Research On Decoupling Control For Looper System Based On Improved PSO

With the development of the steel industry,it puts forward higher request to the production efficiency,dimensional precision and performance of the products in the process of industrial production.The control of looper system largely determines the performance of strip steel,however,the looper system is a coupled system with double input and double output.In order to improve the quality of strip steel products,only by solving the strong coupling problem between looper height and strip tension can looper system work stably and efficiently.So far,there are several control methods under a lot of research at home and abroad,such as the conventional PID control,Non-interactive decoupling control,the optimal multivariable control,neural network control,etc.In order to overcome some defects of previous control methods,such as the interaction incompletely eliminate,low control accuracy,weak dynamic response performance.A new decoupling control method will be proposed.The connection weights of PIDNN easily get into precocious convergence in the learning process,which is difficult to achieve the ideal control effect.So,the connection weights of PID neural network is optimized by the particle swarm optimization algorithm.The basic PSO has the characteristics of fast convergence and strong universality,but it has the problems of precocious convergence,low search accuracy and low efficiency with the iteration.Therefore,to solve these problems an improved particle swarm optimization algorithm is proposed on the basis of the combination of mutation mechanism and the particle swarm optimization algorithm.PID neural network connection weights optimized by the improved particle swarm algorithm.The simulation results show that the proposed algorithm makes up for the deficiency of the PID neural network and improve the performance of fast convergence and anti-interference ability,the method is proven to be good decoupling ability.