一种基于内模结构的模糊控制算法及应用

基于模型的预测控制算法在工业控制中获得了广泛的应用,特别是针对非线性大时滞系统.本文提出了一种基于内模结构的模糊控制算法,它的最大特点是引入模糊内模预估器作为被控过程的内部模型,对过程输出进行预测,从而克服时滞对系统带来的不利影响.同时,根据预测误差,滤波器在线修正、补偿被控过程的模型失配.仿真及炉温实时控制实验结果表明,此种控制策略优于常规控制方法,实用前景明显.     

高阶模糊内模预测控制算法及在间歇造气炉中的应用

针对大时滞系统,提出一种高阶模糊内模预测控制算法.通过引入智能因子,被控对象的高阶模糊内部模型可实时预测对象输出,在线修正模型失配,从而克服时滞因素的不利影响.将算法应用于化工间歇造气炉集散控制系统中,现场运行结果验证了算法的实用性.     

制粒机温控系统设计与仿真

制粒机调质温度是现代饲料生产过程中的重要参数之一,必须控制在最佳值附近。而调质温度被控对象具有大时滞,非线性以及参数时变的特性。传统的Smith预估器难以取得良好的控制效果。针对上述问题,提出一种IMC-Smith预估控制算法,算法将Smith预估器与内模控制相结合,采用内模控制原理和积分平方差指标设计给定点追踪控制器。在Matlab平台上对IMC-Smith控制算法进行模型匹配和模型失配的仿真,仿真结果表明控制系统性能较常规Smith预估控制有较大的提高,证明了控制方案的有效性。     

基于继电辨识的大时滞对象的内模控制

采用改进的饱和继电反馈实验辨识高精度的过程模型，将内模控制与Smith预估控制器相结合，提出了一种适用于大时滞对象的内模控制结构，并按内模控制结构设计控制器。仿真实验表明该控制结构可获得比Smith预估控制更好的动态性能、鲁棒性和抗干扰性能，为大时滞对象的控制提供了一种新的实用方法。     

基于改进Smith预估器的二阶时滞系统

在关于工业过程控制的研究中,针对常规Smith预估器是基于时滞对象精确数学模型,而在实际应用中,当被控系统的参数不稳定或受到扰动时,就会造成Smith预估模型的失控,使得控制效果变差甚至振荡.尽管模糊 Smith预估控制对系统参数的变化不敏感,但并没有从本质上克服预估模型失控给系统带来的不稳定性影响.为解决时滞引起失控的问题,提出了一种改进的模糊Smith预估控制方法,并对方法进行了仿真.仿真结果表明,能够增强系统的稳定性、鲁棒性,并且具有较好的控制精度,为实际工程设计提供了参考.     

针对大时滞过程下的改进内模控制方法研究

针对工业过程中较难控制的大滞后系统,提出了一种改进内模控制方法.该方法首先引入滞后时间削弱环节来降低滞后对系统的影响,然后在设定值之后增加导引过渡过程解决系统快速性与超调之间的矛盾,最后再基于内模控制的原理设计控制器.该方法解决了传统Smith预估控制等方法在被控对象模型预估不准确的情况下很难取得较好控制效果的问题,并...     

线滞后系统的控制算法比较

本文介绍了用于时滞系统的Smith预估补偿控制、采样PI控制和预测控制方法的原理,并对一阶纯滞后对象,采用Matlab工具对其控制性能进行仿真比较.仿真结果表明,Smith预估补偿算法在滞后被控对象模型已知的情况下能够取得较好的控制效果,而在模型参数未知或不准确情况下,适合采用采样PI控制或预测控制.     

改进的模糊内模控制在时滞系统中的应用

采用模糊控制器和内模控制器相结合,对模糊内模控制方法进行了研究。将Smith预估器和内模控制器结合起来,使用模糊控制器作为主控制器。用积分环节消除系统的静态误差,用惯性环节低通滤波来改善模型失配较严重的情形。能在较大的模型误差范围内得到良好的控制品质。通过时滞系统试验表明本文提出的控制方案同常规控制方法相比有了更好的鲁棒性和较好的控制性能。     

时滞对象的自适应Smith预估控制方案

针对时滞被控对象提出了一种自适应Ｓｍｉｔｈ预估控制方案,利用变遗忘因子递推最小二乘法进行参数在线辨识以构成Ｓｍｉｔｈ预估器,采用模糊神经网络控制器完成对被控对象的控制。仿真结果证明了这种方法的有效性。     

大时滞不确定过程对象参考自适应时滞补偿器——基于Popov超稳定理论的参数自适应律设计

给出了大时滞不确定过程的一种新型描述方式.提出了采用一族模型拟合大时滞不确定过程的对象参考参数自适应时滞补偿器.以大时滞不确定被控过程为参考对象,给出了基于Popov超稳定理论综合自适应律的方法.自适应律使时滞补偿器的参数逐渐逼近被控过程的参数,从而克服了Smith预估补偿器对模型偏差的敏感问题,大大提高了系统的抗扰动能力.仿真结果表明,参数自适应时滞补偿器对大时滞不确定过程及复杂高阶过程均有良好的自适应跟踪及预估补偿能力.     

Predictive functional control based on fuzzy T-S model for HVAC systems temperature control

In heating, ventilating and air-conditioning (HVAC) systems, there exist severe nonlinearity, time-varying nature, disturbances and uncertainties. A new predictive functional control based on Takagi-Sugeno (T-S) fuzzy model was proposed to control HVAC systems. The T-S fuzzy model of stabilized controlled process was obtained using the least squares method, then on the basis of global linear predictive model from T-S fuzzy model, the process was controlled by the predictive functional controller. Especially the feedback regulation part was developed to compensate uncertainties of fuzzy predictive model. Finally simulation test results in HVAC systems control applications showed that the proposed fuzzy model predictive functional control improves tracking effect and robustness. Compared with the conventional PID controller, this control strategy has the advantages of less overshoot and shorter setting time, etc.     

Predictive functional control based on fuzzy T-S model for HVAC systems temperature control

In heating, ventilating and air-conditioning （HVAC） systems, there exist severe nonlinearity, time-varying nature, disturbances and uncertainties. A new predictive functional control based on Takagi-Sugeno （T-S） fuzzy model was proposed to control HVAC systems. The T-S fuzzy model of stabilized controlled process was obtained using the least squares method, then on the basis of global linear predictive model from T-S fuzzy model, the process was controlled by the predictive functional controller. Especially the feedback regulation part was developed to compensate uncertainties of fuzzy predictive model. Finally simulation test results in HVAC systems control applications showed that the proposed fuzzy model predictive functional control improves tracking effect and robustness. Compared with the conventional PID controller, this control strategy has the advantages of less overshoot and shorter setting time, etc.     

一种新的预测控制算法：模糊预测控制算法*

将模糊控制与预测控制相结合，提出了一种基于被控对象一般形式的时间离散模型的模糊预测控制算法，并对控制算法的有效性进行了分析，仿真研究结果表明，该模糊预测控制算法既适用于线性对象，也可用于非线性对象的控制。     

Interval type-2 fuzzy inverse controller design in nonlinear IMC structure

In the recent years it has been demonstrated that type-2 fuzzy logic systems are more effective in modeling and control of complex nonlinear systems compared to type-1 fuzzy logic systems. An inverse controller based on type-2 fuzzy model can be proposed since inverse model controllers provide an efficient way to control nonlinear processes. Even though various fuzzy inversion methods have been devised for type-1 fuzzy logic systems up to now, there does not exist any method for type-2 fuzzy logic systems. In this study, a systematic method has been proposed to form the inverse of the interval type-2 Takagi-Sugeno fuzzy model based on a pure analytical method. The calculation of inverse model is done based on simple manipulations of the antecedent and consequence parts of the fuzzy model. Moreover, the type-2 fuzzy model and its inverse as the primary controller are embedded into a nonlinear internal model control structure to provide an effective and robust control performance. Finally, the proposed control scheme has been implemented on an experimental pH neutralization process where the beneficial sides are shown clearly.     

利用BP算法的一种自适应模糊预测控制器

提出一种由模糊预测器和模糊预测控制器组成的自适应模糊预测控制方案,采用ＢＰ算法训练模糊预测器和模糊预测控制器,并给出这种模糊预测控制器的训练算法。控制系统对于具有纯时延的非线性被控过程有良好的控制性能。     

基于模糊线性化预测模型的HVAC系统温度控制

针对暖通空调（HVAC）系统难以控制的问题，提出一种基于max-product推理的Mamdani模糊模型预测控制策略．首先利用一步模糊预测模型的结构分析得到其解析表达式，获得系统在k＋1时刻的线性化预测模型；然后基于模糊线性化模型进行模型预测控制器设计．对HVAC系统的仿真和实验结果表明，该算法是一种跟踪性能好且鲁棒性强的有效控制算法．     

基于模糊预测PID算法的地下空间湿度控制

根据防护工程地下空间空调系统的时滞以及非线性特性,结合预测控制和模糊控制两种方法,设计了基于模糊预测PID算法的控制器.建立了系统的数学模型,并基于所提出的控制方法对其进行仿真研究,结果显示该控制器抗干扰能力较好,相较于传统PID控制,该控制器调节时间更快,控制效果更佳.     

一种线性化模糊内模自适应控制算法

针对非线性对象，提出一种线性化模糊内模自适应控制算法。该算法以一组模糊规则作为非线性对象内部模型，一条模糊规则表示一个局部线性系统；根据对象输入与输出测量值，利用TSK建模方法在线辨识局部模糊内部模型；同时依据辨识模型设计局部H2最优模糊控制规则，所有规则构成H2最优模糊控制器。仿真实验显示：该算法适用于非线性对象的控制，具有较好的鲁棒性和抗干扰能力。     

Predictive functional control based on fuzzy model forheat-exchanger pilot plant

In this paper, a new method of predictive control is presented. In this approach, a well-known method of predictive functional control is combined with fuzzy model of the process. The prediction is based on fuzzy model given in the form of Takagi-Sugeno type. The proposed fuzzy predictive control has been evaluated by implementation on heat-exchanger plant, which exhibits a strong nonlinear behavior. It has been shown that in the case of nonlinear processes, the approach using fuzzy predictive control gives very promising results. The proposed approach is potentially interesting in the case of batch reactors, heat-exchangers, furnaces, and all the processes that are difficult to model