System identification of an interacting series process for real-time model predictive control
10 Jun 2009-pp 4384-4389
TL;DR: In this study, the discrete-time identification approach based on subspace method with N4SID algorithm is applied to construct the state space model around a given operating point, by probing the system in open-loop with variation of input signals.
Abstract: This paper presents the empirical modeling of the gaseous pilot plant which is a kind of interacting series process with presence of nonlinearities. In this study, the discrete-time identification approach based on subspace method with N4SID algorithm is applied to construct the state space model around a given operating point, by probing the system in open-loop with variation of input signals. Three practical approaches are used and their performances are compared to obtain the most suitable approach for modeling of such a system. The models are also tested in the real-time implementation of a linear model predictive control. The selected model is able to well reproduce the main dynamic characteristics of gaseous pilot plant in open loop and produces zero steady-state errors in closed loop control system. Several issues concerning the identification process and the construction of MIMO state space model are discussed.
Citations
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Book•
01 Jan 2005
TL;DR: In this article, the authors propose a formalization theory for the realization of deterministic problems in linear algebra and disctrete-time linear systems based on the Kalman filter.
Abstract: Introduction Part I: Preliminaries Linear Algebra and Preliminaries Disctrete-time Linear Systems Stochastic Processes Kalman Filter Part II: Realization Theory Realization of Deterministic Problems Stochastic Realization Theory I Stochastic Realization Theory II Part III: Subspace Identification Subspace Identification I: ORT Subspace Identification II: CCA Identification of Closed-loop System Appendices Least-squares Method Input Signals for System Identification Overlapping Parametrization Matlab(R) Programs Solutions to Problems
235 citations
TL;DR: Development of the methanol synthesis recycle-loop model is described in detail, along with several case studies performed using the steady-state and dynamic models for better understanding of the process behavior.
48 citations
TL;DR: The empirical modeling using system identification technique with a focus on an interacting series process is carried out experimentally and the selected model is able to reproduce the main dynamic characteristics of the plant in open-loop and produces zero steady-state errors in closed-loop control system.
Abstract: This paper discusses the empirical modeling using system identification technique with a focus on an interacting series process. The study is carried out experimentally using a gaseous pilot plant as the process, in which the dynamic of such a plant exhibits the typical dynamic of an interacting series process. Three practical approaches are investigated and their performances are evaluated. The models developed are also examined in real-time implementation of a linear model predictive control. The selected model is able to reproduce the main dynamic characteristics of the plant in open-loop and produces zero steady-state errors in closed-loop control system. Several issues concerning the identification process and the construction of a MIMO state space model for a series interacting process are deliberated.
11 citations
Additional excerpts
...[18] considered the problem of developing a linear model from the input–output data that have the relation as given...
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01 Jan 2014
TL;DR: In this article, a real-time two-tank interacting level process is taken-up for study and the mathematical model of a two-totank interacting process is derived. And a Fuzzy Logic Controller (FLC) is designed and implemented to improve the performance of the closed loop.
Abstract: The control of liquid level in tanks and f low between tanks is a basic problem in the process industries. In vital industries such as petro-chemical industries, paper industries, water treatment industries have the interacting tank s which the processes of chemical or mixing treatment takes pla ce in the process tanks. Hence, the level of fluid in the tan ks and interaction between tanks must be controlled. It is essential fo r control system engineers to understand how interacting tanks control system works and how the level control problem is solved. Th e problem of level control in interacting tank processes are sys tem dynamics and interacting characteristics. In interacting pro cess dynamics of tank1 affects the dynamics of tank2 and vice versa because flow rate depends on the difference between the liquid le vels. In this work, a real-time two-tank interacting level process is taken-up for study. The mathematical model of a two-tank interacting process is derived. The hydraulic resistances (R1 and R2) are obtained using Experimental data. The servo and regulatory responses are obtained with PI controller. To improve the performance of the closed loop a Fuzzy Logic Controller (FLC) is designed and implemented for a two-tank interacting process. The servo and regulatory responses are obtained with FLC. The performances of Fuzzy Logic Controller are compared with PI controller in simulation. The performance measures are tabulated. It is observed from the results that the FLC out performs wi th no overshoot, faster settling time, better set-point t racking and thereby producing minimum integral square error(ISE). Keywords—Two-tank interacting process, PI controller and FLC.
7 citations
01 Jan 2018
TL;DR: A mathematical model of a real-time PEMFC is obtained and its quality is assessed using various validation techniques and validation procedures like recursive least square algorithm, ARX and ARMAX were employed to assess the model.
Abstract: A model is an input–output mapping that suitably explains the behavior of a system. Model helps to analyze the functionality of the system and to design suitable controllers. System identification builds model from experimental data obtained by exciting the process with an input and observing its response at regular interval (Wibowo et al. in System identification of an interacting series process for real-time model predictive control, American Control Conference, pp. 4384–4389, 2009). Fuel cells (FC) systems are a potentially good clean energy conversion technology, and they have wide range of power generation applications. Classification of fuel cells is based on the fuel and the electrolyte type used. The proton exchange membrane fuel cells (PEMFC) are portable devices with superior performance and longer life. They act as a good source for ground vehicle applications. They also possess high power density and fast start-up time. In this work, mathematical model of a real-time PEMFC is obtained and its quality is assessed using various validation techniques. The model is obtained using system identification tool in MATLAB, and validation procedures like recursive least square algorithm, ARX and ARMAX were employed to assess the model. Controllers such as PI and PID were employed in order to achieve the desired load current by controlling the hydrogen flow rate. The values of the gain constant, integral time and derivative time were obtained using Cohen-Coon method. PI and PID control schemes were implemented using SIMULINK in MATLAB environment, and the system response was observed.
6 citations
References
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Book•
01 Jan 1996
1,228 citations
"System identification of an interac..." refers background in this paper
...Related to (1), it is assumed that the system is asymptotically stable, the pair (A,C) is observable and the pair of (A,B) is controllable [4, 6, 7]....
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Book•
01 Jan 1995
TL;DR: In this paper, the Proportional-Integral-Derivative (PID) algorithm is used to tune a PID controller for dynamic performance. But, it is not suitable for non-linear processes.
Abstract: 1 Introduction to Process Control 2 Control Objectives and Benefits 3 Mathematical Modeling Principles 4 Modeling and Analysis for Process Control 5 Dynamic Behavior of Typical Process Systems 6 Empirical Model Identification 7 The Feedback Loop 8 The Proportional-Integral-Derivative (PID) Algorithm 9 PID Controller Tuning for Dynamic Performance 10 Stability Analysis and Controller Tuning 11 Digital Implementation of Process Control 12 Practical Application of Feedback Control 13 Performance of Feedback Control Systems 14 Cascade Control 15 Feedforward Control 16 Adapting Single-Loop Control Systems for Non-Linear Processes 17 Inferential Control 18 Level and Inventory Control 19 Single-Variable Model Predictive Control 20 Multiloop ControlEffects of Interaction 21 Multiloop Control Performance Analysis 22 Variable Structure and Constraint Control 23 Centralized Multivariable Control 24 Process Control Design Definition and Decisions 25 Process Control Design Managing the Design Procedure 26 Control for Product Quality and Profit Appendices
453 citations
TL;DR: A comparison between subspace identification and prediction error methods is made on the basis of computational complexity and precision of the methods by applying them on 10 industrial data sets.
346 citations
"System identification of an interac..." refers background in this paper
...As De Moor pointed out, the general algorithm of the subspace methods involves three major steps [8, 9]: 1....
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Book•
01 Jan 2002
TL;DR: This work presents a meta-modelling framework for model-predictive control that automates the very labor-intensive and therefore time-heavy and expensive process of modeling discrete-time models.
Abstract: Preface. Acknowledgements. 1. Introduction. 2. Continuous-Time Models. 3. One-Degree of Freedom Internal Model Control. 4. Two-Degree of Freedom Internal Model Control. 5. Model State Feedback Implementations of IMC. 6. PI and PID Parameters From IMC Designs. 7. Tuning and Synthesis of 1DF IMC for Uncertain Processes. 8 Tuning and Synthesis of 2DF IMC for Uncertain Processes. 9. Feedforward Control. 10. Cascade Control. 11. Output Constraint Control (Override Control). 12. Single Variable Inferential Control (IC). 13. Inferential Estimation Using Multiple Measurements. 14. Discrete-Time Models. 15. Identification: Basic Concepts. 16. Identification: Advanced Concepts. 17. Basic Model-Predictive Control. 18. Advanced Model-Predictive Control. 19. Inferential Model-Predictive Control. Appendix A. Review of Basic Concepts. Appendix B. Review of Frequency Response Analysis. Appendix C: Review of Linear Least-Squares Regression. Appendix D: Review of Random Variables and Random Processes. Appendix E: MATLAB and Control Toolbox Tutorial. Appendix F: SIMULINK Tutorial. Appendix G: Tutorial on IMCTUNE Software. Appendix H: Identification Software. Appendix I: SIMULINK Models for Projects. Index.
305 citations
"System identification of an interac..." refers background in this paper
...Model 2 Brosilow and Joseph [13] point out the key parameters that are needed to design a PRBS signal....
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...Brosilow and Joseph [13] point out the key parameters that are needed to design a PRBS signal....
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