Data-Driven Modeling and Design of Multivariable Dynamic Sliding Mode Control for the Underground Coal Gasification Project Thar

Javed, Syed Bilal; Utkin, Vadim I.; Uppal, Ali Arshad; Samar, Raza; Bhatti, Aamer Iqbal

doi:10.1109/tcst.2021.3057633

Cited by 8 publications

(6 citation statements)

References 29 publications

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“…In [28], the authors linearized the nonlinear model of [25], using Taylor series approximation around an operating point of interest and designed a linear matrix inequality (LMI) based optimal H 2 /H ∞ controller to keep the heating value on a desired level. In [29], [30], the authors have identified a linear model for the UCG process and then This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ This article has been accepted for publication in a future issue of this journal, but has not been fully edited.…”

Section: A Related Workmentioning

confidence: 99%

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Model Predictive Control and Adaptive Kalman Filter Design for an Underground Coal Gasification Process

Chaudhry¹,

Uppal

Bram

2021

IEEE Access

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The control of a nonlinear system such as an underground coal gasification (UCG) process is a challenging task. Several nonlinear design approaches are implemented to improve the tracking performance of the UCG process, however, the nonlinear techniques make implementation complex and computationally inefficient. In this work, a constrained linear model predictive control (MPC) is designed for the UCG process to track the desired trajectory of the heating value, while satisfying actuator constraints pertaining to UCG. The unknown states required for MPC design are reconstructed by using linear adaptive Kalman filter (AKF) and unscented Kalman filter (UKF). The design of MPC and AKF is based on the quasi-linear model of UCG process. A fair comparison is conducted between different control strategies, which include MPC-AKF, MPC-UKF, MPC-gain scheduled modified Utkin observer (GSMUO) and dynamic integral sliding mode control (DISMC)-GSMUO. The quantitative analysis and simulation results show that MPC-AKF outperforms its counterparts by yielding the least tracking error and average control energy. This conclusion holds, even in the presence of an external disturbance, parametric variations, and measurement and process noises. Moreover, MPC-AKF yields 51%, 44% and 46% improvement in absolute relative rootmean-squared error with reference to MPC-UKF, MPC-GSMUO and DISMC-GSMUO, respectively. A quantitative analysis has also been carried for AKF and UKF, which shows that the performance of AKF is more robust against the changes in the initial values of measurement and process covariances.INDEX TERMS Model predictive control (MPC); underground coal gasification (UCG); adaptive Kalman filter (AKF); unscented Kalman filter (UKF); energy conversion systems.

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Section: A Related Workmentioning

confidence: 99%

“…Furthermore, both the controllers are implemented on the cavity simulation model (CAVSIM) of [31]. As in [28]- [30] the control design is based on linear models, so the controllers are only effective in a limited operating range.…”

Section: A Related Workmentioning

confidence: 99%

Model Predictive Control and Adaptive Kalman Filter Design for an Underground Coal Gasification Process

Chaudhry¹,

Uppal

Bram

2021

IEEE Access

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“…For instance, Javed et al employed a subspace system identification technique to identify the linear multivariable model, which can enhance the efficiency of the underground coal gasification process. 20 Based on the maximum likelihood principle, Mattsson et al tackled the identification problems for multivariable nonlinear systems by using the majorization-minimization approach. 21 Bai proposed a separable least squares identification algorithm and a correlation analysis-based identification algorithm for a nonlinear system with hard input nonlinearity.…”

Section: Introductionmentioning

confidence: 99%

“…When the physical mechanism are unknown or unclear, system identification provides a way to model the process through input–output data 19 and achieves many fruitful results. For instance, Javed et al employed a subspace system identification technique to identify the linear multivariable model, which can enhance the efficiency of the underground coal gasification process 20 . Based on the maximum likelihood principle, Mattsson et al tackled the identification problems for multivariable nonlinear systems by using the majorization‐minimization approach 21 .…”

Section: Introductionmentioning

confidence: 99%

A coupled recursive least squares algorithm for multivariable systems and its computational amount analysis by using the coupling identification concept

Jin,

Ding

2023

Adaptive Control & Signal

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SummaryIn order to solve the problem of the parameter identification for large‐scale multivariable systems, which leads to a large amount of computation for identification algorithms, two recursive least squares algorithms are derived according to the characteristics of the multivariable systems. To further reduce the amount of computation and cut down the redundant estimation, we propose a coupled recursive least squares algorithm based on the coupling identification concept. By coupling the same parameter estimates between sub‐identification algorithms, the redundant estimation of the subsystem parameter vectors are avoided. Compared with the recursive least squares algorithms, the proposed algorithm in this article have higher computational efficiency and smaller estimation errors. Finally, the simulation example tests the effectiveness of the algorithm.

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“…In References 28,29, linear models were developed to predict the carbon capture levels of PCC. Another recent linear model was implemented with subspace identification to develop an H ∞ multi‐variable controller for underground coal gasification (UCG) unit, 30 as well as previously proposed models based on the robust linearized multi‐objective H 2 /H ∞ controller that is intended to keep the heating value of syngas constant for UCG 31,32 . Znad et al 33 have presented a novel model predictive control strategy to speed up the start‐up process of a 600 MW SCPP based on a subspace state‐space identified linear model with a multi‐input single‐output structure which proved to help in more savings in fuel and water flows, and hence, fewer emissions.…”

Section: Introductionmentioning

confidence: 99%

Qualitative and quantitative comparison of three modeling approaches for a supercritical once‐through generation unit

Haddad

Mohamed

2022

Intl J of Energy Research

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Summary With the affirmative advancements in clean coal technologies toward more environmentally friendly power systems, it has become critical to gain a comprehensive understanding of the modeling philosophies of such technologies. Supercritical (SC) and ultra‐supercritical (USC) technologies are among the leading options of clean coal technologies, and it is widely acknowledged that they provide flexible power generation for grid electrical demand while sustaining cleaner operations. This paper presents detailed qualitative and quantitative comparisons of the most salient modeling methodologies of a 600 MW SC generation unit which study the once‐through operation to provide future researchers with a better understanding of this field and be used as a reference for more advanced strategies and decision‐making problems, such as efficiency improvements, emissions reduction, and control design for industry personnel, by determining the key characteristics and drawbacks of each modeling technique. These methods are first‐principles or physics‐based modeling, state‐space system identification (SI) modeling using prediction error minimization, and artificial neural networks (ANN) modeling using the non‐linear autoregressive exogenous input (NARX). The comparison was found to be effective in recognizing the diversity in the computational capabilities of the three developed models. The simulation errors for the adopted output responses are presented to quantify the comparison and justify the superiority and imperfection of each method in terms of accuracy. Other qualitative factors for comparison include simplicity and physics‐based sagacity. It has been eventually deduced that in terms of accuracy, the ANN‐based model using NARX is the most accurate model and has the best performance for black‐box modeling, whereas the linearized state‐space model using SI modeling is the most suitable one for designing linear controllers, and the physical model is superior for physical interpretation and stability studies. The control applications of each technique have been highlighted as well. Highlights Three different models are presented for a 600 MW cleaner SC boiler‐turbine‐generator unit that covers the once‐through mode. A comprehensive realization of the three modeling techniques is presented. Simulation results have shown the suitability of the three methods for this specific application with different capabilities, computational burdens, and possible control methods. A comparative study is then reported and discussed in detail, which can be used as a future educational guide for future researchers.

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Data-Driven Modeling and Design of Multivariable Dynamic Sliding Mode Control for the Underground Coal Gasification Project Thar

Cited by 8 publications

References 29 publications

Model Predictive Control and Adaptive Kalman Filter Design for an Underground Coal Gasification Process

Model Predictive Control and Adaptive Kalman Filter Design for an Underground Coal Gasification Process

A coupled recursive least squares algorithm for multivariable systems and its computational amount analysis by using the coupling identification concept

Qualitative and quantitative comparison of three modeling approaches for a supercritical once‐through generation unit

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