基于定量反馈理论的多变量解耦预测控制及其在CSTR化学过程中的应用

1 INTRODUCTION Many multi-input and multi-output (MIMO) sys- tems worldwide are regarded as linear invariants, but there are still some difficulties in controlling these systems. The challenges arise from the need to achieve both robust stability and control performance when the plants to be controlled are highly uncer- tain[1―3]. Quantitative feedback theory (QFT) is a fre- quency domain design technique[4], which is perhaps the only known method that deals with highly uncer- tain pla…     

基于模型预测控制的部分分散控制系统设计(英文)

An expansion procedure to design partially decentralized controllers via model predictive control is proposed in this paper. Partially decentralized control is a control structure that lies between a fully decentralized structure and a fully centralized one, and has the advantage of achieving comparable performance as a fully centralized controller but with simpler structure. The proposed method follows the expansion method proposed in a previous paper where internal model control (IMC) was used to design controllers for non-square subsystems. The method requires computing the pseudo-inverse of a non-square matrix via pseudo-inverse factors. Instead, the proposed method uses dynamic matrix control (DMC) to design PID controllers for non-square subsystems without using additional factors. The effectiveness of the proposed method is demonstrated on several chemical examples. Simulation results show that the proposed method is simple and can achieve better performance.     

模糊自适应遗传算法及其在化工中的应用

Considering that the performance of a genetic algorithm (GA) is affected by many factors and their rela-tionships are complex and hard to be described,a novel fuzzy-based adaptive genetic algorithm (FAGA) combined a new artificial immune system with fuzzy system theory is proposed due to the fact fuzzy theory can describe high complex problems.In FAGA,immune theory is used to improve the performance of selection operation.And,crossover probability and mutation probability are adjusted dynamically by fuzzy inferences,which are developed according to the heuristic fuzzy relationship between algorithm performances and control parameters.The experi-ments show that FAGA can efficiently overcome shortcomings of GA,i.e.,premature and slow,and obtain better results than two typical fuzzy GAs.Finally,FAGA was used for the parameters estimation of reaction kinetics model and the satisfactory result was obtained.     

基于区间二型模糊推理的双容水箱液位控制系统研究(英文)

For dealing with large static error due to poor immunity of the traditional fuzzy control, a novel interval type-2 fuzzy control system is proposed. By extending the typical membership functions to interval type-2 membership functions, the proposed control system can efficiently reduce the uncertain disturbance from real environment without increasing the design complexity. The simulation results on the water tank level control system showed that the proposed method succeeded in better static and dynamic control with stronger robust performance than the traditional fuzzy control method.     

基于改进自适应模糊推理系统的半导体制造系统瓶颈设备预测方法(英文)

Semiconductor manufacturing (SM) system is one of the most complicated hybrid processes involved continuously variable dynamical systems and discrete event dynamical systems. The optimization and scheduling of semiconductor fabrication has long been a hot research direction in automation. Bottleneck is the key factor to a SM system, which seriously influences the throughput rate, cycle time, time-delivery rate, etc. Efficient prediction for the bottleneck of a SM system provides the best support for the consequent scheduling. Because categorical data (product types, releasing strategies) and numerical data (work in process, processing time, utilization rate, buffer length, etc.) have significant effect on bottleneck, an improved adaptive network-based fuzzy inference system (ANFIS) was adopted in this study to predict bottleneck since conventional neural network-based methods accommodate only numerical inputs. In this improved ANFIS, the contribution of categorical inputs to firing strength is reflected through a transformation matrix. In order to tackle high-dimensional inputs, reduce the number of fuzzy rules and obtain high prediction accuracy, a fuzzy c-means method combining binary tree linear division method was applied to identify the initial structure of fuzzy inference system. According to the experimental results, the main-bottleneck and sub-bottleneck of SM system can be predicted accurately with the proposed method.     

延迟焦化加热炉的自适应状态反馈预测控制和专家控制

An adaptive state feedback predictive control （SFPC） scheme and an expert control scheme are presented and applied to the temperature control of a 1200 kt·a^-1 delayed coking furnace, which is the key equipment for the delayed coking process. Adaptive SFPC is used to improve the performance of temperature control in normal operation. A simplified nonlinear model on the basis of first principles of the furnace is developed to obtain a state space model by linearization. Taking advantage of the nonlinear model, an online model adapting method is presented to accommodate the dynamic change of process characteristics because of tube coking and load changes. To compensate the large inverse response of outlet temperature resulting from the sudden increase of injected steam of a particular velocity to tubes, a monitoring method and an expert control scheme based on heat balance calculation are proposed. Industrial implementation shows the effectiveness and feasibility of the proposed control strategy.     

基于改进DOB的矿物研磨过程最优运行先进反馈控制(英文)

Advanced feedback control for optimal operation of mineral grinding process is usually based on the model predictive control (MPC) dynamic optimization. Since the MPC does not handle disturbances directly by controller design, it cannot achieve satisfactory effects in controlling complex grinding processes in the presence of strong disturbances and large uncertainties. In this paper, an improved disturbance observer (DOB) based MPC advanced feedback control is proposed to control the multivariable grinding operation. The improved DOB is based on the optimal achievable H 2 performance and can deal with disturbance observation for the nonminimum-phase delay systems. In this DOB-MPC advanced feedback control, the higher-level optimizer computes the optimal operation points by maximize the profit function and passes them to the MPC level. The MPC acts as a presetting controller and is employed to generate proper pre-setpoint for the lower-level basic feedback control system. The DOB acts as a compensator and improves the operation performance by dynamically compensating the setpoints for the basic control system according to the observed various disturbances and plant uncertainties. Several simulations are performed to demonstrate the proposed control method for grinding process operation.     

一种对预测控制系统经济性能评估的实用方法及其工业应用

In this article, an approach for economic performance assessment of model predictive control (MPC) system is presented. The method builds on steady-state economic optimization techniques and uses the linear quadratic Gaussian (LQG) benchmark other than conventional minimum variance control (MVC) to estimate the potential of reduction in variance. The LQG control is a more practical performance benchmark compared to MVC for performance assessment since it considers input variance and output variance, and it thus provides a desired basis for determining the theoretical maximum economic benefit potential arising from variability reduction. Combining the LQG benchmark directly with benefit potential of MPC control system, both the economic benefit and the op-timal operation condition can be obtained by solving the economic optimization problem. The proposed algorithm is illustrated by simulated example as well as application to economic performance assessment of an industrial model predictive control system.     

一类连续/间歇过程的迭代学习模型预测控制

An iterative learning model predictive control (ILMPC) technique is applied to a class of continuous/batch processes. Such processes are characterized by the operations of batch processes generating periodic strong disturbances to the continuous processes and traditional regulatory controllers are unable to eliminate these periodic disturbances. ILMPC integrates the feature of iterative learning control (ILC) handling repetitive signal and the flexibility of model predictive control (MPC). By on-line monitoring the operation status of batch processes, an event-driven iterative learning algorithm for batch repetitive disturbances is initiated and the soft constraints are adjusted timely as the feasible region is away from the desired operating zone. The results of an industrial appli-cation show that the proposed ILMPC method is effective for a class of continuous/batch processes.     

集散控制系统在醇酸树脂生产中的应用

以郑州油漆厂迁建改造工程高温树脂车间的集散控制系统(DCS)为例,介绍了DCS在醇酸树脂生产中的液体配料、溶剂计量、温度控制等生产工艺中的应用及其注意事项。     

小波基预测函数控制在陶瓷烧成温度控制中的应用

本文针对陶瓷烧成窑炉具有大滞后、非线性、不确定性等特点,提出一种基于小波基函数的陶瓷烧成温度的预测函数控制方法。给出陶瓷烧成温度预测函数控制的基本原理;利用小波的多尺度分析和紧支局部特性,选取小波函数作为基函数,根据逼近要求灵活设置小波基函数的个数及位置分布,在保证系统整体优化性能的同时又兼顾了局部重点要求;理论分析和仿真表明,该控制方法与普通PFC方法相比,在动态特性、控制精度、抗干扰等方面具有明显的改善作用。     

新型模糊预测PID控制在pH中和过程中的应用

利用自适应学习算法及模糊推理方法在线修正pH过程所得的局部线性化模型,同时基于广义预测控制(GPC)的思想和离散PID算法的相互关系,提出了一种以预测控制这类先进控制方法为思想,以经典PID控制为实现的新型控制器。其中,控制器的参数通过GPC与PID的相互关系递推计算得到。仿真研究表明本文所提出方法的可行性和有效性。     

模糊预测函数控制在水泥回转窑分解炉温控系统中的应用研究

通过对水泥回转窑系统中预分解炉温度控制回路的特性分析,提出一种基于模糊模型的增量型预测函数控制算法,根据生料流量的波动来修正相应的预测函数控制律,并以Hoenywell公司的Plantscape集散控制系统为平台,开发了实时控制软件,实际应用表明对存在大迟后和参数时变的水泥回转窑系统,该算法能达到良好的控制效果。     

基于FGPC的单元机组协调控制系统研究

针对单元机组的大迟延、强耦合、参数时变且不确定性的特点,将T—s模糊模型引入预测控制中,作为预测模型。首先,用改进的模糊c一均值聚类算法和随机牛顿法辨识得到非线性系统的T-s模型;然后基于线性化后的系统模型设计模糊广义预测控制器,并对非线性对象进行在线控制。仿真结果表明：FGPC对于时变的非线性系统具有很好的控制效果。     

加热炉炉温的模糊广义预测控制仿真研究

针对加热炉炉温的大惯性、大滞后及非线性等特点,提出一种基于T-S模糊模型的模糊广义预测控制策略。T-S模糊模型的前件和后件参数分别采用粒子群优化的模糊C-均值算法和递推最小二乘法辨识,根据输入变量更新模型隶属度并将T-S模糊模型等价转换为线性模型,以此作为预测模型应用于广义预测控制。仿真结果表明:该方法在不同工况下均具有较短的调节时间,在扰动作用下有很强的鲁棒性。     

基于支持向量机的精馏塔模糊预测控制算法研究

利用模糊预测控制,依据支持向量机对模糊预测控制方法中的预测模型进行训练,以精馏塔的塔顶回流控制为例,通过仿真研究了支持向量机作为预测模型训练方法在模糊预测控制中的应用,得到了较好的控制效果。利用支持向量机与模糊预测控制结合,进一步发挥了信息处理方法在过程控制中的应用。     

基于Matlab/Simulink的气体分离装置多变量预测控制

依据丙烯精馏塔的工艺流程,介绍丙烯精馏塔机理模型和辨识模型的构建方法,并通过系统辨识方法得到了被控对象的多输入多输出模型。介绍预测控制及其算法的基本原理,通过编写基于状态空间模型的单步预测控制算法,对丙烯精馏塔进行多变量预测控制仿真,比较了预测控制算法对可测干扰,不可测干扰的抑制作用。     

基于控制规则调整的自适应模糊控制器

工业对象大多是具有非线性、大时滞、高阶次的复杂对象 ,常规的控制方法往往难以适应这些对象的变化。文中提出了一种自适应模糊控制器 ,它能在控制过程中不断调整和修改控制规则 ,以适应对象和环境的变化。对控制方法与常规PID控制、Smith预估控制、基本模糊控制进行了仿真比较。仿真曲线表明 ,自适应模糊控制器的控制性能明显好于其它 3种控制方法     

铅锌烧结过程烧穿点的模糊预测控制方法

针对具有时滞、非线性、不确定等特性的铅锌密闭鼓风烧结过程,提出一种烧穿点的模糊预测控制方法。首先,基于台车速度和烧穿点之间的分段线性化关系,建立烧穿点的模糊T-S模型。然后,采用动态线性化方法获得烧穿点的全局模型,并在此基础上进行模型预测控制器设计,以实现烧穿点的模糊预测控制。最后,采用烧结生产历史数据,对模糊预测控制方法进行了仿真和实验研究。结果表明：模糊T-S模型能有效抑制垂直燃烧速度不确定性的影响,具有较高的辨识精度,能够满足工业现场生产指导的需要;此外,烧穿点的模糊预测控制方法比单纯的模糊控制调节时间短、超调量小,能快速有效地响应烧穿点的变化,具有一定的应用价值。     

典型大时变时滞系统神经网络模糊PID控制及应用

针对典型大时变时滞系统,设计了一种基于神经网络的模糊PID控制器.该控制器综合模糊逻辑、神经网络与PID调节的各自优点,既具有模糊控制简单和有效的非线性控制作用,又具有神经网络的学习和适应能力,同时还具备PID控制的广泛适应性.该控制器能实现系统参数大范围失配情况下的闭环鲁棒稳定,并使闭环系统达到设定值无静差跟踪及满意的动态性能.