一种基于模型的多变量协调控制技术

给出的基于模型的多变量协调控制技术将被控变量的动态控制问题与操作变量的静态优化问题有机地结合起来.对于操作变量多于被控变量的“胖”系统,本控制技术在保证对关键变量实施定点控制的同时,综合考虑操作变量的控制速度、操作成本等经济因素,尽可能将一些操作变量协调至理想操作区内,达到优化(节能、降耗)的目的.对于操作变量少于被控变量的“瘦”系统,则据被控变量的重要程度对其施加不同的控制.给出了控制器的唯一解条件、稳态无差和稳定性性质.针对催化裂化主分馏塔给出的仿真结果说明本协调控制有良好的控制性能,适合生产过程的需要.     

ARTIFICIAL NEURAL NETWORKS: PRINCIPLE AND APPLICATION TO MODEL BASED CONTROL OF DRYING SYSTEMS - A REVIEW

Abstract

This paper reviews the developments in the model based control of drying systems using Artificial Neural Networks (ANNs). Survey of current research works reveals the growing interest in the application of ANN in modeling and control of non-linear, dynamic and time-variant systems. Over 115 articles published in this area are reviewed. All landmark papers are systematically classified in chronological order, in three distinct categories; namely, conventional feedback controllers, model based controllers using conventional methods and model based controllers using ANN for drying process. The principles of ANN are presented in detail. The problems and issues of the drying system and the features of various ANN models are dealt with upto-date. ANN based controllers lead to smoother controller outputs, which would increase actuator life. The paper concludes with suggestions for improving the existing modeling techniques as applied to predicting the performance characteristics of dryers. The hybridization techniques, namely, neural with fuzzy logic and genetic algorithms, presented, provide, directions for pursuing further research for the implementation of appropriate control strategies. The authors opine that the information presented here would be highly beneficial for pursuing research in modeling and control of drying process using ANN     

ARTIFICIAL NEURAL NETWORKS: PRINCIPLE AND APPLICATION TO MODEL BASED CONTROL OF DRYING SYSTEMS - A REVIEW

This paper reviews the developments in the model based control of drying systems using Artificial Neural Networks (ANNs). Survey of current research works reveals the growing interest in the application of ANN in modeling and control of non-linear, dynamic and time-variant systems. Over 115 articles published in this area are reviewed. All landmark papers are systematically classified in chronological order, in three distinct categories; namely, conventional feedback controllers, model based controllers using conventional methods and model based controllers using ANN for drying process. The principles of ANN are presented in detail. The problems and issues of the drying system and the features of various ANN models are dealt with upto-date. ANN based controllers lead to smoother controller outputs, which would increase actuator life. The paper concludes with suggestions for improving the existing modeling techniques as applied to predicting the performance characteristics of dryers. The hybridization techniques, namely, neural with fuzzy logic and genetic algorithms, presented, provide, directions for pursuing further research for the implementation of appropriate control strategies. The authors opine that the information presented here would be highly beneficial for pursuing research in modeling and control of drying process using ANN     

TOWARDS A COMPREHENSIVE MODEL BASED CONTROL OF MILK DRYING PROCESSES

ABSTRACT

Milk powder manufacture is carried out on large scale and to exacting standards. To date, process control has been largely empirical. Recent advances in the understanding of process mechanisms and product properties is allowing model-based process control strategies to be developed. This review briefly describes the manufacture of milk powder and discusses in detail milk concentrate viscosity and atomization, particle size distribution, the drying mechanism and protein denaturation, in relation to the development of such strategies.     

CONTROL OF A PULSED LIQUID-LIQUID EXTRACTION COLUMN BASED ON A MULTILEVEL SYSTEM OF AUTOMATA

This paper presents the real-time application of the learning control theory to the control of a chemical pilot plant: a pulsed liquid-liquid extraction column.

The behaviour of an agitated liquid-liquid extraction column can be related to random mechanisms such as the phenomena of droplets breakage and coalescence. Previous studies on hydrodynamic and mass transfer aspects showed that a pulsed liquid-liquid extraction column had an optimal behaviour for operating conditions close to flooding. These results led to choose the following strategy to control the column in its optimal behaviour zone:

- the measure of the conductivity of the liquid medium below the distributor which gives a good information about flooding, is the controlled variable

-the pulse frequency is the control action.

The learning control algorithm is based on a multilevel system of automata which operates in a random environment. By means of an evaluation unit of the performances of the column which generates either penalty (inaction) or reward on the basis of heuristic rules, the automaton chooses a value of the pulse frequency. This approach is essentially connected to artificial intelligence in so far as human knowledge on the plant is included in these rules.

This algorithm has been implemented on a microcomputer for control purposes. The experimental results presented show the good performances of the approach.     

SYSTEMATIC DERIVATIONS OF MODEL PREDICTIVE CONTROL BASED ON ARTIFICIAL NEURAL NETWORK

This article presents systematic derivations of setting up a nonlinear model predictive control based on the artifical neural network. Unlike most research in the past, the control law is mathematically developed in detail so that the performance of the ANN-based controller can be improved. In this paper, a three-layer feedforward neural network with hyperbolic tangent functions in the hidden layer and with a linear function in the output layer is used. The two-stage scheme including pseudo Gauss-Newton and least squares is proposed for training ANN. This training method is better than the traditional algorithm in terms of training speed. The Levenberg-Marquardt approximation is also utilized for the minimum of the predictive control criterion. Two typical chemical processes are simulated and the ANN model predictive control applications can reach fairly good results.     

APPLICATION OF ADAPTIVE PID CONTROL WITH GENETIC ALGORITHM TO A POLYMERIZATION REACTOR

This article describes the application of adaptive PID control with genetic algorithm (GA) to a jacketed batch polymerization reactor. This method was used to keep the polymerization reactor temperature at the desired optimal path, which was determined by the Hamiltonian maximum principle method. The reactor was simulated and the model equations of this jacketed polymerization reactor were solved by means of Runge-Kutta-Felthberg methods. A genetic algorithm can be a good solution for finding the optimum PID parameters because unlike other techniques it does not impose many limitations and it is simple. In this research, suitability of these parameters was checked by the integral absolute error (IAE) criterion. The control parameters in the PID algorithm were changed with time during the control of a polymerization reactor. It was seen that the genetic algorithm was able to tune the PID controller used in this system in terms of higher robustness and reliability by changing the parameters continuously.     

NONLINEAR HEAT EXCHANGER CONTROL THROUGH THE USE OF PARTIALLY LINEARIZED CONTROL VARIABLES

The use of partial linearization by nonlinear state variable feedback has been proposed as a means of reducing the detrimental effects of system nonlinearities upon the performance of linear control schemes used with nonlinear systems. In this paper a set of generalized transformed variables are derived for a single pass shell and tube heat exchanger using this technique. The implementation of these generalized transformed variables, which reduce the apparent nonlinear behavior of single pass heat exchangers, eliminates the need to rederive a nonlinear transformation for each heat exchanger controller design. As shown by open loop transient behavior of the system, the transformed variables reduce the nonlinear characteristics of the system response. The closed loop performance of the heat exchanger system has been evaluated for both servo and regulator control, and the effect of model error upon the robustness of the closed loop controller performance has been investigated.     

ADAPTIVE FUZZY MODEL BASED PREDICTIVE CONTROL OF AN EXOTHERMIC BATCH CHEMICAL REACTOR*

An adaptive fuzzy model based predictive control (AFMBPC) approach is presented to track the desired temperature trajectories in an exothermic batch chemical reactor. The AFMBPC incorporates an adaptive fuzzy modeling framework into a model based predictive control scheme to derive analytical controller output. This approach has the flexibility to cope with different fuzzy model structures whose choice also lead to improve the controller performance. In this approach, adaptation of fuzzy models using dynamic process information is carried out to build a predictive controller, thus eliminating the determination of a predefined fixed fuzzy model based on various sets of known input-output relations. The performance of the AFMBPC is evaluated by comparing to a fixed fuzzy model based predictive controller (FFMBPC) and a conventional PID controller. The results show the better suitability of AFMBPC for the control of highly nonlinear and time varying batch chemical reactors.     

非线性系统的一种智能PID控制算法及应用

讨论了一种基于专家知识的智能 Ｐ Ｉ Ｄ控制算法。根据专家知识与现场经验,实时修正 Ｐ Ｉ Ｄ 参数,并根据系统响应的在线识别进行知识调整。两个具有明显非线性时变特性对象的仿真结果表明,该算法具有良好的控制特性与鲁棒性,可望被改进为一种实时在线的计算机控制策略而加以实施。     

A NEW INTERNAL MODEL CONTROL SCHEME WITH SIMPLIFIED DESIGN AND IMPLEMENTATION

This paper proposes a new internal model control scheme. The proposed scheme does not require an explicit transfer function model of the process. The design procedure is simple and the parameters of the controller are given in analytic formula. The resulting controller is of fixed-order form, which makes implementation easy. Simulation shows that the proposed scheme gives consistent and satisfactory performance for a large class of processes.     

MULTIVARIABLE CONTROL OF CATALYTIC CRACKING PROCESSES

Simple, explicit and physically intuitive Feedforward and Feedback control policies are designed for Fluidized Catalytic Cracking Processes. The Feedforward (FF) control algorithm compensates for changes in the feed rate and feed coking tendency by the use of the air flow and catalyst circulation rates as control variables to maintain the conversion and the reactor temperature at fixed levels. Through steady state and dynamic simulations the FF controller is shown to be very effective. To improve the dynamic response of the process and to account for the process/model mismatch a feedback (FB) controller is also designed to complement the FF action. The FB action is designed by use of the transformation related to the physical modes which correspond to the extensive variables of the process. It is shown that the required control structure consists of two loops. One uses the air flow rate to control the total sensible heat content of the reactor and regenerator solid phases. The other loop controls the regenerator enthalpy by changes in the catalyst circulation rate. The air flow rate controller includes an integral action to avoid reactor temperature offsets, while the catalyst circulation rate controller requires a nonlinear static observer to predict the coke concentration on the regenerated catalyst from dense bed and flue gas regenerator temperatures. The performance of the controller for changes on the oil feed rate, caking tendency of the feed, as well as for reactor temperature set point changes is faster and smoother than Kurihara's scheme.     

ROBUST STABILITY ANALYSIS OF GENERIC MODEL CONTROL

In this paper, the robust stability of Generic Model Control (GMC) is analyzed under the condition that the explicit control law is available. This anslysis is performed by finding a strict Lyapunov function for the nominal process and applying a perturbation theorem. Based on the passivity theorem, a procedure to synthesize a robust stable GMC controller is proposed for a given set of processes. The significance of this approach is discussed as well as its disadvantages.     

TOWARDS A COMPREHENSIVE MODEL BASED CONTROL OF MILK DRYING PROCESSES

Milk powder manufacture is carried out on large scale and to exacting standards. To date, process control has been largely empirical. Recent advances in the understanding of process mechanisms and product properties is allowing model-based process control strategies to be developed. This review briefly describes the manufacture of milk powder and discusses in detail milk concentrate viscosity and atomization, particle size distribution, the drying mechanism and protein denaturation, in relation to the development of such strategies.     

基于PID模糊控制的陶瓷窑炉温度控制系统的设计

分析了应用于陶瓷窑炉的PID模糊控制技术的基本原理、设计方法以及控制程序的计算机实现方法等。这是一种能抗干扰，不依赖于对象模型的实用智能控制技术。     

NONLINEAR MODEL PREDICTIVE CONTROL

Nonlinear Model Predictive Control (NMPC), a strategy for constrained, feedback control of nonlinear processes, has been developed. The algorithm uses a simultaneous solution and optimization approach to determine the open-loop optimal manipulated variable trajectory at each sampling instant. Feedback is incorporated via an estimator, which uses process measurements to infer unmeasured state and disturbance values. These are used by the controller to determine the future optimal control policy. This scheme can be used to control processes described by different kinds of models, such as nonlinear ordinary differential/algebraic equations, partial differential/algebraic equations, integra-differential equations and delay equations. The advantages of the proposed NMPC scheme are demonstrated with the start-up of a non-isothermal, non-adiabatic CSTR with an irreversible, first-order reaction. The set-point corresponds to an open-loop unstable steady state. Comparisons have been made with controllers designed using (1) nonlinear variable transformations, (2) a linear controller tuned using the internal model control approach, and (3) open-loop optimal control. NMPC was able to bring the controlled variable to its set-point quickly and smoothly from a wide variety of initial conditions. Unlike the other controllers, NMPC dealt with constraints in an explicit manner without any degradation in the quality of control. NMPC also demonstrated superior performance in the presence of a moderate amount of error in the model parameters, and the process was brought to its set-point without steady-state offset.     

STOCHASTIC INFERENTIAL CONTROL OF THE FLUIDIZED STATE VIA DIFFERENTIAL PRESSURE MEASUREMENTS

Differential pressure is measured across some portion of most industrial fluidized beds. Average differential pressure is related to the weight of material suspended between the measurement locations, but higher frequency noise of the measurement is usually not considered. In this work stochastic variations in differential pressure were modeled as a time series and related to the degree of mixing and turbulence within the bed (fluidized stale). Specifically, mean values of recursively estimated model parameters changed regularly with fluidizing-air flow and, consequently, with the character of the fluidized state. Control of the fluidized state was inferred through closed loop control of model parameters. Both a simple floating algorithm and more complex adaptive algorithm were successful based on controller and fluidized bed performance.     

LINEAR FEEDBACK EQUIVALENCE AND NONLINEAR CONTROL OF A CLASS OF TWO-DIMENSIONAL SYSTEMS

This paper applies the concept of linear feedback equivalence to two-dimensional nonlinear control systems of a certain model structure. Uniqueness and stability characteristics of the system are investigated. It is shown that global asymptotic stability can in general be achieved. A simple mathematical expression for a component of the unique steady-state is derived which provides a guide for the choice of the control parameters to obtain desirable dynamic properties and minimize steady-state offset. Numerical experiments in the phase plane of a model of an exothermic CSTR are employed to verify the analysis.     

基于遗传算法的层压板强度优化设计

本文的主要目的是研究遗传算法的优化机理并将遗传算法应用到复合材料层压板铺层的优化设计中。在深入理解力学原理,熟悉掌握数值计算方法的基础上对层压板进行强度优化设计。因此,在深入研究单层复合材料以及叠层复合材料的弹性特征理论基础和改进的遗传算法优化理论之后,针对复合材料层压结构遗传算法优化设计中层压结构参数具有离散型的特点,提出了适合复合材料层压结构遗传算法优化设计的整数编码策略,以整数来表征层压结构参数,这种编码方式简单直观、使用方便、易于进行遗传算法的交叉及变异操作。在分析层压结构强度的基础上,针对结构强度优化的目标构造了可用于遗传算法的适应度函数。同时参考了一定的铺层规则,在铺层角度限制为工程中常用的四种角度的前提下,应用遗传算法对层压板进行了铺层优化设计。设计结果表明,由于遗传算法特有的处理离散型问题的优势,在层压板铺层的优化设计中应用遗传算法是可行和可信的。     

ADAPTIVE INTERNAL MODEL CONTROL OF HAMMERSTEIN-NONLINEAR SYSTEMS

Adaptive internal model control is analyzed for a single-input-single-output nonlinear system represented by the Hammerstein model wherein the nonlinearity is an odd order polynomial. The recursive least-squares method of Chang and Luus (1971) is used for parameter estimation. To obtain a stable approximation to the inverse of the process model for use as the controller, Vogel-Edgar's method (1980) for linear system is employed. Simulation results have shown that the adaptive internal model control (IMC) performs well even with non-minimum phase Hammerstein systems in the presence of unmeasured load changes and dead-time variations. The performance of nonlinear IMC is found to be superior to that of linear IMC.