The design of experiments,training and implementation of nonlinear controllers based on neural networks

In the area of nonlinear predictive control, several control schemes using artificial neural networks have been proposed. In this work, the issues relating to the information contents of the data used to train the neural network components of these nonlinear predictive control schemes are considered. This raises questions about the design of experiments. A class of feedback-feedforward nonlinear controller based on the model predictive structure (also known as Internal Model Control, IMC, structure) is investigated. The implementation and performance of these neural network based controllers, together with comparisons to other nonlinear and linear controllers, are illustrated on two nonlinear continuous-stirred-tank-reactor simulations.     

Hybrid Neural Network Models Applied to a Free Radical Polymerization Process

This article presents different ways of obtaining hybrid models, which are composed of a simplified phenomenological model and one or several neural networks. As an example, we consider free radical polymerization of methyl methacrylate, achieved through a batch bulk process, in which modeling of conversion and polymerization degrees is analyzed. Kinetics of the process is described through a simplified phenomenological model that does not take into account the gel and glass effects. This last part of the process, which is more difficult to model, is rendered by means of feed-forward neural networks with one or two hidden layers. In the present paper, the hybridization procedure is made in three ways: 1) the neural network corrects the outputs of the simplified kinetic model by modeling the residuals of conversion and polymerization degrees; 2) the neural network provides accurate values of the rate constants to the simplified kinetic model; 3) the neural network models that part of the process in which gel and glass effects appear. It is demonstrated that accurate results are obtained in all three cases, and the hybrid models are easily created and manipulated, especially because they are based on neural networks with quite simple topologies.     

Neural‐Networks‐Based Feedback Linearization versus Model Predictive Control of Continuous Alcoholic Fermentation Process

In this work advanced nonlinear neural networks based control system design algorithms are adopted to control a mechanistic model for an ethanol fermentation process. The process model equations for such systems are highly nonlinear. A neural network strategy has been implemented in this work for capturing the dynamics of the mechanistic model for the fermentation process. The neural network achieved has been validated against the mechanistic model. Two neural network based nonlinear control strategies have also been adopted using the model identified. The performance of the feedback linearization technique was compared to neural network model predictive control in terms of stability and set point tracking capabilities. Under servo conditions, the feedback linearization algorithm gave comparable tracking and stability. The feedback linearization controller achieved the control target faster than the model predictive one but with vigorous and sudden controller moves.     

Modelling and control of different types of polymerization processes using neural networks technique: A review

Polymerization process can be classified as a nonlinear type process since it exhibits a dynamic behaviour throughout the process. Therefore, it is highly complicated to obtain an accurate mechanistic model from the nonlinear process. This predicament always been a “wall” to researchers to be able to devise an optimal process model and control scheme for such a system. Neural networks have succeeded the other modelling and control methods especially in coping with nonlinear process due to their very conciliate characteristics. These characteristics are further explained in this work. The predicament that is encountered by researchers nowadays is lack of data which consequently lead to an imprecise mechanistic model that scarcely conforms to the desired process. The implementations of the neural network model not only restrict to polymerization reactor but to other difficult‐to‐measure parameters such as polymer quality, polymer melts index and mixture of initiators. This work is aimed to manifest ascendancy of neural networks in modelling and control of polymerization process.     

The control of MSF desalination plants based on inverse model control by neural network

In this paper, a nonlinear inverse model control strategy based on neural network is proposed for MSF desalination plant. Artificial neural networks (ANNs) can handle complex and nonlinear process relationships, and are robust to noisy data. The designed neural networks consist of three layers identified from input–output data and trained with a descent gradient algorithm. The set point tracking performance of the proposed method was studied when the disturbance is present in the MSF system. Three controllers are designed for controlling the top brine temperature, the level of last stage and salinity. These results show that a neural network inverse model control strategy (NNINVMC) is robust and highly promising to be implemented in such nonlinear systems. Also the comparison between the top brine temperature of the proposed model and NN predicted data from the literature supports the accuracy of the model.     

基于神经网络的pH中和过程非线性预测控制

针对pH中和过程这一化工过程系统中的典型非线性对象特点，应用神经网络建模思想和模型预测控制方法，并结合Hammerstein模型特点，研究pH中和过程非线性系统的两种新型模型预测控制手段，分别建立基于神经网络的非线性预测控制系统整体求解策略和基于Hammerstein模型的两步法预测控制策略，并用MATLAB对其进行仿真。控制仿真结果表明，建立的神经网络预测控制策略和非线性Hammerstein模型预测控制均优于传统PID控制方法，具有良好的设定值跟踪效果和抗干扰控制响应，说明这两种控制策略是非线性过程的有效控制方法。     

一种用于动态化工过程建模的反馈神经网络新结构

提出了一种新的用于非线性动态化工过程的状态集成反馈神经网络结构 (SIRNN) ,并将静态BP网络的训练算法引入到该网络的训练中 .状态反馈、时间序列延迟与集成节点的概念结合在SIRNN结构中 ,使得在用SIRNN建模过程中既可以考虑系统过去更多时刻的状态信息又可以相对降低网络的复杂程度 ,使得网络结构更趋于合理 .将SIRNN对一单输入单输出二阶非线性动态系统建模 ,并与其他反馈神经网络建模效果进行了比较 ,同时对该网络结构进行了抗干扰性检验 ,并对其在多输入单输出系统的应用中进行了尝试 ,结果表明SIRNN结构对非线性动态系统建模具有快速、高效和抗干扰的良好性能     

Statistical machine-learning–based predictive control of uncertain nonlinear processes

In this study, we present machine-learning–based predictive control schemes for nonlinear processes subject to disturbances, and establish closed-loop system stability properties using statistical machine learning theory. Specifically, we derive a generalization error bound via Rademacher complexity method for the recurrent neural networks (RNN) that are developed to capture the dynamics of the nominal system. Then, the RNN models are incorporated in Lyapunov-based model predictive controllers, under which we study closed-loop stability properties for the nonlinear systems subject to two types of disturbances: bounded disturbances and stochastic disturbances with unbounded variation. A chemical reactor example is used to demonstrate the implementation and evaluate the performance of the proposed approach.     

基于Hammerstein-Wiener模型的连续搅拌反应釜神经网络预测控制

针对化工过程中广泛使用的连续搅拌反应釜(CSTR),提出一种基于神经网络的模型预测控制策略,采用分段最小二乘支持向量机辨识Hammerstein-Wiener模型系数的方法,在此基础上建立线性自回归模式〖DK〗(ARX)结构和高斯径向基神经网络串联的非线性预测控制器。利用BP神经网络训练预测控制输入序列和拟牛顿算法求解非线性预测控制律,从而实现一种基于支持向量机Hammerstein-Wiener辨识模型的非线性神经网络预测控制算法。对CSTR的仿真结果表明,该方法能够更有效地跟踪控制反应物浓度。     

利用组合B样条神经网络实现对间歇反应过程的动态建模

间歇反应过程具有强非线性、非稳态和反应时间固定等特点。利用间歇反应操作时间可预先确定的性质,提出一种新的组合B样条神经网络的建模方法。被控对象输出f（u,t）往往是操纵变量和时间的函数,新方法把这两类函数关系的模拟分别交由两个神经网络承担,以确定变化区域的时间变量作为B样条神经网络的输入,让其分担描述对象随时间变化的动态特性部分,而输出变量与操作变量间的关系则由另一B样条神经网络表示,两个神经网络的组合输出建立间歇反应器的非线性动态模型。它不仅能够简化每个网络的结构,减少权值参数和训练时间,更重要的是可以方便控制策略的求解。本文介绍了建模方法的设计过程,并应用于苯乙烯悬浮聚合间歇反应建模中,仿真实验研究了方法的有效性。还推导了基于该模型的优化控制策略的算法。     

基于控制性能比较的非线性不对称系统预测控制

生产过程某些非线性系统常常表现出不对称动态特性,相对于其在工业工程中经常出现的理论研究特别是控制方法研究则十分有限。本文针对基于正反方向上的两个线性模型分别设计PID控制器的缺陷,提出根据正反方向上的线性模型分别设计相应的状态反馈预测控制器。在每一步的控制率计算中,正反方向的控制器分别计算控制作用,并通过比较正反控制器的控制性能指标来确定最终采用的控制作用。通过pH值控制的仿真实验证明其对非线性不对称系统的控制效果明显优于传统的在正反方向分别采用PID控制的控制效果。     

模糊神经网络及其在系统建模与控制中的应用

模糊神经网络是模糊系统和神经网络的有机结合 ,它吸取了两者的优点。给出了两个具体的模糊神经网络结构以及相应的学习算法 ;介绍了利用模糊神经网络建立 T- S模糊模型的方法 ;讨论了基于 T- S模糊模型的控制系统分析和设计。     

A Comparative Study of Combined Feedforward/Feedback Model Predictive Control for Nonlinear Systems

Model predictive control (MPC) provides a natural framework to realize feedforward and feedback control for nonlinear systems where the effect of disturbances (DVs) cannot be separated from that of manipulated variables (MVs). This study examines the performance of MPC with measured DVs as partial inputs of the model used, which is termed as combined feedforward/feedback MPC (CMPC) in contrast to conventional MPC using a model without input of any measured DV. In the simulation of a pH process, we demonstrate the clear superiority of CMPC over MPC. In the experiment with a bench‐scale ethanol and water distillation column, CMPC and MPC using artificial neural network (ANN) models are applied to the dual temperature control problem. External recurrent neural networks (ERNs) with and without a measured DV (feed rate of the column) as their partial input are built and employed in the experiment, with a result that inclusion of the measured DV in the model makes CMPC perform significantly better than MPC. To strengthen practical experience in applying ANN‐based MPC, a detailed procedure of the experiment is also documented.     

动态系统前馈神经网络模型及其应用

提出反映炼油厂分馏装置动态特性的前馈神经网络模型 ,根据工厂的生产实际及数据特点建立了一种基于时间序列的、适合油品质量指标监测的动态系统前馈神经网络 (DBPNN)结构 .通过用实验室模拟的动态过程数据和炼油厂分馏装置的生产数据分别建模并与传统静态前馈神经网络模型比较 ,结果表明 ,DBPNN模型能够反映动态过程的特性 ,并具有更高的可靠性和适应性 .     

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     

基于神经网络的丙烯腈流化床反应器的模拟与离线优化

利用人工神经网络的方法建立了工业合成丙烯腈流化反应器的数学模型。采用遗传算法与梯度下降法相结合的方法训练神经网络的权值和阀值。经过训练和可靠性检验的人工神经网络能够满足工业生产的模拟要求。利用单纯型算法与遗传算法相结合的优化方法合成丙烯腈工业流化反应器进行了操作系统优化，为在线实时优化控制奠定了基础。     

Dynamic On‐Line Reoptimization Control of a Batch MMA Polymerization Reactor Using Hybrid Neural Network Models

A hybrid neural network model based on‐line reoptimization control strategy is developed for a batch polymerization reactor. To address the difficulties in batch polymerization reactor modeling, the hybrid neural network model contains a simplified mechanistic model covering material balance assuming perfect temperature control, and recurrent neural networks modeling the residuals of the simplified mechanistic model due to imperfect temperature control. This hybrid neural network model is used to calculate the optimal control policy. A difficulty in the optimal control of batch polymerization reactors is that the optimization effort can be seriously hampered by unknown disturbances such as reactive impurities and reactor fouling. With the presence of an unknown amount of reactive impurities, the off‐line calculated optimal control profile will be no longer optimal. To address this issue, a strategy combining on‐line reactive impurity estimation and on‐line reoptimization is proposed in this paper. The amount of reactive impurities is estimated on‐line during the early stage of a batch by using a neural network based inverse model. Based on the estimated amount of reactive impurities, on‐line reoptimization is then applied to calculate the optimal reactor temperature profile for the remaining time period of the batch reactor operation. This approach is illustrated on the optimization control of a simulated batch methyl methacrylate polymerization process.     

基于神经网络的丙烯腈流化床反应器的离线模拟与优化

A mathematical model is developed for an industrial acrylonitrile fluidized-bed reactor based on artificial neural networks.A new algorithm,which combines the characteristics of both genetic algorithm(GA) and generalized delta-rule(GDR) is used to train artificial neural network (ANN) in order to avoid search terminated at a local optimal solution.For searching the global optimum,a new algorithm called SM-GA,incorporating advantages of both simplex method (SM) and GA, is proposed and applied to optimize the operating conditions of an acrylonitrile fluidized-bed reactor in industry.     

Trajectory tracking of a batch polymerization reactor based on input–output-linearization of a neural process model

Input–output-linearization via state feedback offers the potential to serve as a practical and systematic design methodology for nonlinear control systems. Nevertheless, its widespread use is delayed due to the fact that developing an accurate plant model based on physical principles is often too costly and time consuming. Data-based modeling of dynamic systems using neural networks offers a cost-effective alternative. This work describes the methodology of input–output-linearization using neural process models and gives an extended simulative case study of its application to trajectory tracking of a batch polymerization reactor.     

Sequential and iterative architectures for distributed model predictive control of nonlinear process systems

In this work, we focus on distributed model predictive control of large scale nonlinear process systems in which several distinct sets of manipulated inputs are used to regulate the process. For each set of manipulated inputs, a different model predictive controller is used to compute the control actions, which is able to communicate with the rest of the controllers in making its decisions. Under the assumption that feedback of the state of the process is available to all the distributed controllers at each sampling time and a model of the plant is available, we propose two different distributed model predictive control architectures. In the first architecture, the distributed controllers use a one‐directional communication strategy, are evaluated in sequence and each controller is evaluated only once at each sampling time; in the second architecture, the distributed controllers utilize a bi‐directional communication strategy, are evaluated in parallel and iterate to improve closed‐loop performance. In the design of the distributed model predictive controllers, Lyapunov‐based model predictive control techniques are used. To ensure the stability of the closed‐loop system, each model predictive controller in both architectures incorporates a stability constraint which is based on a suitable Lyapunov‐based controller. We prove that the proposed distributed model predictive control architectures enforce practical stability in the closed‐loop system and optimal performance. The theoretical results are illustrated through a catalytic alkylation of benzene process example. © 2010 American Institute of Chemical Engineers AIChE J, 2010