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.     

GENERIC MODEL ADAPTIVE CONTROL

Generic Model Control (GMC) is a process model based control algorithm incorporating a process model directly within the control structure. It has been shown to produce excellent control, despite reasonable modelling errors. In this paper an algorithm is developed within a GMC framework which reduces the effect of larger modelling errors by regularly updating the model parameters. This new adaptive algorithm is capable of adapting model parameters in a nonlinear model, where the parameters appear in a nonlinear manner. Several examples are presented to illustrate the principles of the technique.     

RELATIVE ORDER REDUCTION OF MULTIVARIABLE NONLINEAR PROCESSES FOR GENERIC MODEL CONTROL (GMC)

To be implemented within Generic Model Control, a process model must have a relative order of one. When systems have relative orders greater than one (“high relative order systems”) techniques are required to enable model based control to take place. In this paper, a relative order model reduction algorithm is presented, which reduces high relative order models to relative order one. The reduction algorithm is based on the singular perturbation model reduction method. The reduction method conserves some of the important linear qualities of the system, making implementation of the model within a nonlinear model based controller such as GMC attractive.     

AN ADAPTIVE STRATEGY FOR CONSTRAINED GENERIC MODEL CONTROL

The performance of control systems on industrial processes is often constrained—constraints on the process inputs and outputs. Effective control algorithms must be cognizant of the presence of these constraints. Generic Model Control (GMC) is a model-based control framework for both linear and nonlinear systems without explicit constraint handling. In this paper, it is shown that an adaptive approach can be incorporated within GMC to accommodate the constraints by adapting one of the two GMC parameters during the control procedure. Adaptation is determined to be necessary when the predicted process state and output variables as calculated by the process model violate their constrained values. The adaption is achieved through assessing the sensitivities of the constraints to the GMC parameters. Two non-linear examples are presented which demonstrate the efficiency of the approach.     

自适应广义一般模型控制

针对一般模型控制的缺点,提出了两种改进的自适应广义一般模型控制方法.它们把传统的一般模型控制推广到相对阶大于1 同时又具有时变参数的复杂非线性过程.第1种控制策略主要利用强跟踪滤波器直接在线估计时变参数,来修正过程模型;另一种方法是将所有干扰因素归结为输入等价干扰,通过估计它来实现对过程的前馈控制.仿真实验结果验证了所提出方法的有效性.     

ROBUST CONTROL OF BATCH REACTORS

The design of robust nonlinear feedback controller is analysed for a trajectory tracking in a single-input single-output nonlinear state variable system x = f(x) + g(x)u, y=cx which arises in nonlinear chemical processes particularly in batch reactor control problems. Simulation results for the batch reactor temperature tracking problem show the effectiveness of the control scheme and its robustness to modelling errors. The method is also applicable to multi-input multi-output system where the number of inputs is equal to that of outputs. The controller design is also analyzed for situations wrier: the kinetics, the activation energy and Ihe heat of reaction are unknown and also only limited measurement of state-variables are available. The method of Youcef-Toumi and Ito (1987) is applied to such problems and the effectiveness of control system is shown by simulation.     

一种约束一般模型控制新方法

在引入近似预测模型的基础上 ,应用基于二次规划的滚动优化算法 ,处理被控量、操作量及其变化速率的线性约束 .将此优化算法与经典的一般模型控制 (GMC)方法相结合 ,给出了一种基于二次规划的约束一般模型控制新方法 .     

ROBUST NONLINEAR PREDICTIVE CONTROL USING A DISTURBANCE ESTIMATOR

A robust nonlinear predictive control strategy using a disturbance estimator is presented. The disturbance estimator is comprised of two parts: one is the disturbance model parameter adaptation and the other is future disturbance prediction. A linear discrete model is proposed as a disturbance model which is formulated by using process inputs and available process measurements. The recursive least square (RLS) method with exponential forgetting is used to determine the uncertain disturbance model parameters and for the future disturbance prediction, future disturbances projected by the future process inputs are used. Two illustrative examples: a jacketed CSTR as a SISO system: an adiabatic CSTR as a MIMO system, and experimental results of the distillation column control are presented. The results indicate that a substantial improvement in nonlinear predictive control performance is possible using the disturbance estimator.     

IDENTIFICATION AND ROBUST CONTROL OF AN INDUSTRIAL STEAM REFORMER

The dynamic behaviour of a methane fired steam reformer is studied in an industrial environment. A system identification approach is used to determine transfer functions linking coil outlet temperature and fuel rate. All models have in common an unstable zero and large gain fluctuation over different operating conditions. A statistical clustering technique was used to determine the reactor dynamic model, for different operating conditions. To counteract the impact of gain fluctuation, a minimax error process model selection was used to determine a nominal model for the design of an IMC controller. The tuning of the controller was shown to be equivalent to the tuning of the time constant of the robustness filter. Improved temperature regulation can be used to increase the operating temperature of the reactor, thus leading to reducing the steam to carbon ratio and in turn improving process ecconomics.     

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.     

ROBUST ANALYSIS AND PID TUNING OF CASCADE CONTROL SYSTEMS

The robustness and performance of the cascade control system are analyzed in this article. A robustness measure is defined, and it is shown that it can reflect the interaction between the inner loop and the outer loop and has a clear indication of the robustness of each loop. The measure can serve as a graphic aid for tuning the cascade controller. Moreover, it is observed that the cascade controller can be tuned individually for each loop instead of sequentially.     

ROBUST ANALYSIS AND PID TUNING OF CASCADE CONTROL SYSTEMS

The robustness and performance of the cascade control system are analyzed in this article. A robustness measure is defined, and it is shown that it can reflect the interaction between the inner loop and the outer loop and has a clear indication of the robustness of each loop. The measure can serve as a graphic aid for tuning the cascade controller. Moreover, it is observed that the cascade controller can be tuned individually for each loop instead of sequentially.     

ON THE DESIGN OF ROBUST CONTROL SYSTEMS FOR DISTILLATION COLUMNS

The distillation column dynamics is very nonlinear, especially at high purity. Multivariable control system designs, which are essentially linear, may not be able to perform well at different operating conditions. This paper looks at three different multivariable design techniques—decoupling control, optimal state feedback and pole assignment as applied to distillation column control. Robustness of these techniques are analyzed by looking at the performance of these controllers at different operating conditions. Some interesting results are obtained.     

APPLICATION OF MULTIVARIABLE NONLINEAR ADAPTIVE GENERIC MODEL CONTROL TO A PACKED DISTILLATION COLUMN

Nonlinear adaptive generic model control and self-tuning PID control systems were applied to control the top and bottom product temperature of a packed distillation column separating methanol-water mixture. In the first control algorithm, an adaptive generic model control (AGMC) structure was proposed for dual temperature control of the system. In the second control algorithm, nonlinear self tuning PID (NLSTPID) control based on pole-placement technique was used to control the same system. For NLSTPID control purposes pseudo random binary sequence (PRBS) signal and recursive identification algorithm were used to estimate the relevant parameters of a polynomial NARMAX model. In this work, real-time application has been carried out. In both dynamic and control studies, perturbations in feed composition were utilized as the disturbance, and the reboiler heat duty and the reflux ratio were selected as the manipulated variables. The control performances have been obtained by using ISE and, in general, AGMC results were better than those of the STPID control algorithm.     

APPLICATION OF MULTIVARIABLE NONLINEAR ADAPTIVE GENERIC MODEL CONTROL TO A PACKED DISTILLATION COLUMN

Nonlinear adaptive generic model control and self-tuning PID control systems were applied to control the top and bottom product temperature of a packed distillation column separating methanol-water mixture. In the first control algorithm, an adaptive generic model control (AGMC) structure was proposed for dual temperature control of the system. In the second control algorithm, nonlinear self tuning PID (NLSTPID) control based on pole-placement technique was used to control the same system. For NLSTPID control purposes pseudo random binary sequence (PRBS) signal and recursive identification algorithm were used to estimate the relevant parameters of a polynomial NARMAX model. In this work, real-time application has been carried out. In both dynamic and control studies, perturbations in feed composition were utilized as the disturbance, and the reboiler heat duty and the reflux ratio were selected as the manipulated variables. The control performances have been obtained by using ISE and, in general, AGMC results were better than those of the STPID control algorithm.     

两种数控缠绕软件的建模方法及特点

本文主要分析CADWIND软件和WSAP软件的数学模型形成思想,总结两种数控缠绕软件的特点,通过实际缠绕编程的应用来验证这两种缠绕软件的优缺点.     

SIMPLIFIED MODEL PREDICTIVE CONTROL FOR SINGLE AND CASCADE LOOPS

An analysis of the simplified model predictive control (SMPC) has been carried out so that the digital algorithm involved could be conveniently applied to single loop control of complex higher order processes with noise and dead time. Simulation results are presented to show the excellent capability of the control technique particularly in the presence of noise. Certain aspects related to SMPC tuning, such as development of a simple closed loop tuning procedure, have been discussed. Finally, the control law is extended to cascade control structure involving a primary and a secondary loop.     

MODEL PREDICTIVE CONTROL (MPC) AND ITS CURRENT ISSUES IN CHEMICAL ENGINEERING

Model predictive control (MPC) is one of the main process control techniques explored in the recent past; it is the amalgamation of different technologies used to predict future control action and future control trajectories knowing the current input and output variables and the future control signals. It can be said that the MPC scheme is based on the explicit use of a process model and process measurements to generate values for process input as a solution of an on-line (real-time) optimization problem to predict future process behavior. There have been a number of contributions in the field of nonlinear model–based predictive control dealing with issues like stability, efficient computation, optimization, constraints, and others. New developments in nonlinear MPC (NMPC) approaches come from resolving various issues, from faster optimization methods to different process models. This article specifically deals with chemical engineering systems ranging from reactors to distillation columns where MPC plays a role in the enhancement of the systems’ performance.     

COMPENSATOR FOR CONSTANT RELATIVE STABILITY IN PROCESS CONTROL SYSTEMS

Process control systems are designed for a closed-loop peak magnitude of 2 dB, which corresponds to a damping coefficient (ζ) of 0.5 approximately. With this specified constraint, the designer should choose and/or design the loop components to maintain a constant relative stability. However, the manipulative variable in almost all chemical processes will be the flow rate of a process stream. Since the gains and the time constants of the process will be functions of the manipulative variable, a constant relative stability cannot be maintained. Up to now, this problem has been overcome either by selecting proper control valve flow characteristics or by gain scheduling of controller parameters. Nevertheless, if a wrong control valve selection is made then one has to account for huge loss in controllability or eventually it may lead to an unstable control system. To overcome these problems, a compensator device that can bring back the relative stability of the control system was proposed. This compensator is similar to a dynamic nonlinear controller that has both online and offline information on several factors related to the control system. The design and analysis of the proposed compensator is discussed in this article. Finally, the performance of the compensator is validated by applying it to a two-tank blending process. It has been observed that by using a compensator in the process control system, the relative stability could be brought back to a great extent despite the effects of changes in manipulative flow rate.     

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.