing furnace is one of the main equipments in the continuous annealing production line for iron and steel enterprise, and has a direct impact on the quality of cold-rolled strip steel, production and cost. The temperature control of continuous annealing furnace is a complex industrial process which is difficult to control. It is difficult to obtain a good control result by using the traditional control method, so a temperature control method with good performance is of great significance. Particle swarm optimization (PSO) has evolved recently as an important branch of stochastic techniques to explore the search space for optimization (Kennedy & Eberhart, 1995). The motivation for the development of this method is based on simulation of simplified social behavior such as bird flocking or fish schooling. Nowadays, PSO has been developed to be real competitor with other well-established techniques for population-based evolutionary computation. PSO has many advantages over other evolutionary computation techniques (for example, genetic algorithms (GAs)), such as simpler implementation, faster convergence rate and fewer parameters to adjust. The proposed scheme is applied to the optimal of the continuous annealing process. Simulation shows the proposed approach is effective.