Robust tracking of the heating value in an underground coal gasification process using dynamic integral sliding mode control and a gain-scheduled modified Utkin observer

Uppal, Ali Arshad; Butt, Saif Siddique; Khan, Qudrat; Aschemann, Harald

doi:10.1016/j.jprocont.2018.11.005

Cited by 19 publications

(20 citation statements)

References 18 publications

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“…In [25], it has been assumed that all the states of the system are measurable, which is not the case practically. This discrepancy has been removed in [26], [27]. In [26], a gain scheduled modified Utkin observer (GSMUO) along with an integral SMC have been proposed to robustly track the desired trajectory of the heating value.…”

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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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%

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

Chaudhry¹,

Uppal

Bram

2021

IEEE Access

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“…In this article, a special nonlinear plant will be used in which the outlets have a linear combination with the variables, the variables have a nonlinear combination with the variables, and the perturbations are entered additively. The nonlinear plant is [25], [26]:…”

Section: The Estimator For the Variables And Perturbations Estimationmentioning

confidence: 99%

“…Our estimator is applied for the variables and perturbations estimation in the gas turbine and gasification plants. The gas turbine plant is used for the electrical energy generation from the gas [25], while the gasification plant is used for the gas generation from biomass [26].…”

Section: Introductionmentioning

confidence: 99%

The Perturbations Estimation in Two Gas Plants

et al. 2020

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The perturbations are the unwanted and unknown inlets in nonlinear plants which can affect the outlets. In this article, an estimator is studied for the variables and perturbations estimation in nonlinear plants. The saturation map is used in our estimator instead of the signum map to decrease the chattering, and we ensure the estimator convergence by the Lyapunov analysis. The conditions required by our estimator gains are found to reach the variables error convergence, and these gains are used for the perturbations estimation. An algorithm is proposed to choose the gains for achieving a satisfactory performance in our estimator. The studied estimator is applied for the variables and perturbations estimation in the gas turbine and gasification plants.

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“…Assume, there is a fault occurring between systems (16) and (18), then TPS errors are e 1 = x 2 − q 1 x 1 , e 2 = x 3 − q 2 x 2 and e 3 = x 1 − q 3 x 3 , where q 1 q 2 q 3 = 1 and u 1 = 0 in e 3 . Therefore…”

Section: Case IV (Unknown System Parameters With a Fault)mentioning

confidence: 99%

“…NowṠ =ė 1 + 2ė 2 +ė 3 = e 2 + 2e 3 + v. By choosing v = −e 2 − 2e 3 − ks, k > 0, we haveṠ = −kS, therefore S → 0 which gives e 1 , e 2 , e 3 → 0. Case II (Known System Parameters With a Fault): Assume that there exists a fault between the systems(16) and(18), then the TPS errors x 2 −q 1 x 1 , e 2 = x 3 −q 2 x 2 and e 3 = x 1 −q 3 x 3 , where q 1 q 2 q 3 = 1. We set u 1 = 0 in e 3 so that     ė…”

mentioning

confidence: 99%

Transmission Projective Synchronization of Multiple Non-Identical Coupled Chaotic Systems Using Sliding Mode Control

et al. 2019

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This paper presents the control design method for the transmission projective synchronization (TPS) of the Multiple Non-identical Coupled Chaotic systems using sliding mode. A total of four different cases of chaotic systems are studied which are: 1) systems with known parameters without fault; 2) systems with known parameters with a fault; 3) systems with unknown parameters without fault; and 4) systems with unknown parameters and fault occurrence. In first and third cases, the controllers are designed using sliding mode, and adaptive integral sliding mode (AISM) is used to design controllers for the second and fourth cases. To employ AISM, the error dynamics is broken into a structure comprising a nominal and some unknown part, which are adaptively computed. The error dynamics are stabilized by AISM control which consists of a nominal and a compensator control. To avoid the chattering phenomenon, smooth continuous compensator control is used instead of the traditional discontinuous control. The stability of adaptive law and compensator is derived using a Lyapunov function which becomes strictly negative. Finally, the simulations of a numerical example verify the TPS behavior. INDEX TERMS Multiple coupled chaotic system, transmission projective synchronization, adaptive integral sliding mode control, and Lyapunov function. Recently, the synchronization of MCCS has attained a considerable attention from research community due to its

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Robust tracking of the heating value in an underground coal gasification process using dynamic integral sliding mode control and a gain-scheduled modified Utkin observer

Cited by 19 publications

References 18 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

The Perturbations Estimation in Two Gas Plants

Transmission Projective Synchronization of Multiple Non-Identical Coupled Chaotic Systems Using Sliding Mode Control

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