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.     

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.     

Nonlinear adaptive control algorithm for a multicomponent batch distillation column

In this article, a nonlinear adaptive control strategy is proposed for a multicomponent batch distillation column. The hybrid control scheme consists of a generic model controller (GMC) and a nonlinear adaptive state estimator (ASE). In the first part of the study, an adaptive observer is designed aiming to estimate the partially known parameters based on the measured compositions in the presence of process/predictor mismatch. The open-loop dynamic behavior of the developed ASE estimator is investigated under initialization error, disturbance, and uncertain parameters. In the subsequent part, the adaptive GMC-ASE controller (GMC control structure in conjunction with ASE estimator) has been synthesized for the example distillation column. A simulation-based comparative study has been conducted between the derived nonlinear GMC-ASE control algorithm and a gain-scheduled proportional integral (GSPI) law in terms of constant composition control. The proposed adaptive control scheme is shown to be quite promising due to the exponential error convergence capability of the ASE estimator in addition to the high-quality performance of the GMC controller.     

Observer-based control algorithms for a distillation column

This paper studies the synthesis of nonlinear observer-based globally linearizing control (GLC) algorithms for a multivariable distillation column. Two closed-loop observers/estimators, namely extended Kalman filter (EKF) and adaptive state observer (ASO), have been designed within the GLC framework to estimate the state variables along with the poorly known parameters. Exactly same basic model structure was used for developing the observers. The model structure is so simple that the estimator design was performed based on only two component balance equations around the condenser-reflux drum and the reboiler-column base systems of the distillation column. To construct these observers, the poorly known parameters, namely component vapor flow rate leaving top tray, component liquid flow rate leaving bottom tray and distribution coefficient in the reboiler, were considered as extra states with no dynamics. The comparative study has been carried out between the proposed GLC in conjunction with ASO (GLC-ASO) and that coupled with EKF (GLC-EKF). The GLC-ASO control scheme showed comparatively better performance in terms of set point tracking and disturbance as well as noise rejections. The control performance of GLC-ASO and a dual-loop proportional integral derivative (PID) controller was also compared under set point step changes and modeling uncertainty. The proposed GLC-ASO structure provided better closed-loop response than the PID controller.     

Generic model control — A case study revisited

The application of the Generic Model Control (GMC) algorithm to the control of an evaporator has been reported recently by Lee et al. (1989). The results of their case study are claimed to demonstrate the superiority of the nonlinear GMC algorithm over conventional techniques including Dynamic Matrix Control. In this note it is shown that for the evaporator example the improved performance arises primarily from the full multivariable and feedforward nature of the control law, rather than from the nonlinear nature of GMC.     

Nonlinear state estimation and control of a batch reactive distillation

This paper deals with the advanced adaptive control of a batch reactive distillation (RD) column for the production of ethyl acetate. The nonlinear adaptive control law consists of the generic model controller (GMC) and an adaptive state estimator (ASE). In the first part of the present work, the design approach of the ASE scheme in two different forms, namely ASE1 and ASE2, has been addressed for a batch reactive rectifier. The predictor model of both the ASE estimators includes only a component mole balance equation around the condenser-reflux drum system and an extra state equation having no dynamics, and therefore, there is a large process/predictor mismatch. In presence of this structural discrepancy, the adaptive estimation schemes compute the imprecisely known parameters quite accurately based on the measured distillate composition under initialization error, disturbance and uncertainty. In the subsequent part, the adaptive GMC–ASE1 control structure has been formulated for the sample reactive column. This nonlinear control strategy shows comparatively better closed-loop performance than the gain-scheduled proportional integral (GSPI) controller due to the exponential error convergence capability of the estimation scheme and the high-quality control of the GMC law.     

Generic model controller tuning for chemical processes with input saturation

Control in the face of process input constraints is very common and of great practical importance in the processing industries. Generic Model Control (GMC) is a model‐based control framework for both linear and nonlinear systems. In this paper, a constrained GMC controller tuning approach using a nonlinear least squares technique is proposed. This tuning approach is simple to apply. For a SISO GMC control system with input saturation, the tracking performance is significantly improved by adding a simple heuristic switching strategy. The effectiveness of the proposed controller tuning approach is demonstrated using dynamic simulations and MIMO real‐time experiments.     

Control of milk pasteurization process using model predictive approach

A milk pasteurization process, a nonlinear process and multivariable interacting system, is difficult to control by the conventional on–off controllers since the on–off controller can handled the temperature profiles for milk and water oscillating over the plant requirements. The multi-variable control approach with model predictive control (MPC) is proposed in this study. The proposed algorithm was tested for control of a milk pasteurization process in four cases of simulation such as set point tracking, model mismatch, difference control and prediction horizons, and time sample. The results for the proposed algorithm show the well performance in keeping both the milk and water temperatures at the desired set points without any oscillation and overshoot and giving less drastic control action compared to the cascade generic model control (GMC) strategy.     

基于广义最小变差基准的多变量控制性能评估方法

This paper is concerned with the control performance assessment based on the multivariable generalized minimum variance benchmark. An explicit expression for the feedback controller-invariant (the generalized minimum variance) term of the multi-variable control system is obtained, which is used as a standard benchmark for the assessment of the control performance for multi input multi output (MIMO) process. The proposed approach is based on the multivariable minimum variance benchmark. In com-parison with the minimum variance benchmark, the developed method is more reasonable and practical for the control performance assessment of multivariable systems. The approach is illustrated by a simulation example and an industrial application.     

常规控制系统配对设计的动态相对增益阵研究

针对多变量控制系统,提出了一种新的回路配对标准——动态相对增益阵。这种新的配对方案在时域分析内基于系统的开环阶跃响应进行,注重系统的动态特性。通过实例研究和与其他方法的对比表明:这种新的方法更加可靠有效。     

一类化工过程多变量系统的自适应非线性预测控制

针对化工过程的一类多变量非线性系统,提出了一种自适应非线性预测控制(ANMPC)算法。在采用递归最小二乘法进行预测模型参数在线辨识的基础上,将系统的静态非线性关系用一个反向传播(BP)神经网络稳态模型来表示,通过稳态模型求得的动态增益来进一步校正预测模型的参数。详述了ANMPC控制器设计步骤,通过在一个多变量pH中和过程中的仿真验证了本算法的可行性和有效性。     

A study on the adaptive predictive control method for multivariable bilinear processes

A predictive control method for multivariable bilinear processes is derived based on ARMA model. To identify bilinear process models, we use simple equation error method extended to multivariable system. We can obtain the adaptive predictive controller for multivariable bilinear processes by incorporation of the identification algorithm. Offset compensator is provided to correct for the effects of unmeasured disturbances and model inaccuracies. A filter with a singled parameter is used to correct for the effects of an incorrect model. Results of simulation on multivariable bilinear processes show that the proposed control method has satisfactory performance.     

ON THE BEHAVIOR OF CONSECUTIVE ENZYMATIC REACTIONS IN CYCLICALLY OPERATED REACTORS

The dynamics of a process involving consecutive enzymatic reactions were investigated for a case in which the reactions are carried out in a cyclically operated reactor. Each cycle of the operation protocol involves three phases. During phase I the reactor operates in a semi-batch mode involving input of the reactants. During the second phase, the vessel operates in a batch mode. During phase III the reactor has an output only and a fraction of its contents are emptied before another identical cycle begins.

It was found that there are regimes in the operating parameter space where the system can reach more than one limit cycle (multistability). Using computer software based on the bifurcation theory for forced systems, as well as one- and two-parameter continuation algorithms, the impact of various parameters on the dynamics of the system was investigated. The results are presented in the form of diagrams. Conditions under which formation of the intermediate product in the reaction sequence is maximized were also investigated.

Production of the intermediate product in a limit cycle was compared to that obtained in batch and semi-batch operation. Implications of the proposed operation protocol for process optimization and pollution prevention are discussed.     

Advantage of Low‐Cost Predictive Control: Study Case on a Train of Distillation Columns

The process of enriching the ¹³C isotope, performed in trains of cryogenic distillation columns, exhibits large settling times, nonlinearities, large dead‐times, and are difficult to model precisely. Such equipment has been developed in Romania, with concentration increasing up to 70 %. A control analysis for a single unit has already been done including a decentralized multivariable PI controller and two decoupling control algorithms based on the internal model control (IMC) approach. Here, a multivariable predictive controller, the extended prediction self‐adaptive controller is proposed. The simulation results, considering significant modeling errors, demonstrate that this represents a more suitable choice than the previously designed strategies. Comparisons are included to support this idea.     

Micromixing performance of the teethed high shear mixer under semi-batch operation

Semi-batch operated reaction processes are necessary for some competitive reaction systems to achieve a desirable process selectivity and productivity of fine chemical products. Herein the structural and operating parameters of the teethed high shear mixers were adjusted to study the micromixing performance in the semi-batch operated system, using the Villermaux/Dushman reaction system. The results indicate that the rising of the rotor speed and the number of rotor teeth, the decrease of the width of the shear gap and the radial distance between the feed position and the inner wall of stator can enhance the micromixing level and lead to the decrease of the segregation index. Additionally, computational fluid dynamics calculations were carried out to disclose the evolution of the flow pattern and turbulent energy dissipation rate of the semi-batch operated high shear mixer. Furthermore, the correlation was established with a mean relative error of 8.05% and R² of 0.955 to fit the segregation index and the parameters studied in this work, which can provide valuable guidance on the design and optimization of the semi-batch operated high shear mixers in practical applications.     

Integrated run-to-run and on-line model-based control of particle size distribution for a semi-batch precipitation reactor

The dynamics of particle size distribution (PSD) in a precipitation process are represented by a population balance (PB) and related differential–algebraic equations. The control of PSD is studied by using a closed-form solution of the PB. Batch-to-batch control and on-line single batch control strategies are investigated for controlling a semi-batch reactor. A systematic integration of the two strategies is shown to have a complementary effect on the control performance.     

Performance assessment of MIMO control systems with time-variant disturbance dynamics

In this paper, we consider performance assessment problem for multivariable control systems subject to piecewise constant time varying disturbance dynamics. The problem is motivated by the observation that most industrial controllers are linear time invariant (LTI) but the process, particularly the disturbance dynamics, can be time varying. We consider a class of disturbance dynamics that can be modelled by piecewise linear disturbance models, namely piecewise linear time varying (LTV) disturbance dynamics. The problem is formulated as searching for a multi-input multi-output (MIMO) benchmark control that is LTI but optimal in regulating the LTV disturbances. The single-input single-output (SISO) case has been previously solved by minimizing the variance of a most representative disturbance or by the minimization of the sum of the weighted variances of all but one of major disturbances, while satisfying a structured regulatory performance requirement for the major disturbance. In this paper, the previous results are extended to MIMO systems. The counterparts of the two SISO benchmarks are defined as the regular linear time varying disturbances (LTVDs) benchmark and the weighted LTVD benchmark for MIMO control systems, respectively. In addition, a new yet more practical LTVD benchmark, the generalized LTVD benchmark, is also proposed, which minimizes the maximum total variance among all different disturbance dynamics. These three LTVD benchmarks are compared by simulation and industrial application examples. The results show that the weighted and generalized LTVD benchmarks can always lead to better trade-offs on the total output variances.     

Iterative learning model predictive control for constrained multivariable control of batch processes

In this paper, we propose a model predictive control (MPC) technique combined with iterative learning control (ILC), called the iterative learning model predictive control (ILMPC), for constrained multivariable control of batch processes. Although the general ILC makes the outputs converge to reference trajectories under model uncertainty, it uses open-loop control within a batch; thus, it cannot reject real-time disturbances. The MPC algorithm shows identical performance for all batches, and it highly depends on model quality because it does not use previous batch information. We integrate the advantages of the two algorithms. The proposed ILMPC formulation is based on general MPC and incorporates an iterative learning function into MPC. Thus, it is easy to handle various issues for which the general MPC is suitable, such as constraints, time-varying systems, disturbances, and stochastic characteristics. Simulation examples are provided to show the effectiveness of the proposed ILMPC.     

Iterative identification of temperature dynamics in single wafer rapid thermal processing

As the standard size of silicon wafers grows and performance specifications of integrated circuits become more demanding, a better control system to improve the processing time, uniformity and repeatability in rapid thermal processing (RTP) is needed. Identification and control are complicated because of nonlinearity, drift and the time-varying nature of the wafer dynamics. Various physical models for RTP are available. For control system design they can be approximated by diagonal nonlinear first order dynamics with multivariable static gains. However, these model structures of RTP have not been exploited for identification and control. Here, an identification method that iteratively updates the multivariable static gains is proposed. It simplifies the identification procedure and improves the accuracy of the identified model, especially the static gains, whose accurate identification is very important for better control.