英语翻译目前,由于PID结构简单,可通过调节比例积分和微分取得基本满意的控制性能,广泛应用在电厂的各种控制过程中.电厂主汽温被控对象是一个大惯性、大迟延、非线性且对象变化的系统

英语翻译目前,由于PID结构简单,可通过调节比例积分和微分取得基本满意的控制性能,广泛应用在电厂的各种控制过程中.电厂主汽温被控对象是一个大惯性、大迟延、非线性且对象变化的系统
英语翻译
目前,由于PID结构简单,可通过调节比例积分和微分取得基本满意的控制性能,广泛应用在电厂的各种控制过程中.电厂主汽温被控对象是一个大惯性、大迟延、非线性且对象变化的系统,常规汽温控制系统为串级PID控制或导前微分控制,当机组稳定运行时,一般能将主汽温控制在允许的范围内.但当运行工况发生较大变化时,却很难保证控制品质.因此本文研究基于BP神经网络的PID控制,利用神经网络的自学习、非线性和不依赖模型等特性实现PID参数的在线自整定,充分利用PID和神经网络的优点.本处用一个多层前向神经网络,采用反向传播算法,依据控制要求实时输出Kp、Ki、Kd,依次作为PID控制器的实时参数,代替传统PID参数靠经验的人工整定和工程整定,以达到对大迟延主气温系统的良好控制.对这样一个系统在MATLAB平台上进行仿真研究,仿真结果表明基于BP神经网络的自整定PID控制具有良好的自适应能力和自学习能力,对大迟延和变对象的系统可取得良好的控制效果.
别弄自动翻译出来的.3q3q

英语翻译目前,由于PID结构简单,可通过调节比例积分和微分取得基本满意的控制性能,广泛应用在电厂的各种控制过程中.电厂主汽温被控对象是一个大惯性、大迟延、非线性且对象变化的系统
Currently, the PID structure is simple, but by adjusting the proportion integral and differential obtain basic satisfactory control performance, widely used in power plant all kinds of control process. Power plant main steam temperature controlled object is a big inertia, time-delayed, nonlinear and object changes system, conventional steam temperature control system for cascade PID control or guide differential control, when the unit before the stable operation, the general will main steam temperature control in allowing range. But when running condition when great changes have taken place, but it was difficult to ensure that the control quality. Therefore this paper studies based on BP neural network PID control, using neural network self-learning, nonlinear and not rely on the model of characteristics to realize PID parameters online auto-tuning, make full use of the advantages of PID and neural network. This place USES a multilayer feedforward neural network, and adopts back propagation algorithm, based on real-time output control requirements for Ki, Kd Kp mohan, PID controller, ordinal as the real-time parameters, instead of the traditional PID parameters depend on experience neatly and engineering setting, in order to achieve the temperature system of time-delayed Lord good control. For such a system in MATLAB simulation research, the simulation results show that based on the BP neural network auto-tuning PID control has fine self-adaptive capacity and the ability to learn, to change the system ChiYanHe object large can get a good control effect.