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.     

模糊自适应控制算法在配料系统中的应用

介绍模糊自适应控制算法在配料系统中的应用。模糊自适应控制器以误差E和误差变化量Ee作为输入，可以满足不同时刻E和Ee对PID参数自整定的要求；利用模糊控制规则在线对PID参数进行修改，消除了生产过程中人为因素的影响，提高了产品质量的均一性和稳定性。     

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。     

离线辨识和在线辨识相结合的广义预测控制算法在固定床反应器温度控制中的应用

An improved generalized predictive control algorithm is presented in this paper by incorporating offline identification into onlie identification.Unlike the existing generalized predictive control algorithms.the proposed approach divides parameters of a predictive model into the time invariant and time-varying ones,which are treated respectively by offline and onlie identification algorithms.Therefore,both the reliability and accuracy of the predictive model are improved,Two simulation examples of control of a fixed bed reactor show that this new algorithm is not only reliable and stable in the case of uncertainties and abnormal distrubances,but also adaptable to slow time varying processes.     

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。     

带约束输入的多变量广义预测解耦控制及其在化工过程中的应用

A constrained decoupling （generalized predictive control） GPC algorithm is proposed for MIMO （malti-input multi-output） system. This algorithm takes account of all constraints of inputs and their increments. By solving matrix equations, the multi-step predictive decoupling controllers are realized. This algorithm need not solve Diophantine functions, and weakens the cross-coupling of the variables. At last the simulation results demonstrate the effectiveness of this proposed strategy.     

多频多变量预测控制系统的IMC结构及其改进算法

Multirate multivariable predictive control system with the sampling mechanism that adjusts the plant inputs only once but detects the plant outputs several times during a period is examined. The IMC structure of the system is derived, and its robust stability and zero steady state error chaxacteristics axe analyzed. A new controlal gorithm is developed by adding the variation of the outputs to the index performance. The simulation results show that the method is effective and has zeros steady-state error.     

模糊自寻优控制系统及工业应用

针对复杂工业过程中普遍存在的优化控制量无法直接检测问题,提出了一个运用模糊自寻优控制的解决方法,该方法将控制系统分为由模糊自适应PID控制构成级与由模糊自寻优和模糊异常两个推理子系统构成的监控极,着重介绍了模糊自寻优推理子系统的构成方法,该系统已应用于中储式制粉系统,取得了显著的经济效益。     

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。     

状态多重时滞系统的预测控制算法及应用

针对石油化工过程状态多重时滞的特点，提出多重时滞系统的预测控制算法，给出基于本算法的闭环系统稳定性条件。仿真和应用结果表明，该算法提高了闭环系统的鲁棒性，是有效和可行的。     

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。