氯乙酸生产结晶过程的预测函数控制仿真研究

针对氯乙酸生产结晶过程被控对象大滞后、大惯性的特点,设计了基于PFC—PID的串级控制系统：该系统将串级控制结构和预测函数控制算法相结合,内回路采用常规PID控制器,外回路采用预测函数控制(PFC)。结合了2者的优点,仿真结果表明,该串级控制系统在控制品质、鲁棒性等方面明显优于常规PID控制系统。     

串级控制对过程预测控制的影响分析与对策

化工过程控制中普遍设置以流量控制为副回路的串级控制来实现对温度、液位和成分等被控变量的控制。预测控制的操作变量在很多情况下也是流量,其控制作用的实现要靠底层的流量控制回路。本文针对由于现场串级控制结构不允许改变,流量副回路只能接收温度等主控制器的输出作为其给定值,造成上层预测控制的操作变量无法直接下载到流量控制回路的问题,分别提出了一种将上层优化输出通过一阶惯性滤波作用于主回路控制器和一种将串级控制中流量对主被控变量的传递函数嵌入预测控制模型的实施方案,通过Shell标准重油分馏塔的控制问题进行仿真实验证明了两种方案的可行性,并对其控制性能进行了比较分析。两种方法理论上构思简单,实际中易于实现,具有普遍适用性。     

化工过程多回路PID控制系统模式切换参数自整定

化工过程一般为多变量系统,但其主要控制方案为分散多回路PID常规控制。由于多变量系统内部存在不同程度的耦合作用,各控制回路之间存在相互影响,当其他回路进行手动/自动模式切换时,本回路等效被控对象将会发生突变,导致本回路的原有控制参数不能适应等效被控对象的变化,造成控制性能下降,甚至闭环系统不稳定。为避免这种情况的发生,从整个系统的角度研究控制回路模式切换时的稳定性,采用多变量频域Nyquist阵列设计法。基于对角优势下正Nyquist稳定性判据,从Gershgorin圆边界点的角度定量分析各个控制回路在模式切换前后的稳定性变化程度,从而确定各回路控制器增益的调整方向及程度,实现各回路的控制器参数在控制回路模式切换瞬间的自动整定,尽可能抵消控制回路模式切换对整个系统的扰动,保证整个系统的闭环稳定性。以Shell公司重油分馏塔的多回路PID控制系统为例,将3个PID控制回路依次投用时,根据Gershgorin圆边界点进行控制参数的自整定,闭环系统仍能保持一定的控制性能,否则闭环系统将不稳定。     

基于改进差分进化算法-径向基神经网络的电热水浴串级控制系统研究

针对电热水浴装置温度控制中被控对象存在的大惯性、非线性、大延迟等特点,设计了一种基于改进差分进化(improved differential evolution, IDE)算法的径向基（radial basis function, RBF）神经网络串级控制系统。采用IDE算法对RBF神经网络的初始参数进行优化,采用优化后的RBF神经网络辨识主控制回路被控对象的Jacobian信息,进而实现主控制回路PID(proportional integration differentiation)控制器参数的在线调整。针对主控制回路控制器包含输出噪声,导致控制性能下降的问题,引入Kalman 滤波器对串级控制的主回路进行重新设计,控制对象的输出值经过Kalman 滤波算法处理后再返回闭环控制系统。以微化工领域常用电热水浴装置为对象,对IDE-RBF-PID-PI串级控制系统进行仿真实验,结果表明,IDE-RBF-PID-PI串级控制系统相较于常规串级控制,大大提高了控制性能,主控制回路引入的Kalman滤波算法有效消减控制系统的输出噪声,控制效果接近于无噪声的理想状态。     

氯乙酸生产结晶过程预热水温度的预测函数控制

针对氯乙酸生产结晶过程被控对象大滞后、大惯性的特点 ,采用先进的预测函数控制策略对其进行控制。仿真表明 ,模型参数变化时 ,在控制系统稳定性、鲁棒性和抗干扰性等综合性能方面 ,PFC比 PID具有明显的优越性。     

基于RBF神经网络陶瓷窑炉温度动态矩阵控制

陶瓷窑炉普遍具有纯滞后、大惯性、非线性、时变复杂等特点,其精确数学模型往往很难获取。针对这类系统,本文采用RBF神经网络建立被控对象模型,避免了常规控制算法建立对象精确数学模型的困难。应用动态矩阵预测算法实现对被控系统的预测控制。该控制方法具有很好的动、静态性能和强鲁棒性。以陶瓷窑炉温度为对象,与PID控制进行了比较;仿真结果证明了所提控制方法的有效性。     

钻井试验装置钻压伺服系统的控制器设计

利用电液伺服控制理论和xPC技术建立钻井试验装置钻压伺服系统,系统采用阀控缸实时电液力伺服控制。以钻力模型为控制对象设计一种由PID控制和模糊控制组成的复合控制器。带有前馈补偿的控制器中引入自调整修正因子,采用模糊切换来保证两种控制的平稳过渡,模糊切换的执行区域可通过设定其隶属函数的形状参数加以确定。仿真和实验结果表明:复合控制方法消除了模糊控制的稳态误差,提高了PID控制的响应速度,改善了电液力伺服系统的综合性能,对被控对象参数变化有很强的适应能力。     

采用辐射能信号的燃烧系统参数自调整模糊PI控制

针对火电厂燃烧过程中主蒸汽压力控制系统的大时滞、大惯性和非线性,采用能迅速反映燃料侧扰动的辐射能信号进行快速补偿,并设计一个参数自调整的模糊PI控制器作为主控制器。该控制器首先通过编写S函数来自动修正量化因子和比例因子,从而改善基本模糊控制器的性能;然后将模糊控制与PI控制相结合,以优化燃烧控制性能;仿真结果表明该方案显著提高了非线性、大时滞燃烧系统的控制品质。     

基于ADRC-PID的超临界机组主汽温控制系统设计

针对超临界机组动态特性复杂、变参数的运行方式、多变量的控制特点,提出二级过热汽温主调节器采用自抗扰控制器(ADRC),副调节器采用PID控制的设计方法,从而构成ADRC-PID方法来控制超临界主汽温.同时采用600MW超临界机组为被控对象进行试验研究.试验结果表明,用ADRC-PID控制主汽温对被控对象模型的不确定性和...     

模糊预测控制在循环水加药控制中的应用

针对模糊控制和预测控制的特点.提出了一种模糊预测控制方法,利用模糊逻辑实现被控对象模糊化,解决被控对象难以建立的问题;利用预测控制,根据系统当前信息预测出系统未来的变化趋势,解决被控对象滞后问题.以火电厂循环水加药控制为例,对该控制方法进行了仿真和实际应用研究.结果表明,该系统能有效地避免纯滞后环节在控制系统中带来的振荡现象,工程应用取得了理想的控制效果,为纯滞后、大惯性、模型不确定系统的控制提供了很好的解决方法.     

Adaptive type-2 fuzzy logic control of a bioreactor

Two adaptive type-2 fuzzy logic controllers with minimum number of rules are developed and compared by simulation for control of a bioreactor in which aerobic alcoholic fermentation for the growth of Saccharomyces cerevisiae takes place. The bioreactor model is characterized by nonlinearity and parameter uncertainty. The first adaptive fuzzy controller is a type-2 fuzzy-neuro-predictive controller (T2FNPC) that combines the capability of type-2 fuzzy logic to handle uncertainties, with the ability of predictive control to predict future plant performance making use of a neural network model of the nonlinear system. The second adaptive fuzzy controller is instead a self-tuning type-2 PI controller, where the output scaling factor is adjusted online by fuzzy rules according to the current trend of the controlled process. The performance of a type-2 fuzzy logic controller with 49 rules is used as reference.     

Application of genetic algorithm to self-organizing fuzzy controller in fed-batch culture ofScutellaria baicalensis G. plant cell

A self-organizing fuzzy controller is constructed for control of substrate concentration in fed-batch operation of a cell culture process. A genetic algorithm is used to generate fuzzy rules of the self-organizing fuzzy controller and to modify the universe of discourse automatically. The fuzzy controller is designed for the application ofScutellaria baicalensis G. plant cell culture process as a model system. A substrate feeding strategy of a two stage culture method to maximize flavone glycoside production in fed-batch culture ofScutellaria baicalensis G. plant cell is proposed based on structured model of growth and product synthesis. As a two stage culture, the feeding strategy consists of the first period at 22 g/L of glucose concentration to promote cell growth and the second period at 10 g/L of glucose concentration to promote flavone glycoside synthesis. The designed self-organizing fuzzy controller is applied to regulate the glucose concentration at a given set-point to increase flavone glycoside synthesis. The simulation results show that the proposed feeding strategy in a fed-batch culture enhances flavone glycoside production and the self-organizing fuzzy logic controller generated by genetic algorithm improves controller performance.     

复合控制器及其在连消中的应用

由于常规PID控制方式对非线性、大滞后对象难以进行有效的控制,模糊控制具有很好的动态特性,所以结合常规PID和模糊控制的优势设计了参数自调整Fuzzy-PID复合控制器。通过模糊推理实现参数自调整,以使控制器能够适应不同对象和对象的不同状态。采用模糊推理的方法完成两种控制方式的平稳过渡。对某制药厂连消温度的控制表明,该控制器可以大幅度提高控制精度和缩短系统响应时间,从而避免了染菌事故的发生,提高了发酵单位。     

模糊自整定PID控制器的设计与仿真

制浆碱回收蒸发工段第一步就是通过蒸汽将黑液进行浓缩,蒸汽的温度控制效果对后续燃烧工段和苛化工段起着举足轻重的作用。主管道蒸汽温度控制通常具有大惯性、大延迟、时变等特性,采用常规的PID控制难以获得满意的控制效果。为此,提出一种模糊自整定PID控制器的串级控制算法,该算法很大程度上提高了主蒸汽温度的控制品质。针对隶属度函数对模糊推理模型的精度影响和控制算法的特点编写了模糊规则,并且根据动态性要求的不同,分两种情况进行比较。仿真表明,模糊自整定PID串级控制算法在整体性能上要优于传统PID串级控制。     

Controlling of Proton Exchange Membrane Fuel Cell by Model Predictive Controller Based on ANFIS Model

Proton exchange fuel cell is one of the most promising new technologies in electrical energy production. Due to slow dynamic, nonlinearity and dependency of time changing variables of proton exchange membrane fuel cell (PEMFC), its control issue is a challenging problem. In this paper, model predictive controller (MPC) based on the adaptive neuro‐fuzzy interface model of the PEMFC is proposed to control the output voltage. First the adaptive neuro‐fuzzy interference system (ANFIS) model is identified to approximate the dynamic behavior of the PEMFC system with a set of data which are taken from a physical model of a 5 kW PEMFC setup plant. Then the branch‐and‐bound method and the greedy algorithm are used to solve the constrained optimization function of the predictive control problem. The results reveal that the ANFIS model can effectively approximate the dynamic behavior of the PEMFC and the predictive controller based on this model can successfully control the output and satisfy the constraints.     

Dynamic recurrent radial basis function network model predictive control of unstable nonlinear processes

A multistep model predictive control (MPC) strategy based on dynamically recurrent radial basis function networks (RBFNs) is proposed for single-input single-output (SISO) control of uncertain nonlinear processes. The control system consists of two automatically configured RBFNs, a trained network representing the plant model and a network with on-line learning to function as controller. The automatic configuration and learning of the networks is carried out by using a hierarchically self-organizing learning algorithm. This control strategy is structurally simple and computationally efficient since a single output node of each RBFN is configured to provide multistep predictions for plant output and controller. The performance of the proposed RBFNMPC strategy is evaluated by applying to two unstable nonlinear chemical processes, a chemical reactor and a biochemical reactor, and also a stable polymerization reactor. Further, the results of the RBFNMPC is compared with similar RBFN model based control strategies and also with well tuned PID/PI controller. The results show the better performance of the proposed RBFNMPC for the control of open-loop unstable nonlinear processes that exhibit multiple steady-state behavior.     

基于模糊自整定PID燃气锅炉仿真研究

针对燃气热水锅炉具有非线性、大惯性及多变量等特点,借助经验公式分析并用MATLAB系统辨识工具箱完成了系统数学模型的辨识,确定了燃气锅炉温度控制系统的模型结构可等效为一阶惯性加滞后环节,在此基础上确立了基于MATLAB的系统SIMULINK仿真框图,设计了模糊自整定PID控制器.通过模糊自整定PID与常规PID算法的仿真对比分析,得出模糊自整定PID不仅在调节时间、超调量等动态性能上的控制效果明显优于常规PID控制效果,而且增强了系统的鲁棒性和适应性,具有良好的动、静态性能.     

Multi-model predictive control of SCR flue gas denitrification system in coal-fired power plant based on kernel fuzzy c-means clustering and integrated model

A multi-model predictive control strategy based on kernel fuzzy c-means (KFCM) clustering and integrated model is proposed for the complex problem of rapid and accurate control of ammonia injection in selective catalytic reduction (SCR) denitrification systems of coal-fired power plants under a wide range of variable load conditions. First, the SCR data samples are clustered using the KFCM clustering algorithm, and the number of clusters is determined by introducing the Xie-Beni index. Second, the prediction model of the SCR denitrification system is established by an integrated modelling approach, and the sub-learners of the integrated model are the genetic algorithm optimized back propagation (GA-BP) neural network model and the least squares support vector machine (LSSVM) model. Third, a multi-model prediction controller based on the particle swarm optimization (PSO) algorithm and the integrated model is designed and developed. To ensure the stability of the system, a model-switching strategy based on the minimum Euclidean distance is proposed. Finally, simulation verification and industrial field application verification are fulfilled by comparing with proportion integral differential (PID) control and single model predictive control (MPC). The results show that the multi-model predictive control method proposed in this paper can obtain higher control accuracy and better control stability and meet the control requirements for the long-term operation of the SCR denitrification system.     

用免疫量子粒子群算法优化控制决策表

提出用免疫量子粒子群算法优化控制决策表,使控制决策表的参数整定简单易行.其核心思想是将控制决策表作为算法中的粒子,以迭代搜索的方式寻找全局最优粒子.该算法的全局寻优能力强,计算机实现简单,可调参数少.模糊控制器和控制决策表的优化设计在SCON-2000模糊控制平台进行了工程化实现,并对水箱液位进行模糊控制.从对比结果中可以看出,优化了控制决策表以后,系统响应更快,精度更高,抗扰动能力更强.这表明了该算法在模糊控制器参数优化中的可行性.     

基于自组织模糊神经网络的污水处理过程溶解氧控制

针对活性污泥污水处理过程溶解氧浓度控制问题,提出一种基于自组织模糊神经网络（SOFNN）的控制方法。该神经网络控制器依据激活强度和互信息理论在线动态增长和修剪规则层神经元,以满足实际工况的动态变化。同时,采用梯度下降算法在线优化隶属函数层中心、宽度和输出权值,以保证SOFNN的收敛性。进一步通过Lyapunov稳定性理论对SOFNN学习率进行分析,给出控制系统稳定性证明。最后在国际基准仿真平台BSM1上进行实验验证。实验结果显示,与PID、模糊逻辑控制（FLC）和固定结构FNN等控制策略相比,SOFNN在跟踪精度、控制平稳性和自适应能力上更具有优势。