Flatness Intelligent Control Based on T-S Cloud Inference Neural Network

The accuracy of traditional flatness control methods are limited and it is difficult to establish a precise mathematical model of the rolling mill. In addition, the flatness control system is complex and multivariate. General model approaches can not satisfy the high precision, demand of rolling process. In this paper, T-S cloud inference neural network and its stability are proposed. It is constructed by cloud model and T-S fuzzy neural network. The stability of T-S cloud inference neural network is analyzed by Lyapunov method in details. Based on the new network, flatness recognition model and flatness predictive model are established. And they are applied for 900HC reversible cold rolling mill. The flatness control system is designed and a simple controller is developed. Initial parameters of the controller are firstly determined through offline training based on measured data, and then they are optimized online automatically. Genetic Algorithm (GA) is used as the optimizing method which is compared with particle swarm optimization (PSO). The simulation results demonstrate that the flatness control system is effective and has a better precision and robustness.