Control-relevant Model Research Articles

The impact of process design decisions on operability and control of an LNG process

This paper describes a framework for integrated process and control structure design, and applies this framework to a Liquefied Natural Gas (LNG) process design. The overall aim of the work is to contribute to the methodological basis for improved design and operability of gas processing plants. Good operability means essentially that a plant can be operated easily, i.e. it can cope with unknown disturbances, offsets and other uncertainties with the smallest possible profit loss and without frequent shutdowns. This is obtained both through the design of the process itself and the design of the control system. There is a potential for improved operability of process plants, and thereby reduced profit loss, by considering these two aspects together. The main message is that this is handled by considering control structure design when process design changes are made. The main steps for developing an improved procedure for integrated process and control design are suggested. Such a procedure includes analyses of how altering key parameters in the process design affects the best possible control structure in the presence of defined disturbance scenarios. An important ingredient is the use of a dynamic, control relevant simulation model. The paper presents an analysis of how altering compressor size affects the choice of control structure for the Tealarc LNG process. This analysis has been carried out using a self-optimizing control methodology.

1‐D dynamic modeling of SOFC with analytical solution for reacting gas‐flow problem

AbstractA first principle one‐dimensional (1‐D) dynamic model for tubular solid oxide fuel cell (SOFC) is developed. To convert the distributed model into a control relevant nonlinear state space model, an approximate analytical solution for reacting gas‐flow problem is proposed, and applied to the 1‐D SOFC dynamic model. Compared to numerical solutions, the proposed solution can significantly reduce requirements in computation, and thus, facilitate dynamic simulations and control applications. Distributed dynamic relations between current density and electromotive force (EMF) are investigated. Diffusion, inherent impedance, primary flow, heat transfer, and internal reforming/shifting reaction are considered simultaneously. Dynamic responses of the interested variables when external current, fuel and air inlet streams are perturbed by step changes are investigated through simulations. © 2008 American Institute of Chemical Engineers AIChE J, 2008.

Una metodología para la identificación integrada con el diseño de controladores imc-pid

Este artículo describe, en forma de tutorial, una metodología integrada para la identificación y diseño de controladores comenzando con el modelado dinámico basado en datos de planta, y concluyendo con la sintonía de parámetros para controladores de tipo PID de elevadas prestaciones. Al integrar el paso de identificación con el diseño de controladores, el método logra demostrar una funcionalidad que es muy deseada por la comunidad de control industrial. Los pasos principales en esta metodología son: diseño experimental, estimación de modelos tipo ARX de alto orden, y reducción de modelos relevantes al control que están de acuerdo con leyes de sintonía IMC-PID. Cuando se utiliza una entrada de excitación persistente, el estimado del modelo ARX de alto orden es consistente, lo cual hace que el método sea atractivo como modelo intermedio para estimación de modelos de bajo orden relevantes al control. Además, el bajo esfuerzo computacional asociado con la estimación de modelos ARX significa que se pueden usar eficazmente métodos estadísticos sencillos (tales como la validación cruzada) para determinar una estructura adecuada para el modelo ARX sin mucha intervención por parte del usuario. La metodología se ilustra en el caso de una planta con retardo sujeta a una perturbación con elevada deriva.

Frequency domain subspace identification of a tape servo system

As capacity demands for magnetic tape storage systems grow, servo actuator design for tracking data on high density tape media presents new modeling and control design challenges. In this paper a frequency weighted subspace identification algorithm is presented for control relevant model estimation of a tape servo actuator. Common to other subspace identification methods, the proposed algorithm is based on linear algebra techniques providing means for model order selection and model computation. The proposed subspace identification also allows for frequency dependent weightings to emphasize frequency data around the cross-over frequency to find models relevant for control design. The algorithm is applied to data obtained from a tape storage device, demonstrating model order selection and the estimation of servo actuator dynamics with control relevant model fit criteria.

Evaluation study of an efficient output feedback nonlinear model predictive control for temperature tracking in an industrial batch reactor

The paper illustrates the benefits of nonlinear model predictive control (NMPC) for the setpoint tracking control of an industrial batch polymerization reactor. Real-time feasibility of the on-line optimization problem from the NMPC is achieved using an efficient multiple shooting algorithm. A real-time formulation of the NMPC that takes computational delay into account is described. The control relevant model for the NMPC is derived from the complex-first principles model and is fitted to the experimental data using maximum likelihood estimation. A parameter adaptive extended Kalman filter (PAEKF) is used for state estimation and on-line model adaptation. The performance of the NMPC implementation is assessed via simulation and experimental results.

Dynamic modeling of a finite volume of solid oxide fuel cell: The effect of transport dynamics

A dynamic model for a finite volume of cell based on physical principles is built in the form of a nonlinear state-space model to investigate dynamic behaviors of tubular solid oxide fuel cell (SOFC) and develop a control relevant model for further control studies. Dynamic effects induced by diffusions, intrinsic impedance, fluid dynamics, heat exchange and direct internal reforming/shifting (DIR) reactions are all considered. Cell temperature, ingredient mole fractions, etc. are the state variables and their dynamics are investigated. Dynamic responses of each variable when the external load changes are simulated. Simulation results show that fuel flow, inlet pressure and temperature have significant effects on the dynamic performance of SOFC. Further it is shown that, compared to other inlet flow properties, cathode side air inlet temperature has the most significant effect on SOFC solid phase temperature and performance. Compared with inlet pressures and temperatures, the effect of flow velocity is not significant. Simulation also indicates that the transient response of SOFC is controlled mainly by the dynamics of cell temperature owing to its large heat capacity.

NONLINEAR MODEL PREDICTIVE CONTROL OF BATCH PROCESSES: AN INDUSTRIAL CASE STUDY

Batch processes play a significant role in the production of most modern high-value added products. The paper illustrates the benefits of nonlinear model predictive control (NMPC) for the setpoint tracking control of an industrial batch polymerization reactor. Real-time feasibility of the on-line optimization problem from the NMPC is achieved using an efficient multiple shooting algorithm. A real-time formulation of the NMPC that takes computational delay into account is described. The control relevant model used in the NMPC is derived from the complex first principles model and is fitted to the experimental data using maximum likelihood estimation. A parameter adaptive extended Kalman filter (PAEKF) is used for state estimation and on-line model adaptation. The performance of the NMPC implementation is assessed via simulation and experimental results.

MISO-Structure-Based Control-Relevant Identification of MIMO Systems with Steady-State Gain Matching

The prefilter-based control-relevant identification (CRI) scheme proposed by Rivera et al. [IEEE Trans. Autom. Control 1992, 37 (7), 964−974] for single-input/single-output (SISO) systems estimates a model of the system that matches the true system dynamics in a range of frequencies that are important and relevant for closed-loop performance. In this paper, an extension of this approach is proposed for the CRI of multi-input/multi-output (MIMO) systems by partitioning the overall system into individual multi-input/single-output (MISO) structures. Furthermore, CRI methodologies proposed hitherto have not examined the issue of accurate identification of the steady-state gain. In this paper, the importance of achieving good steady-state gain match is emphasized, and a method is proposed to identify a control-relevant model that has a good match, at steady state (zero frequency), as well as in the control-relevant frequency band, with the true plant. The effect of the proposed control-relevant prefiltering on the identification of the high- and low-gain directions is also analyzed in this paper. The proposed methodology is validated using simulations on representative problems from the literature and on a paper machine problem.

Model predictive control relevant identification and validation

The role of data prefiltering in model identification and validation is presented in this paper. A model predictive control relevant data prefilter, namely the multistep ahead prediction filter for optimal predictions over every step within a finite horizon, is presented. It is shown that models that minimize the multistep prediction errors can be identified or verified by filtering the data using certain data prefilters and then applying the prediction error method to the filtered data. Based on these identification results, a predictive control relevant model validation scheme using the local approach is proposed. The developed algorithms are verified through simulations as well as industrial applications.

Frequency-Domain-Based, Control-Relevant Model Reduction for Nonlinear Plants

Chemical plants exhibit nonlinear dynamics that need to be captured accurately for model-based control. This paper focuses on a frequency-domain-based, control-relevant model reduction strategy for nonlinear process plants. The generalized frequency response function proposed for the nonlinear plants has been used to analyze the characteristics of nonlinear structures in the frequency domain, from a control-relevant viewpoint. Control-relevant model reduction is sought by minimizing the mismatch between a higher order, nonlinear proxy plant and candidate reduced order nonlinear model structures. The minimization is proposed to be done by prefiltering of identification data to shape the mismatch in a control-relevant manner. For these prefilters as well, candidate linear and nonlinear structures are evaluated and an algorithm to design the prefilters to reflect the servo and regulatory performance specifications is proposed. The model reduction methodology is used along with a nonlinear internal model control (NIMC) algorithm and is demonstrated on a simple simulation as well as on a representative nonlinear chemical reactor studied in the literature.

Review and future directions in the modelling and control of continuous drum granulation

Many granulation plants operate well below design capacity, suffering from high recycle rates and even periodic instabilities. This behaviour cannot be fully predicted using the present models. The main objective of the paper is to provide an overview of the current status of model development for granulation processes and suggest future directions for research and development. The end-use of the models is focused on the optimal design and control of granulation plants using the improved predictions of process dynamics. The development of novel models involving mechanistically based structural switching methods is proposed in the paper. A number of guidelines are proposed for the selection of control relevant model structures.

A control-relevant model reduction technique for nonlinear systems

A novel control-relevant model reduction technique for nonlinear systems is proposed utilizing the idea of the balanced truncation. Unlike the widely-accepted Karhunen — Loeve method where the state basis for the reduced system is found from the state snapshots, the proposed technique takes into account the input, state, and output information together and provides a near-balanced reduced-order model that approximates the system map instead of the state snapshots. Performance of the technique is demonstrated for a linear system and a non-adiabatic fixed-bed reactor model.

An integrated identification and control design methodology for multivariable process system applications

We present a way to take advantage of the favorable asymptotic properties of ARX estimators to develop an integrated methodology for identification and controller design for multivariable process plants. This method relies on well-established numerical tools and builds on an engineer's existing process and statistical intuition. Specifically, the ARX estimate serves as a suitable intermediate model for the design and analysis of MIMO process control systems. Guidelines for the design of pseudo-random binary sequence signals that take advantage of the engineer's prior knowledge of the process time constants are presented. Control-relevant model reduction is performed on elements of the ARX model to obtain low-order models conforming to the IMC-PID tuning rules. A simple analysis technique is used to assess stability of the decentralized and decoupled strategies. These techniques and full multivariable control are applied to the Shell heavy oil fractionator problem and the Weischedel-McAvoy distillation column model, respectively.

PIDtuneTM: A Graphical Package for Integrated System Identification and PID Controller Design

This paper describes pIDtuneTM; a MATLAB-based package that integrates system identification and PID controller design. The program addresses the PID tuning needs commonly expressed by control engineers in the process industries. The package relies on ARX estimation and control-relevant model reduction to obtain models consistent with the Internal Model Control (IMC) PID tuning rules. Furthermore, the package allows the user to simulate closed-loop behavior and provides analysis tools for assessing the benefits of choosing particular tuning parameters for setpoint tracking and load disturbances, with or without uncertainty.

Control relevant model reduction of Volterra series models

This paper presents a two-step method for control-relevant model reduction of Volterra series models. First, using the nonlinear IMC design as a basis, an explicit expression relating the closed-loop performance to the open-loop modeling error is obtained. Secondly, an optimization problem that seeks to minimize the closed-loop error subject to the restriction of a reduced-order model is posed. By showing that model reduction of kernels with different degrees can be decoupled in the problem formulation, the optimization problem is simplified into a mathematically more convenient form which can be solved with significantly less computational effort. The effectiveness of the proposed method is illustrated on a polymerization reactor example where a second-order Volterra model with 85 parameters is reduced to a Hammerstein model with 3 parameters. Despite the lower ‘open-loop’ predictive ability of the control-relevant model, the closed-loop performance of the reduced-order control system closely mimics that of the full order model.

ASUtune: A Program for Integrated System Identification and PID Controller Design

This paper describes ASUtune , a MATLAB program for combined identification and PID controller design. By integrating identification with PID controller design, the method displays functionality that is often demanded by the practicing engineering community. The major steps in this integrated methodology are, experimental design and execution, high-order ARX estimation, and control-relevant model reduction leading to models that comply with the IMC-PID tuning rules. These PID tuning rules are compatible with the Honeywell TDC 3000 distributed control system.

Multivariable frequency-response curve fitting with application to control-relevant parameter estimation

A weighted least-squares method for curve fitting multivariable, discrete-time transfer-function models from a known plant frequency response is presented. The algorithm supports the use of iterative, pre/post matrix-valued weighting functions, which commonly arise in control-relevant parameter estimation problems. A challenging case study involving an ill-conditioned, 40-tray high-purity binary distillation column model is presented. The closed-loop responses from the control-relevant model are superior to those obtained from unweighted multivariable estimation methods and one element-at-a-time estimation, in spite of the fact that these latter models display a closer ‘open-loop’ fit.

A Methodology for Integrated System Identification and PID Controller Design

Abstract This paper describes an integrated identification and control design methodology that begins with dynamic modeling from plant data and concludes with parameter settings for high performance PID controllers. By integrating identification with PID controller design, the method displays functionality that is often demanded by the practicing engineering community. The major steps in this integrated methodology are: experimental design and execution, high-order ARX estimation, and control-relevant model reduction leading to models that comply with the IMC-PID tuning rules. A high-order ARX model estimate serves as an intermediate model for control-relevant model reduction purposes; furthermore, the low computational effort associated with ARX estimation means that simple statistical tools (such as cross validation) can be used to efficiently determine a suitable structure for the ARX model without substantial user intervention. The methodology is demonstrated on a delayed time plant subjected to significant drift (i.e., a disturbance represented by an autoregressive integrated noise model).

Control relevant model reduction: a reduced order model for ‘model IV’ fluid catalytic cracking units

This paper extends the systematic model reduction approach of Robertson, developed for startup/shutdown operations, to regulatory/servo control applications. The ‘Model IV’ FCCU model developed by McFarlane et al. is used as a case study. To facilitate model reduction, the distributed parameter FCCU is firstly transformed into a lumped parameter representation prior to linearization. The lumping procedure presented is based on integral average properties, thereby overcoming the problem of a dramatic increase in the number of states whilst still retaining model accuracy. The subsequent linearization and the properties of the linear model are discussed. The ‘lumped’ parameter model is reduced by applying a structure preserving model reduction technique. The performance of the reduced order model, incorporated within a linear multivariable controller, was seen to lie within the preset bounds of the reference model. In contrast, the deficiency of traditional heuristic model reduction is also illustrated.

Control-relevant model reduction problems for SISO H2, H∞, and μ-controller synthesis

Abstract The problem of model reduction in the context of control system design is investigated. Starting from closed-loop objectives (H2,H∞, and μ), equivalent weighted open-loop plant and controller reduction problems are developed. The control-relevant weight function incorporates explicitly all the important characteristics of the control problem, such as the setpoint/disturbance spectrum and the designer requirements for the sensitivity/complementary sensitivity functions, Furthermore, these control-relevant reduction problems are complemented with validation procedures that indicate rigorously the effects of the reduction problem on the desired performance objectives. A simple algorithm that uses standard regression routines is presented to solve these problems.