Adaptive dynamic matrix control of a distillation column with closed-loop online identification

This paper presents an online identification technique where a process is identified in terms of pseudo impulse response coefficients and subsequently used to update convolution type models to accommodate process-model mismatch. As an example, dynamic matrix control has been applied adaptively to control the top product composition of a distillation column for both servo and regulatory problems. The algorithm automatically detects a large step-like disturbance requiring fresh identification of the process and subsequently adapts the controller to the new model. Simulation studies using an analytical dynamic full order model of a distillation column demonstrated the usefulness of the adaptation scheme. Experimentation on a pilot scale distillation unit vindicated the simulation results.     

Object-oriented approach applied to ANFIS modeling and control of a distillation column

Neurofuzzy networks are hybrid systems that combine neural networks with fuzzy systems, and the Adaptive Neuro-Fuzzy inference system (ANFIS) is a particular case in which a fuzzy system is implemented in the framework of an adaptive neural network. This neurofuzzy approach represents an effective structure to the modeling of plant dynamics, and the oriented-object programming environments offer an intuitive way to address this task. In this paper the MODELICA object-oriented environment has been applied to the ANFIS modeling and indirect control of the heavy and light product composition in a binary methanol-water distillation column by using the adaptive Levenberg–Marquardt approach. The results obtained demonstrate the potential of the adaptive ANFIS scheme under MODELICA for the dual control of composition both for changes in set points with null stationary error even when disturbances are present.     

Adaptive neural model-based decentralized predictive control

This paper considers the problem of developing an adaptive neural model-based decentralized predictive controller for general multivariable non-linear processes, where the equations governing the system are unknown. It derives a method for implementing a neural network model for unknown non-linear process dynamics for adaptive control. The performance of this controller is demonstrated and evaluated using a simulated chemical process: multivariable non-linear control of distillation column. The simulation results indicate that the proposed control strategies have good practical potential for adaptive control of multivariable non-linear processes.     

Online fuzzy identification for an intelligent controller based on a simple platform

The paper presents the identification issues of the self-tuning nonlinear controller ASPECT (Advanced control algorithmS for ProgrammablE logiC conTrollers). The controller is implemented on a simple PLC platform with an extra mathematical coprocessor, but is intended for the advanced control of complex processes. The model of the controlled plant is obtained by means of experimental modelling. A special batch-wise algorithm that is based on the Takagi–Sugeno model and uses “fuzzy instrumental variables” technique is described in the paper. Many robustness problems of the classical adaptive approaches can be circumvented to some extent by the proposed batch-wise approach combined with a supervisory mechanism. The paper also includes some experimental results on the hydraulic pilot plant and some simulation case studies.     

Nonlinear control of high purity distillation column under input saturation and parametric uncertainty

This paper presents a systematic methodology for designing adaptive feedback linearization controller for high purity binary distillation column having uncertain parameters and input saturation. Main goal of the controller is to control the top and bottom compositions of a binary distillation column in presence of both structured and unstructured uncertainty. An adaptive control strategy is used for the estimating uncertain parameters in the system model. Process input saturation always causes an added nonlinearity to the process, leading the process to become uncontrollable. A cascade reduced order nonlinear adaptive controller is designed and implemented to handle both forms of structured and unstructured uncertainty and input saturation problem.     

Globally convergent multivariable adaptive decoupling controller and its application to a binary distillation column

This paper describes a new multivariable adaptive decoupling controller combining a decoupling compensator with a generalized minimum variance technique. The controller can completely decouple closed-loop systems both dynamically and in the steady state. It can control an unstable and/or non-minimum phase system and it can control the process with an arbitrary time delay structure. The proof of global convergence for the algorithm is also given. Successful experimental results of the adaptive decoupling control for a pilot-scale binary distillation column demonstrate the effectiveness of the proposed adaptive decoupling algorithm.     

Selftuning control of some pilot plant processes

This paper reviews the results of some experimental tests carried out to evaluate the performance of selftuning (ST) controllers for temperature control of a continuous stirred tank heater, composition control of a binary distillation column and pH control of an acidic effluent. All the pilot plant units have been controlled using a single variant selftuning control, and a newly developed multivariant ST controller was used for simultaneous control of the terminal compositions of the distillation column. The control performance of the units operating under ST control is compared to that obtained using very well tuned proportional plus integral (PI) or proportional plus integral plus derivative (PID) conventional controllers. Control of pH is shown using a technique of electrochemical neutralisation coupled with a single variant ST controller. The control algorithms have been programmed on a number of microprocessor- and minicomputer-based systems. Z80 for stirred tank heater control, LS1-11 for pH control and HP1000 distributed computer system for distillation control.     

精馏塔的自适应解耦控制

本文针对某精馏塔提出了一种简单的自适应解耦控制器。该控制器将广义最小方差控制策略和解耦补偿器结合起来,不仅可以对随机多变量系统实现动静态解耦而且具有良好的伺服跟踪性能。该控制器与常规PI控制器在某精馏塔双组分控制中的对比实验结果表明自适应解耦控制的性能优越于常规PI控制。     

Robust model predictive control of a pilot plant distillation column

In this paper, the development and application of a robust MPC to a pilot plant ethanol–water distillation column is described. It is shown through experimental tests in the pilot plant, how the linear model can change depending on the operating point. The obtained model has time delays and repeated poles. For this kind of system, the development of an MPC, based on the step response model that is robust to multi-model uncertainty, is presented. The experimental results that confirm the good performance of the proposed controller are also shown.     

Experimental evaluation of a multivariable adaptive nonlinear predictive controller

A new multivariable adaptive nonlinear predictive controller is designed using a general nonlinear input-output model and variable transformations. The controller is similar in form to typical linear predictive controllers can be tuned analogously or by specifying a single parameters for each controlled variable. In addition, the design procedure is computationally efficient. The new controller is compared to a multi-loop proportional-integral (PI) controller with one-way static decoupling and to an adaptive linear predictive controller through tests on a simulated nonlinear distillation column. The new controller performed well in an experimental application to a multicomponent distillation column.     

大型炼油蒸馏塔的多输入预报自校正控制

本文针对一个大型炼油蒸馏塔的控制,设计了一种适用于具有多个确定性扰动对象的多 输入预报自校正控制器,其特点是综合了在线辨识、多输入前馈、最小方差和PI规律各自的优 点,并采用变遗忘因子的最小二乘估计算法在线修正参数,使控制器的收敛性、稳定性与自适应能力较一般自校正控制器大为提高.实践表明,该控制器很好地克服了生产中的干扰和波 动,平稳了操作,成功地实现了对蒸馏塔的产品质量控制.     

Multivariable adaptive predictive control of a binary distillation column

This paper presents a tutorial review of an adaptive predictive control system (APCS). Special emphasis is given to the key issues involved in the practical application of APCS to real processes. These practical issues are illustrated by actual application of SISO and MIMO control of a pilot scale binary distillation column. The experimental evaluation of this method reveals the simplicity of the adaptive algorithm and its excellent performance in an industrial type environment. The experimental results easily outperformed well-tuned classical PID controllers. A brief review of other applications of adaptive control to chemical processes is also included in this paper.     

An Adaptive Neuro-Fuzzy Approach to Control a Distillation Column

In this paper we use a control strategy that enhances a fuzzy controller with self-learning capability for achieving the control of a binary methanol-propanol distillation column. An Adaptive-Network-based Fuzzy Inference System (ANFIS) architecture extended to cope with multivarible systems has been used. This allows the tuning of parameters both of the membership functions and the consequents in a Sugeno-type inference system. To satisfy the control objectives the backpropagation gradient descent through the plant method is applied, hence identification of the plant dynamics is also needed. The performance of the resulting neuro-fuzzy controller under different reference settings for the concentration of methoanol demonstrates the stabilisation of the concentration profiles in the column, leading to an effective methanol composition control.     

Design of adaptive digital set-point tracking PID controllers incorporating recursive step-response matrix identifiers for multivariable plants

It is shown that, by incorporating fast on-line recursive identifiers to provide updated step-response matrices for inclusion in digital proportional-plus-integral-plus-derivative control laws, highly effective adaptive digital set-point tracking controllers can be readily designed for multivariable plants. The effectiveness of such an adaptive controller is illustrated by designing a digital set-point tracking controller for a distillation column.     

Self-tuning regulator applied to a binary distillation column

Utilization of a self-tuning regulator (STR) for control of top product composition of a binary distillation column has been investigated. Results from simulation studies and experimental evaluation of the STR on a pilot scale column are compared with the performance achieved using conventional proportional plus integral control. The STR resulted in significantly improved control for both servo and regulatory control.     

Process identification, uncertainty characterisation and robustness analysis of a pilot-scale distillation column

In this paper different approaches for developing robust advanced control techniques are investigated. A pilot-scale distillation column connected to an industrial distributed control system (ABB MOD 300) that in turn has been interfaced to a VAX-cluster through an Ethernet Gateway is used as a pseudo-industrial set-up to perform these studies. A novel robust multivariable, low order, high performance, model based controller was designed and implemented as a standard PID block within the distributed control system. To provide a systematic approach for designing such an advanced robust controller, several techniques such as dynamic modelling, system identification, uncertainty identification and characterisation etc., are incorporated. The problem of uncertainty characterisation is fully addressed from both theoretical and practical point of view. Both structured and highly structured uncertainty characterisation approaches are used to investigate the robust stability and performance of the control system. Several practical techniques are proposed for designing a robust model-based controller that are readily applicable in an industrial environment. The paper is accompanied by several simulations and also experimental evidences which demonstrate the effectiveness of the proposed approach. ©     

基于神经网络的弹性连杆机构振动主动控制

研究基于神经网络的弹性连杆机构振动主动控制方法.介绍了双隐层动态递归神经 网络的数学模型,利用实验数据离线设计了神经网络辨识器与神经网络控制器.采用基于神 经网络的间接自适应控制策略对弹性连杆机构实施了振动主动控制,机构的动力学品质得到 显著改善.实验结果证明了该方法的有效性.     

基于PLC的精馏塔模糊解耦控制器

针对酒精精馏塔塔顶和塔底两个温度控制环节有较强耦合性,精馏塔精确的数学模型难以建立等问题,提出基于西门子S7—300PLC的模糊解耦控制器设计方法,给出详细的实现步骤。在筛板式酒精精馏塔实验装置上的运行试验证明了该控制器具有良好的控制效果。     

Adaptive recurrent neural control for nonlinear system tracking

We present a new indirect adaptive control law based on recurrent neural networks, which are linear on the input. For the identifier, we adapt a recently published algorithm to fit the neural network type used for identification; this algorithm ensures exponential stability for the identification error. The proposed controller is based on sliding mode techniques. Our main result, stated as a theorem, concerns tracking error asymptotic stability. Applicability of the proposed scheme is tested via simulations.     

基于BP神经网络的自适应控制

本文利用ＢＰ神经网络对被控对象进行在线辨识和控制。为实现自适应控制，本文对ｓｐｅｃｉａｌｉｓｅｄ ｌｅａｒｎｉｎｇ算法进行了改进，在此基础上，本文还提出了一种基于ＢＰ网络的自适应ＰＩＤ控制器。