PSO Neural Network And Its Application In Control System Of Flatness And Gauge

Strip steel is substantial raw materials in national economy and the flatness and gauge are two key indices of performance. As the development of industry, the process of automatic flatness control and automatic gauge control has got more and more attention. In fact the complex system of automatic flatness control and automatic gauge control is a multi-input and multi-output nonlinear system with strong coupling and pure time delay. Therefore how to increase the control level of the flatness and gauge complex system has become hotspot and hard pot of related study.Particle Swarm Optimization (PSO) algorithm is based on swarm intelligence theory. The algorithm can provide efficient solutions for optimization problems through intelligence generated from complex activities such as cooperation and competition among individuals in the biologic colony. Basis PSO algorithm and its potential engineering applications such as neural network training and parameter setting of PID controller is introduced.Firstly, particle swarm optimization is introduced and its developments are reviewed. The basic applications of PSO algorithm and its engineering applications are summarized. The future research directions of PSO algorithm are pointed out and its potential applications are proposed.Secondly, the neural network theory and its structure are reviewed. Because of the great ability of simulation of human memory, analyzing and inference, neural network can approximate arbitrary nonlinear function. In order to avoid the defect in BP training algorithm a training method based on PSO algorithm is put forward. After analysis it is used to decouple in AGC-AFC system.Thirdly, the analysis of roll process is done and the mathematic model is established by the mathematic formula derivation of AGC-AFC system. According to the analysis above a control system of AGC-AFC based on neural network decoupling is designed. In the system a kind of controller is designed which is the combination of PSO algorithm and PID control theory. In the end of this article the whole control system is simulated and the results indicate that the system control scheme has excellent ability of decoupling and robustness.In short, the neural network decoupling scheme based on PSO algorithm has simple structure, good capability of decoupling and robustness, thus it can be realized in engineering easily. This scheme expand the application range of neural network and PSO algorithm in...