基于SMITH预估的神经网络烟草制丝线回潮控制系统

烟草制丝线的烟叶回潮控制系统具有时变性、大惯性和大滞后的特点,用常规的PID控制等方法难以取得满意的控制效果。将Smith补偿原理和PID参数的自适应调整方法相结合,提出了基于BP神经网络整定的Smith—PID控制算法,提高了对被控对象参数变化的自适应能力。Smith预估控制能够克服对象的大迟延特性,克服了常规PID算法不适应大时滞系统控制和常规Smith算法过于依赖模型精度的缺陷。仿真结果表明,算法的控制性能最优,具有良好的鲁棒特性。     

神经网络在气动机械手控制器中的应用

气动机械手控制系统是一个非线性系统,采用常规的PID控制方法难以获得较好的控制效果.将具有自学习和自适应能力的单神经元模型与常规的PID控制算法相结合,设计了单神经元自适应PID控制器,并将其应用于机械手气动压力伺服系统中,控制器采用DSP实现.运行结果表明,该控制器能够适应被控对象在较大范围内的变化,具有较强的鲁棒性,其控制品质优于常规PID控制器.     

单神经元自适应PID控制在气动压力伺服系统中的应用

将具有自学习和自适应能力的单神经元模型与常规的PID控制算法相结合,设计了单神经元自适应PID控制器,并将其应用于气动压力伺服系统中。实验结果表明,采用单神经元自适应PID控制的气动伺服系统能够适应被控对象在较大范围内的变化,具有较强的鲁棒性,其控制品质优于常规PID控制器。     

一种改进的模型驱动PID的应用研究

对模型驱动PID（简称MD-PID）控制算法进行介绍,提出了一种改进的模型驱动PID （简称A-MD-PID）控制算法,并应用于二阶惯性加纯迟延对象,用MATLAB进行仿真研究。结果表明,本文采用的改进模型驱动PID,能有效地克服被控对象的惯性和迟延,提高系统的控制品质。     

基于结构改进Smith预估单神经元PSD的长时延NCS研究

针对长时延网络控制系统（NCS）中存在时延和被控对象等问题,提出了一种新型结构改进Smith预估单神经元PSD控制器.新型控制器由结构改进Smith预估器和单神经元PSD控制器组成,结构改进Smith预估器通过对传统Smith预估器模型结构上的改进,避免了建立网络时延的预估模型,无需对网络时延进行在线测量、估计或辨识,补偿了网络时延对系统的性能带来的影响;单神经元PSD控制器解决了结构改进Smith预估器的模型失配问题,通过Matlab环境下的TrueTime工具箱对NCS进行仿真实验,并对传统PID控制方法、结构改进Smith预估PID控制方法和结构改进Smith预估单神经元PSD控制方法进行了仿真比较.研究结果表明,该新型控制器有效地补偿了网络时延对NCS的性能带来的影响,在模型失配情况下具有良好的鲁棒性和适应性,是对长时延NCS-种可行且有效的控制方法.     

MATLAB环境下的单神经元自适应实时控制系统

利用神经元模型和自学习功能构成自适应PID控制器，在Madab实时开发环境xPC Target下建立液位实时控制系统。给出了单神经元自适应PID控制器的结构和控制算法，介绍了基于xPC Target的快速原型设计方法，即通过拖拉Simulink模块搭建被控对象的实时原型，同时以神经元自适应PID控制器完成系统的实时控制。为检验控制效果还采用了PID控制器进行液位实时控制。最终结果表明：单神经元自适应控制能克服传统PID控制器不稳定的缺点，使系统具有较好的稳定性。     

基于T-S模糊建模的广义预测控制系统在热工过程中的应用

由于过热汽温系统具有大惯性、大迟延和时变性的特性,传统的PID串级控制策略很难达到理想的控制效果.因此,本文被控对象的模型利用基于模糊聚类算法的T-S建模法,并利用广义预测控制算法对过热汽温系统进行控制.针对该算法在火电厂过热汽温控制系统中进行了仿真,仿真结果表明,该算法较常规PID控制方法具有更好的控制性能.     

基于PLC的单神经元PID控制器的设计与实现

本文对于大滞后、时变和非线性的复杂系统,常规PID控制显得无能为力。将神经网络与常规PID控制相结合,构成单神经元自适应PID控制器。给出了基于PLC的单神经元PID控制系统结构,重点介绍了单神经元PID控制算法原理,并用结构控制语言编写了单神经元PID控制算法功能块,该控制功能块具有通用性且易于移植。经实例证明,与传统PID控制器相比较,单神经元PID控制器可以显著改善系统的性能。     

基于LonWorks的智能PID控制器的设计

介绍了基于Lon Works网络的智能PID控制器的设计，分析系统的硬件构成及其基于单神经元预测的PID控制算法，其可以实现权值的在线自适应调整。通过MATLAB仿真表明该方案对参数变化、具有时滞的被控对象可以获得良好的动态性能。     

模糊PID控制器在过热汽温控制中的应用

结合模糊控制技术和PID控制器的优点,提出一种模糊PID控制算法.该算法应用于火电厂过热汽温控制系统的仿真研究表明:该控制方案具有良好的控制品质并能适应被控对象参数的变化,提高了系统的稳态精度和动态性能.     

一种新的控制方法在主汽温控制中的应用研究

针对电厂中主汽温控制系统具有大滞后，非线性的特点,提出了一种基于Smith预估器的单神经元PSD自适应控制算法．即由Smith预估器和单神经元PSD自适应控制器组合的复合控制。仿真结果表明,这种控制方式具有较好的自适应性和鲁棒性。     

多温区电加热炉温度控制算法的研究与应用

针对多温区电加热炉大时滞、时变性强、强耦合和对象模型难以确定等特点,提出一种基于继电辨识模型的PID自整定算法,该法将Smith预估算法、继电辨识和模糊控制相结合,用继电反馈法在线辨识出Smith预估所需要的对象模型,并整定出控制器PID的初始值,在此基础上,对系统进行Smith预估补偿的同时利用模糊控制在线修正PID参数.经实验证明将该算法运用于多温区电加热炉的温度控制中是合适的,可获得很好的控制精度和鲁棒性.     

具有迟滞特性的水下小车牵引系统控制方法

提出了基于改进Smith预估补偿的模糊PID控制器,实现对水下液压绞车牵引小车的精确速度控制。改进Smith预估补偿具有较好的解决迟滞环节对于控制系统的能力,模糊PID控制器具有较好的模型自适应性,两者相结合能够较好的解决水下小车速度控制系统的系统迟滞及参数时变对控制结果所带来的影响。利用MATLAB软件对液压绞车牵引小车的速度控制系统进行仿真,并对结果进行分析。     

Steam temperature controller with LS-SVR-based predictor and PID gain scheduler in thermal power plant

Nonlinearity and time delay in thermal power plant processes during the period of steady state load have been reported to be efficiently compensated for by controlling the open rate of sprays and an angle of burner tilt using a conventional cascade PID controller. However, it is not easy to compensate severe nonlinearity and time delays simultaneously due to feed water and coal flow variations during load changes. This paper introduces advanced method to mitigate these problems. A predictor based on the least square support vector machine for regression (LS-SVR) algorithm is developed to efficiently compensate the nonlinearity in the boiler system, and it enables accurate modeling of the steam temperature by applying LS-SVR algorithm by using one variable, steam temperature, for the superheater and reheater systems. Moreover, the predictor enables to compensate the time delay by generating a prior control action, based on the predicted steam temperature after a certain time interval. An LS-SVR-based predictor is combined with a PID controller that uses a gain scheduler based on an anti-reset windup algorithm to enable more sensitive and efficient steam temperature control during load changes to the boiler system in a thermal power plant. A load-changing simulation is conducted, and the proposed steam temperature controller demonstrates a more stable and efficient performance than a conventional cascade PID controller.     

Application of Smith Predictor Based on Single Neural Network in Cold Rolling Shape Control

Flatness is one of the most important criterion factors to evaluate the quality of the steel strip. To improve the strip' s flatness quality, the most frequently used methodology is to employ the closed-loop automatic shape control system. However, in the shape control system, the shape-meter is always installed at the down way of the exit of the cold rolling mill and can not sense the changes of the strip flatness in the rolling gap directly. This kind of installation results in the delay of the feedback in the control system. Therefore, the stability and response performance of the system are strongly affected by the delay. At present, there is still no mature way to design controllers for systems with time delay. Although the conventional PID controller used in most practical applications has the capability to comte the delay, the effect of the compensation is limited, especially for the systems with long time delay. Smith predictor, as a compensator for solving this problem, is now widely used in industry systems. However, the request of highly precise model of the system and the poor adaptive performance to the changes of related parameters limit the application of the Smith predictor in practice. In order to overcome the drawbacks of the Smith predictor, a new Smith predictor based on single neural network PID (SNN-PID) is proposed. Because the single neural network is employed into the Smith predictor to improve the controller's self-adaptability, the adaptive capability to the varying parameters of the system is improved. Meanwhile, for the purpose of solving the problems such as time-consuming and complicated calculation of the neural networks in real time, the learning coefficient of neural network is divided into several stages as usually done in expert control system. Therefore, the control system can obtain fast response due to the improved calculation speed of the neural networks. In order to validate the performance of the proposed controller, the experiment is conducted on the shape control system in a 300 mm four-high reversing cold rolling mill. The experimental results show that the SNN-PID with Smith predictor controller can effectively compeusate the delay effects and achieve better control performance than the conventional PIO controller.     

一种新的积分过程PID控制器整定方法

针对积分过程，提出了一种简单、实用的PID控制器。该方法首先通过PD控制器对积分过程建立内部反馈控制回路，将内部闭环系统近似为二阶加纯滞后模型；然后，根据有效的模型，基于鲁棒性能指标设计外部反馈回路的PID控制器；最后，简化控制系统的结构，采用设定值加权PID控制器控制积分过程。仿真结果表明了设定值加权PID控制器的有效性和可行性。     

基于改进Smith预估器的显微视觉伺服

针对视觉延迟的问题,提出采用改进的Smith预估器(MSP)来改善视觉伺服系统的控制品质。在对基于位置的动态"look-and-move" 视觉伺服系统特性分析的基础上,建立了基于MSP视觉伺服系统的结构,同时建立了视觉伺服系统的分时模型。在微操作机器人平台上进行了点到点、抗干扰和微齿轮的跟踪抓取实验。结果表明,与只有PID控制器的视觉伺服系统相比,带MSP的视觉伺服系统具有更好的控制品质。克服了由于图像采集、传输和处理导致的视觉伺服延迟而严重影响控制系统的品质局限,并克服了传统方法通过减小系统增益来增强系统的鲁棒性,但同时降低了系统响应特性的缺点。     

Design of a new PID controller using predictive functional control optimization for chamber pressure in a coke furnace

An improved proportional-integral-derivative (PID) controller based on predictive functional control (PFC) is proposed and tested on the chamber pressure in an industrial coke furnace. The proposed design is motivated by the fact that PID controllers for industrial processes with time delay may not achieve the desired control performance because of the unavoidable model/plant mismatches, while model predictive control (MPC) is suitable for such situations. In this paper, PID control and PFC algorithm are combined to form a new PID controller that has the basic characteristic of PFC algorithm and at the same time, the simple structure of traditional PID controller. The proposed controller was tested in terms of set-point tracking and disturbance rejection, where the obtained results showed that the proposed controller had the better ensemble performance compared with traditional PID controllers.     

电加热炉控制的免疫PID应用研究

温控系统在工业现场是一个大滞后、强耦合、非线性的系统,常规PID控制器很难取得优良的控制效果。文章依据免疫系统的自适应反馈调节作用,提出了免疫PID控制器的实现方法,并将其应用到常规温度控制系统中。仿真结果表明,免疫PID控制器具有良好的控制品质,当系统发生扰动时具有较强的鲁棒性和自适应能力。     

基于比例-模糊积分控制算法的智能温控器优化与仿真

针对室内温度系统具有大时滞性、大惯性等特点,传统的控制方法效果不甚理想,提出了一种比例-模糊积分的复合控制策略,以提高碳纤维电地暖系统中智能温控器的控制性能。阐述了比例-模糊积分控制器的工作原理,并对基于模糊控制算法和PID 控制算法相结合的控制算法进行了详细设计。 MATLAB仿真和实验结果表明：与基于开关式控制、PID控制和模糊控制算法的智能温控器相比,基于比例-模糊积分控制算法的智能温控器所需调节时间短,能有效抑制超调,具有良好的动静态特性和鲁棒性,显著改善了控制效果。