毕业设计_神经网络控制算法仿真[汇编].docxVIP

文档收拾 | 学习参阅 collection of questions and answers 20XX 摘 要 现在，因为PID结构简略，可经过调理份额积分和微分获得根本满意的操控功用，广泛运用在电厂的各种操控进程中。电厂主汽温被控方针是一个大惯性、大拖延、非线性且方针改动的体系，惯例汽温操控体系为串级PID操控或导前微分操控,当机组安稳运转时,一般能将主汽温操控在答应的规模内。但当运转工况产生较大改动时,却很难确保操控质量。因而本文研讨根据BP神经网络的PID操控，运用神经网络的自学习、非线性和不依赖模型等特性完结PID参数的在线自整定，充沛运用PID和神经网络的长处。本处用一个多层前向神经网络，选用反向传达算法，根据操控要求实时输出Kp、Ki、Kd，顺次作为PID操控器的实时参数，替代传统PID参数靠经历的人整齐定和工程整定，以到达对大拖延主气温体系的杰出操控。对这样一个体系在MATLAB渠道上进行仿真研讨，仿真效果标明根据BP神经网络的自整定PID操控具有杰出的自习惯才能和自学习才能，对大拖延和变方针的体系可获得杰出的操控作用。 要害词：主汽温，PID，BP神经网络，MATLAB仿真 ABSTRACT At present, because PID has a simple structure and can be adjusted proportional 、integral and differential to satisfactory control performance, it is widely used in power plants of various control process. The system of power plant main steam temperature is an large inertia、big time-delayed and nonlinear dynamic system. Conventional steam temperature control system adopted cascade PID control or the differential control of lead before. When the unit is stable, these methods will control the steam temperature in a certain range ,but when operating conditions changed greatly, it is difficult to ensure the quality of control. This article studies PID control based BP neural network . Using such characteristics of neural network self-learning, nonlinear and don't rely on model realize PID parameters auto-tuning. It can make full use of the advantages of PID and neural network. Here, we use a multilayer feedforward neural network using back propagation algorithm. This net can real-time output Kp, Ki, Kd as the PID controller parameters , insteading of the traditional PID parameters determined by experience, so it can obtain good control performance .For such a system ,we can simulate in MATLAB simulation platform. The simulation results show that the PID control based BP neural network has good adaptive ability and self-learning ability. For the system of large delay and free-model can obtain good control effect. KEY WORDS: main steam temperature ，PID ，BP neural netwo