大型系统控制结构设计的交互分析和分解原理简

Industrial processes are mostly large-scale systems with high order. They use fully centralized control strategy, the parameters of which are difficult to tune. In the design of large-scale systems, the decomposition according to the interaction between input and output variables is the first step and the basis for the selection of control structure. In this paper, the decomposition principle of processes in large-scale systems is proposed for the design of control structure. A new variable pairing method is presented, co_nsidering the steady-state information anddynamic response of large-scale system. By selecting threshold values, the related matrix can be transformed into the adjoining matrixes, which directly measure the couple among different loops. The optimal number of controllers can be obtained after decomposing the large-scale system. A practical example is used to demonstrate the validity and feasibility of the proposed interaction decomposition principle in process large-scale systems.     

基于ARX-PLS解耦框架下的多回路约束迭代模型预测控制方法(英文)

A multi-loop constrained model predictive control scheme based on autoregressive exogenous-partial least squares （ARX-PLS） framework is proposed to tackle the high dimension, coupled and constraints problems in industry processes due to safety limitation, environmental regulations, consumer specifications and physical restric-tion. ARX-PLS decoupling character enables to turn the multivariable model predictive control （MPC） controller design in original space into the multi-loop single input single output （SISO） MPC controllers design in latent space. An idea of iterative method is applied to decouple the constraints latent variables in PLS framework and recursive least square is introduced to identify ARX-PLS model. This algorithm is applied to a non-square simulation system and a stirred reactor for ethylene polymerizations comparing with adaptive internal model control （IMC） method based on ARX-PLS framework. Its application has shown that this method outperforms adaptive IMC method based on ARX-PLS framework to some extent.     

计量泵行程调节的鲁棒非脆弱PID控制器设计

This job focuses on the stroke regulation of a class of high-precision metering pumps.A parametertuning method of robust non-fragile PID(proportional-integral-derivative)controllers is proposed with the assumption that a PID controller has additive gain perturbations.An H-infinite robust PID controller can be obtained by solving a linear matrix inequality.This approach can guarantee that the closed-loop control systems is asymptotically stable and the H-infinite norm of the transfer function from the disturbance to the output of a controlled system is less than a given constant to attenuate disturbances.The simulation case shows that the control performance of the proposed strategy is significantly better than the traditional PID approach in the situation with perturbations of controller parameters.     

An LMI Method to Robust Iterative Learning Fault-tolerant Guaranteed Cost Control for Batch Processes

Based on an equivalent two-dimensional Fornasini-Marchsini model for a batch process in industry, a closed-loop robust iterative learning fault-tolerant guaranteed cost control scheme is proposed for batch processes with actuator failures. This paper introduces relevant concepts of the fault-tolerant guaranteed cost control and formulates the robust iterative learning reliable guaranteed cost controller （ILRGCC）. A significant advantage is that the proposed ILRGCC design method can be used for on-line optimization against batch-to-batch process uncertainties to realize robust tracking of set-point trajectory in time and batch-to-batch sequences. For the convenience of implementation, only measured output errors of current and previous cycles are used to design a synthetic controller for iterative learning control, consisting of dynamic output feedback plus feed-forward control. The proposed controller can not only guarantee the closed-loop convergency along time and cycle sequences but also satisfy the H∞ performance level and a cost function with upper bounds for all admissible uncertainties and any actuator failures. Sufficient conditions for the controller solution are derived in terms of linear matrix inequalities （LMIs）, and design procedures, which formulate a convex optimization problem with LMI constraints, are presented. An example of injection molding is given to illustrate the effectiveness and advantages of the ILRGCC design approach.     

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.     

通过数学规划方法设计具有混合结构的水回用网络

A new design method for a water-reusing network, with a hybrid structure, to reduce the complexity of the network and to minimize freshwater consumption, is proposed. The unique feature of the methodology proposed .in this article is to control the complexity of the water network by regulation of the control number in a water-reusing system. It combines the advantages of a conventional water-reusing network and a water-reusing net work with internal water mains. To illustrate the proposed method, a single contaminant system and a multiple contaminant system serve as examples of the problems.     

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.     

设计余量对运行优化和化学过程控制性能的影响简

Operation optimization is an effective method to explore potential economic benefits for existing plants. The m.aximum potential benefit from operationoptimization is determined by the distances between current operating point and process constraints, which is related to the margins of design variables. Because of various ciisturbances in chemical processes, some distances must be reserved for fluctuations of process variables and the optimum operating point is not on some process constraints. Thus the benefit of steady-state optimization can not be fully achied（ed while that of dynamic optimization can be really achieved. In this study, the steady-state optimizationand dynamic optimization are used, and the potential benefit-is divided into achievable benefit for profit and unachievable benefit for control. The fluid catalytic cracking unit （FCCU） is used for case study. With the analysis on how the margins of design variables influence the economic benefit and control performance, the bottlenecks of process design are found and appropriate control structure can be selected.     

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.     

ROBUST DECENTRALIZED CONTROL OF NON-SQUARE SYSTEMS

The independent design procedure for robust decentralized controllers proposed by Hovd and Skogestad (1994) is extended to non-square systems with more inputs than outputs. The proposed design method is applied to a non-square mixing tank example. The results further justify that non-square systems should be controlled in thier original non-square form instead of squaring them by adding or eliminating variables.     

工业过程多变量系统的辅助常规控制设计

利用PID控制器对多变量系统进行控制,主要采取分散常规控制或解耦控制的策略。对于存在耦合的系统,分散常规控制无法消除各变量间的相互扰动,而直接设计动态解耦矩阵有时候会存在困难,静态解耦的效果却并不明显。针对已设计好分散常规控制器的方系统,以解耦为目标和原则,设计多变量系统中分散常规控制方案剩余配对的控制器作为辅助控制器,以频域近似的方法整定其PID参数,实现多输入多输出的控制结构,降低了各变量的相互耦合作用,提高了控制性能。通过对Shell公司的典型控制问题的辅助控制器设计与仿真实验,取得了比原有分散常规控制与静态解耦控制更好的控制效果,验证了控制策略的可行性与有效性。     

多变量多时滞非方系统的解耦内模控制

多变量复杂控制系统不仅具有多耦合和多时滞性,还具有结构上的复杂性,即输入输出不等,传递函数为奇异矩阵。传统的多变量内模控制是基于对非奇异对象求逆来进行的,因此很难解决这类问题。针对该情况引入矩阵论中的广义逆概念,通过求对象的广义逆矩阵来设计解耦内模控制器,打破了内模控制只能对方系统进行控制的局限性,并利用泰勒近似很好地解决了多滞后的问题,最后通过设计特殊形式的滤波器,不仅能够消除由纯滞后近似引入的不稳定极点,保证系统的稳定性,且能够保证系统的正则性。仿真结果表明,该方法不仅跟踪迅速,且继承了内模控制的无余差和强鲁棒性,动态解耦效果良好,仅对时滞变化较为敏感。由于系统基于内模控制设计,故模型匹配度越高,系统响应越好。     

化工过程非方瘦系统的串级控制系统结构设计

化工过程一般为多变量系统，其中输出变量个数多于输入变量个数的非方多变量系统称为瘦系统，现有的非方系统控制结构设计一般采用方形化处理方法，只能形成一个输入变量与一个输出变量配对的单回路控制，要么作为工艺控制指标的重要变量不能成为被控变量，要么次要变量未纳入反馈控制，无法满足控制要求。本文介绍了非方系统的平均频域相对增益阵，对瘦系统进行了变量配对分析，提出了一种瘦系统串级控制系统结构设计方法。这种方法在不添加输入变量的同时有效利用了所有的输出变量反馈，使系统反馈信息更完备，构造了重要变量与次要变量结合的串级控制系统。最后通过实例分析说明了瘦系统的串级控制系统结构设计方法不仅能够得到合理的变量配对，而且系统控制性能良好，尤其在进行干扰抑制的过程中体现出了快速性和高效性。     

Design of decentralized proportional-plus-integral controllers for multivariable systems

This paper presents a simple method for the design of decentralize proportional-plus-integral controllers for multivariable linear systems where the controllers act directly on the available systems outputs. The proposed design scheme, which includes local as well as hierarchical types of control systems, involves the solution of one nonlinear Riccati matrix equation and one linear Liapunov matrix equation. The practicality and good performance of control systems developed, using this approach, is demonstrated on the mathematical model of a plate absorption column.     

蒸馏塔分散PID控制器整定研究

结合一种双输入双输出 (TITO)分散PID控制器的设计方法 ,并将其应用于乙醇—水的蒸馏分离过程控制中。该法根据相应的收敛性算法 ,通过一系列继电闭环实验辨识预期临界点 (DCP) ,利用T L整定公式获取分散PID控制器参数。通过仿真对比试验 ,表明所设计的控制系统具有满意的动态性能     

基于PID对角优势补偿阵的过程多变量控制系统设计

化工过程一般为多变量系统,输入输出变量之间往往存在耦合作用,常规分散PID控制系统难以保证控制质量。为了削弱多变量系统的耦合作用,基于对角优势的设计准则,通过在若干频率点加权优化,设计PID动态预补偿阵。然后利用正Nyquist阵列设计法对补偿后的系统设计控制器,基于对角优势系统的Nyquist稳定判据,通过绘制优势度曲线和Gershgorin带判断系统的优势程度,初步确定反馈矩阵的稳定参数范围,再按照单输入单输出系统设计动态补偿器,使得系统满足动态控制品质要求。最后通过示例说明,该方法设计的集中控制系统与分散控制相比,控制性能具有一定的优势,且方法简便,容易实现。     

Distributed model predictive control of switched nonlinear systems with scheduled mode transitions

A method for the design of distributed model predictive control (DMPC) systems for a class of switched nonlinear systems for which the mode transitions take place according to a prescribed switching schedule is presented. Under appropriate stabilizability assumptions on the existence of a set of feedback controllers that can stabilize the closed‐loop switched, nonlinear system, a cooperative DMPC architecture using Lyapunov‐based model predictive control (MPC) in which the distributed controllers carry out their calculations in parallel and communicate in an iterative fashion to compute their control actions is designed. The proposed DMPC design is applied to a nonlinear chemical process network with scheduled mode transitions and its performance and computational efficiency properties in comparison to a centralized MPC architecture are evaluated through simulations. © 2013 American Institute of Chemical Engineers AIChE J, 59:860‐871, 2013