基于FNN的水泥回转窑分解炉温度控制

在水泥工业中,窑尾分解炉温度是一个重要的工艺参数,它的稳定对整条水泥生产线的稳产、高产和节能具有重大影响.用建立对象模型的传统控制方法实现对分解炉温度的控制非常困难,因此提出了一种基于模糊神经网络的温度控制方法,将模糊控制技术与神经网络技术相结合,用神经网络表示模糊控制规则,采用神经网络来实现模糊推理,利用神经网络的学习能力来达到模糊隶属函数和模糊规则的目的.Matlab仿真试验结果表明,模糊神经网络用于回转窑分解炉温度控制可以取得比较好的控制效果.这一点在水泥厂实际生产应用中也得到了验证.     

水泥分解炉出口温度的多变量模糊控制

以2 000 t/d新型干法水泥分解炉为研究对象,使用前馈-负反馈的多变量模糊控制方法,实现了分解炉出口温度的控制。建立了水泥分解炉出口温度数学模型(MISO),使用VC++6.0对水泥分解炉出口温度多变量模糊控制与常规的PID进行仿真对比研究。研究结果表明:前馈-负反馈的多变量模糊控制效果优于传统PID控制。为便于工程实现,采用西门子WinCC6.0为OPC服务器端,自行开发的分解炉出口温度VC多变量模糊控制软件为OPC客户端,通过OPC接口成功地实现了二者间的数据通信,且系统运行稳定、效果良好,具有很强的实际应用意义。     

基于BFCM-iWM模糊规则自提取的水泥分解炉温度控制

针对水泥分解炉温度难以控制、模型建立困难且通用性差的问题,提出一种基于信任度模糊C聚类和改进查表法(BFCM-iWM)模糊规则自提取的温度控制方法.首先,采用信任度模糊C聚类方法(BFCM)从分解炉运行样本数据中提取样本的信任度;然后,利用改进查表法(iWM)提取模糊控制规则,减小噪声和坏点数据对规则提取的影响,以温度偏差和偏差变化率为输入量、喂煤增量为输出量构造控制器实现对温度的控制.实验结果表明,提取到的控制规则鲁棒性好、准确性高、分解炉温度控制效果良好.     

基于T-S模糊神经网络分解炉燃烧控制系统设计

针对分解炉分解是非线性、大滞后、多扰动及多变量过程,难以实现其对温度自动控制的问题,提出了一种基于T-S神经网络的控制方法。针对这一问题,文章首先对水泥预分解工艺进行分析以及对燃烧理论进行研究,然后再利用T-S模糊控制理论确定规则数目和输入变量的隶属度函数,采用神经网络的自学习和自适应能力实现模糊推理。仿真结果表明：该控制器对分解炉燃烧控制起到很好的控制效果,并且比传统PID控制器具有更好的效果。在实际生产应用当中,具有很好地稳定性和鲁棒性,并且节省了煤的消耗和降低了环境污染。     

双模模糊预测控制在水泥回转窑生料系统中的应用

针对水泥回转窑的大惯性、滞后等工艺特点，提出了一种模糊预测协调控制算法。将模糊控制与预测控制相结合，引入双模功能．增强了算法的跟踪与抗干扰能力。现场运行结果表明该算法简捷实用，控制效果优于传统的PID控制。     

水泥回转窑分解炉温度的模糊控制

介绍了模糊控制技术在水泥回转窑分解炉温度控制中的成功应用,并讨论了水泥回转窑生产过程ＤＣＳ系统的总体结构和应用软件开发。实际运行结果表明：系统稳定可靠、控制效果良好、满足了生产工艺要求。     

水泥回转窑分解炉温度的模糊控制

分析了目前水泥工业自动化技术的现状,介绍了模糊控制技术在水泥回转窑分解炉温度控制中的成功应用、水泥回转窑生产过程DCS的总体结构和应用软件开发。运行结果表明,系统稳定可靠,控制效果良好,能满足生产工艺要求。     

模糊控制与PID控制在水泥生产中的应用

介绍了模糊控制技术在吉林亚泰水泥回转窑分解炉温度控制中的成功应用,将模糊控制技术与PID相结合实现回转窑分解炉温度的自动控制,是一种先进的控制理念,提高了控制系统的智能化程度。该控制方案在实际应用中控制效果良好,系统稳定,易于操作员操作,完全满足了生产工艺要求。     

模糊控制技术在水泥回转窑分解炉温度控制中的应用

介绍了模糊控制技术在水泥回转窑分解炉温度控制中的成功应用,并讨论了水泥因转窑生产过程ＤＣＳ系统的总体结构和应用软件开发。实际运行结果表明：系统稳定可靠,控制效果良好,满足了生产工艺要求。     

大滞后系统的专家-模糊PID控制器设计

在工业过程中,大滞后系统的时滞、非线性情况严重,常规PID控制往往难以获得理想的控制效果.针对上述情况,设计了一种基于专家经验的模糊与PID并联复合型控制器,能够在PID控制的基础上改善系统的控制品质.介绍了带有可调因子的模糊控制算法,给出了模糊控制器相关参数的整定方法.根据专家经验制定了模糊控制与PID控制的加权系数的整定规则.对具有大迟延、大滞后特性的循环流化床的燃料量与床温模型进行了仿真研究.仿真结果表明:在大滞后系统的控制中,所设计的专家模糊PID控制器能够很大程度的改善系统的动态动性,且具有较好的鲁棒性.     

Design and Stability Analysis of Fuzzy Model-based Predictive Control – A Case Study

In the paper a fuzzy model based predictive control algorithm is presented. The proposed algorithm is developed in the state space and is given in analytical form, which is an advantage in comparison with optimisation based control schemes. Fuzzy model-based predictive control is potentially interesting in the case of batch reactors, heat-exchangers, furnaces and all the processes with strong nonlinear dynamics and high transport delays. In our case it is implemented to a continuous stirred-tank simulated reactor and compared to optimal PI control. Some stability and design issues of fuzzy model-based predictive control are also given.     

PEMFC的模糊辨识和非线性预测控制

质子交换膜燃料电池(PEMFC)是21世纪最有前景的发电技术之一。该文针对PEMFC等一类具有严重非线性的复杂被控对象,提出一种基于模糊模型的非线性预测控制算法对PEMFC系统进行建模与控制。在建模与控制过程中,采用模糊聚类和线性辨识方法在线建立PEMFC控制系统的T-S模糊预测模型,然后基于分支定界法的基本原理对控制量进行离散寻优,从而实现PEMFC的非线性预测控制。仿真和实验结果证明了该算法的有效性和优越性。     

GA-based nonlinear predictive switching control for a boiler-turbine system

In this paper,the problem of designing a controller for a highly coupled constrained nonlinear boilerturbine system is addressed with a predictive controller.First,a nonlinear predictive control is imp...     

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。     

多变量模糊预测控制及其应用研究

将文献「３」的单变量模糊预测控制方法加以推广，提出一种多变量模糊预测控制算法，并将其应用无机组负荷控制系统。仿真结果表明，所提出的多变量模糊预测控制方法是有效的。     

大射电望远镜馈源轨迹跟踪的模糊预测控制

针对新一代大射电镜望远镜悬索馈源系统为时变大滞后的被控对象，并充分考虑易于实现的工程要求，提出一种由模糊预测器和模糊控制器组成的馈源轨迹跟踪自适应模糊预测控制方案。数值仿真证明了该方案的有效性。     

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。     

Fuzzy predictive PI control for processes with large time delays

This paper presents the design, tuning and performance analysis of a new predictive fuzzy controller structure for higher order plants with large time delays. The designed controller consists of a fuzzy proportional-integral (PI) part and a fuzzy predictor. The fuzzy predictive PI controller combines the advantages of fuzzy control while maintaining the simplicity and robustness of a conventional PI controller. The dynamics of the prediction term are adaptive to the system's time delay. The prediction term has two parts: a fuzzy predictor that uses the system time delay as an input for calculating the prediction horizon and an exponential term that uses the prediction horizon as its positive power. The prediction term also introduces phase lead into the system which compensates for the phase lag due to the time delay in the plant, thereby stabilizing the closed-loop configuration. The performance of the proposed controller is compared with the responses of the conventional predictive PI controller, showing many advantages of the new design over its conventional counterpart.     

Force Control of Robotic Manipulators Using a Fuzzy Predictive Approach

This paper proposes a force control strategy for robotic manipulators considering a non-rigid environment described by a nonlinear model. This approach uses a fuzzy predictive algorithm to generate, in an optimal way, the reference or virtual position to the classical impedance controller in order to apply a desired force profile on the environment. The main advantage of this control strategy is the possibility of including a nonlinear model of the environment in the controller design in a straightforward way, improving the global force control performance, especially in non-rigid environments. Moreover, in order to reduce the oscillations on the optimized reference position a fuzzy scaling machine is included on the force control strategy. The performance of the force control scheme is illustrated for a two degree-of-freedom PUMA 560 robot, which end-effector is forced to move along a flat surface located on the vertical plane. The simulation results obtained with the fuzzy control scheme reveal significant improvement in the force tracking performance, when compared to the impedance control with force tracking in non-rigid environments.     

Predictive Functional Control Based on Fuzzy Model: Comparison with Linear Predictive Functional Control and PID Control

The implementation of the fuzzy predictive functional control (FPFC) on the magnetic suspension system is presented in the paper. The magnetic suspension system was in our case the pilot plant for magnetic bearing and is an open-loop unstable process, therefore a lead compensator was used to stabilize it. The high quality control requirements were a-periodical step response and zero steady-state error. Adding the integrator to a feedback causes overshoot. The solution to the problem was cascade control with fuzzy predictive functional controller in the outer loop. To cope with the unknown model parameters and the nonlinear nature of the magnetic system, a fuzzy identification based on FNARX model was used. After successful validation the obtained fuzzy model was used for controller design. The FPFC is compared with a cascade linear predictive functional control (PFC) and PID control. The results we obtained with the FPFC are very promising and hardly comparable with conventional control techniques.