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 .     

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.     

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.     

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.     

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.     

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.     

Generic model control — A case study

This paper outlines the use of a process model directly in a control algorithm. The process considered, a forced circulation single-stage evaporator, is a nonlinear interacting process. The control strategy employing a process model derived from fundamental mass and energy balances is shown to outperform single loop and predictive control strategies by a significant amount. The control structure is first presented in general form and then specifically applied to this process.     

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.     

PROCESS/MODEL MISMATCH COMPENSATION FOR MODEL-BASED CONTROLLERS

Process model-based control algorithms that employ a process model directly in the controller, have been shown to produce good control performance and robust behaviour, despite process modelling errors. However, when the process/model mismatch is large, the closed-loop response, while still being better than responses obtained by conventional controllers, will be degraded. This paper presents a new approach to compensate for process/model mismatch errors, and is based upon the Generic Model Control (GMC) algorithm. This approach is applicable to both linear and nonlinear model-based algorithms. Simulation results are presented to illustrate the efficiency of the approach     

Optimization and nonlinear control of a batch crystallization process

Crystallization process has been widely used for separation in many chemical industries due to its capability to provide high purity product. To obtain the desired quality of crystal product, an optimal cooling control strategy is studied in the present work. Within the proposed control strategy, a dynamic optimization is first preformed with the objective to obtain the optimal cooling temperature policy of a batch crystallizer, maximizing the total volume of seeded crystals. Two different optimization problems are formulated and solved by using a sequential optimization approach. Owing to the complex and nonlinear behavior of the batch crystallizer, the nonlinear control strategy which is based on a generic model control (GMC) algorithm is implemented to track the resulting optimal temperature profile. The optimization integrated with nonlinear control strategy is demonstrated on a seeded batch crystallizer for the production of potassium sulfate.     

Simulated nonlinear control studies of five-effect evaporator models

This paper is devoted to the simulated nonlinear control studies of two dynamic models of an industrial five-effect evaporator of an alumina refinery. The simulated control studies were carried out to ascertain the performance and robustness of nonlinear control techniques on the five-effect evaporator prior to its implementation on-site. The nonlinear control structure used in the studies was the multi-input multi-output (MIMO) globally linearizing control (GLC) structure of Kravaris and Soroush (1990). The I/O linearizing controller was implemented in a cascade arrangement with the industrial coded velocity form of proportional-integral (PI) controllers in the simulation. The design parameters were chosen such that the desired decoupling control of the ill-conditioned evaporator models was achieved, and such that cascade arrangement of the nonlinear controller was possible. Simulated results indicates that the MIMO GLC structure provides superior servo and regulatory control to multi-loop single-input single-output (SISO) PI controllers that are currently being used to regulate the five-effect evaporator on-site.     

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.     

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.     

Observer based nonlinear quadratic dynamic matrix control for state space and input/output models

An observer based nonlinear Quadratic Dynamic Matrix Control (QDMC) algorithm is developed for use with nonlinear input-output (I/O) and state space models. It generalizes and extends previously published nonlinear QDMC algorithms. The extension to I/O models is particularly important due to the increased use of neural networks and other types of nonlinear black box models in the chemical industry. Disturbance rejection and offset free tracking is addressed in a general setting utilizing concepts from filtering theory. Various kinds of disturbance models can be incorporated in the formulation. Even though nonlinear models are utilized for model prediction, the on-line optimization is formulated as a single Quadratic Program, thus preserving the computational advantages of nonlinear QDMC as compared to Model Predictive Control algorithms based on nonlinear programming techniques. The examples illustrate parameter tuning for open-loop unstable and stable processes and point out both benefits and shortcomings of the algorithm.     

Nonlinear Model Algorithmic Control of a pH Neutralization Process

Control of pH neutralization processes is challenging in the chemical process industry because of their inherent strong nonlinearity. In this paper, the model algorithmic control （MAC） strategy is extended to nonlinear processes using Hammerstein model that consists of a static nonlinear polynomial function followed in series by a linear impulse response dynamic element. A new nonlinear Hammerstein MAC algorithm （named NLH-MAC） is presented in detail. The simulation control results of a pH neutralization process show that NLH-MAC gives better control performance than linear MAC and the commonly used industrial nonlinear propotional plus integral plus derivative （PID） controller. Further simulation experiment demonstrates that NLH-MAC not only gives good control response, but also possesses good stability and robustness even with large modeling errors.     

Application of pH control to a tubular flow reactor

Tubular flow reactors are mainly used in chemical industry and waste water discharged units. Control of output variables is very difficult because of the existence of high dead-time in these types of r...     

pH控制在管状流动反应器中的应用(英文)

Tubular flow reactors are mainly used in chemical industry and waste water discharged units. Control of output variables is very difficult because of the existence of high dead-time in these types of reactors. In the present work, sodium hydroxide and acetic acid solutions were sent to the tubular flow reactor. The aim was to control pH at 7 in the nonlinear region. The pH control of a tubular flow reactor with high time delay and a highly nonlinear behavior in pH neutralization reaction was investigated experimentally in the face of the various load and set point changes. Firstly, efficiency of conventional Proportional-Integral-Derivative (PID) algorithm in the experiments was tested. Then self-tuning PID (STPID) control system was applied by using the ARMAX model. The model parameters were calculated from input–output data by using PRBS signal as disturbance and Bierman algorithm. Lastly, the experimental fuzzy control of pH based on fuzzy model was achieved to compare the success of fuzzy approach with the performance of other control cases studied.     

基于一般模型控制的高纯内部热耦合精馏策略

内部热耦合精馏塔（ITCDIC）是精馏节能控制的一个前沿。本文提出了一种基于一般模型控制（GMC）的内部热耦合精馏塔的先控策略,以解决导致传统线性控制策略难以得到较好控制效果的高纯下内部热耦合精馏塔的非线性。以苯-甲苯物系作为研究实例,对所提出的高纯ITCDIC控制策略进行了详细研究。设定值改变和过程扰动下的控制品质表明了所提出的高纯ITCDIC的GMC控制策略的切实有效性。     

Explicit Nonlinear Predictive Control of a Distillation Column Based on Neural Models

A nonlinear Model Predictive Control (MPC) algorithm and its application to a distillation column are described. The algorithm uses a neural model of the process that is linearized online around the current operating point. The algorithm is computationally efficient because the control policy is calculated explicitly without any optimization. The algorithm requires online repetition of a matrix decomposition task and the solution of linear equations. The obtained solution is projected onto the admissible set of constraints imposed on the magnitude and the increment of the manipulated variables. For the distillation column considered, the control accuracy is comparable not only to that obtained in MPC with online linearization and quadratic programming but also to that obtained in nonlinear MPC, which is based on full nonlinear optimization repeated at each sampling instant.