The Control Method For The Large Time Delay System Based On Neural Network

The rapid development of industrial production made the industrial systems more complex. The characters of large time delay, time-varing and high nonlinear present the higher requirements for the control systems of industry. The control systems with these characters widely exist. It is very difficult to control the system with large time delay. Traditional control methods generally base on the mathematical model of controlled object. In fact, because the control object is a complex of the electronic, mechanical, software and on-site environment. it is difficult to establish accurate mathematical model, it is also impossible to do so for some controlled objects. The neural networks have many good features such as the self-learning and self-organization, they can approximate any nonlinear function, so the application and theory of the neural network are used for the control of the large-time delay industrial systems. Extensive research results show that control systems based on neural network provide new ways for the control of large delay system.Now, the control systems of large time delay based on neural network have two types of structures, one is the combination of the neural network and conventional PID controller, another has the structure which only use neural network itself. The paper selects a style of neural network from these two structures. BP neural network with PID controller is the first structure, in which the control parameters are firstly adjusted by BP neural network with self-learning function, then these parameters are put into the PID controller to form the control laws to control the object. This paper analyzed the structure of BP neural network, presented the forward and back algorithm of BP in detail, and discussed the two major shortcomings of the BP algorithm which are the problem of falling into local minima and slow convergence. It presented the improved methods, gave the structures and algorithms of the BP neural network with PID control system. The paper used the standard BP algorithm and improved Vogl algorithm to stimulate the Furnace control with the first order large time delay. The simulation results show that the control quality in Vogl algorithm is better than the standard Bp algorithm. PID neural network control system belongs to the second control structure, in this structure, the paper analyzed the PID neural with the dynamic processing power, presented the block diagram and algorithm of the PID neural control system. By making simulations for the object of the typical second-order large time-delay, the paper discussed the practicality of the algorithm from the step response, anti-jamming capability and robustness.Finally, the paper mainly discussed the control system of the PID neural network combined with the Smith predictor, gave the combination algorithm and associated procedures and simulation. The simulation results show that the combined control system has been improved more than the one which only uses PID neural network control system.