Control performance enhancement of gas turbines in the minimum command selection strategy

Eslami, Mostafa; Banazadeh, Afshin

doi:10.1016/j.isatra.2020.12.003

Cited by 4 publications

(4 citation statements)

References 36 publications

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“…For example, the advantage of the data for the enhancement of the MBC system performance in an industrial engine is evident. 22 Looking at breakthroughs in control engineering, the direct adaptive control 23,24 and neural-network-based control systems [25][26][27] are in the same paradigm of the DDC systems. After overwhelming use of neural networks in control systems design, the data-driven controllers were introduced and are progressively developing.…”

Section: Introductionmentioning

confidence: 99%

“…Today's progressive increase in computational capacity has introduced new visions for the evolution of control systems with the assistance of data to extend model‐based capabilities. For example, the advantage of the data for the enhancement of the MBC system performance in an industrial engine is evident 22 . Looking at breakthroughs in control engineering, the direct adaptive control 23,24 and neural‐network‐based control systems 25‐27 are in the same paradigm of the DDC systems.…”

Section: Introductionmentioning

confidence: 99%

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Data‐assisted control: A framework development by exploiting NASA Generic Transport platform

Eslami,

Banazadeh

2023

Intl J Robust & Nonlinear

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The increasing capabilities of control systems, driven by abundant data and computational resources, necessitate the incorporation of data‐based techniques with model‐based approaches. While mathematical models exist for air and space vehicles, they may not fully meet the requirements for expanding control system capabilities. Consequently, the development of data‐based extensions in collaboration with these models becomes crucial to provide specific analysis and synthesis tools in the hybrid realm for practical cases. This article introduces the data‐assisted control (DAC) framework for aerospace vehicles, with the NASA generic transport model serving as the platform for study. By leveraging data, the model‐based controller enhances performance during a damage event. Real‐time decisions are made to update the control law based on information obtained from the data, while the model‐based controller may not exhibit consistent performance. The closed‐loop system is shown to be stable in the transition phase between the data and the model, and robust against uncertainties. The dual unscented Kalman filter is employed to estimate fixed dynamic parameters and generalized force‐moment, and the Koopman estimator is used to estimate the evolution equation of the estimated forces and moments. The dual estimation model ensures observability by considering the persistency of excitation. Simulations have shown that the purely model‐based robust control leads to degradation of the closed‐loop performance in case of damage, suggesting the need for data assistance. The DAC framework offers a robust approach to integrate data and models, significantly enhancing control performance in practical aerospace applications.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data‐assisted control: A framework development by exploiting NASA Generic Transport platform

Eslami,

Banazadeh

2023

Intl J Robust & Nonlinear

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“…Eslami and Banazadeh [ 23 ] addressed the problem of severe performance loss at the switching of the controller command. They proposed new heuristic approaches, namely α-method, ζ–method, and ϵ-method, to enhance the performance of the GT minimum command selection (MCS) control technique.…”

Section: Introductionmentioning

confidence: 99%

Design Optimization of a Gas Turbine Engine for Marine Applications: Off-Design Performance and Control System Considerations

Machmudah

Lemma

Solihin

et al. 2022

Entropy

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This paper addresses a design optimization of a gas turbine (GT) for marine applications. A gain-scheduling method incorporating a meta-heuristic optimization is proposed to optimize a thermodynamics-based model of a small GT engine. A comprehensive control system consisting of a proportional integral (PI) controller with additional proportional gains, gain scheduling, and a min-max controller is developed. The modeling of gains as a function of plant variables is presented. Meta-heuristic optimizations, namely a genetic algorithm (GA) and a whale optimization algorithm (WOA), are applied to optimize the designed control system. The results show that the WOA has better performance than that of the GA, where the WOA exhibits the minimum fitness value. Compared to the unoptimized gain, the time to reach the target of the power lever angle is significantly reduced. Optimal gain scheduling shows a stable response compared with a fixed gain, which can have oscillation effects as a controller responds. An effect of using bioethanol as a fuel has been observed. It shows that for the same input parameters of the GT dynamics model, the fuel flow increases significantly, as compared with diesel fuel, because of its low bioethanol heating value. Thus, a significant increase occurs only at the gain and depends on the fuel flow.

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“…This study allows for the analysis of the turbine condition monitoring system and reduces maintenance costs, while also improving the efficiency of the turbine control system under review. Also, Yang et al (2021) performed a thermodynamic modeling for the development Merouane Alaoui, Obaid S. Alshammari, Abdelhamid Iratni, Ahmed Hafaifa, Houssem Jerbi of a real-time control strategy applied to a solar micro gas-turbine and Eslami & Banazadeh (2021) improved the control performance for gas turbines in order to reverse performance losses with the selection of minimum control actions, while analyzing the various factors that triggered the aging and degradation of the examined gas turbine, in order to lengthen its useful life. Hence, they have improved the control loop control dynamics limitations optimally, in comparison with the conventional control techniques.…”

Section: Introductionmentioning

confidence: 99%

Gas Turbine Speed Monitoring Using a Generalized Predictive Adaptive Control Algorithm

ALAOUI¹,

Alshammari²,

Iratni³

et al. 2022

STUD INFORM CONTROL

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The outstanding performance of modern gas turbine power plants requires an efficient and robust control strategy in order to recover the losses caused by the undesirable effects and the instabilities related to their operation. Also, these losses can be the cause of their unstable dynamic behaviour during full-load and partial load operation in the unplanned shutdown state. Hence, choosing the right control strategy can improve the operation of this type of machine by increasing its efficiency by up to 60%. This paper proposes the implementation of an innovative control strategy based on a generalized predictive adaptive control algorithm for monitoring the rotation speed of a Solar Titan 130 gas turbine, with the purpose of considering the constraints related to the nonlinear behavior of a turbine during large variations in machine load. The aim is to automatically adjust the parameters of the regulator of the turbine's control loop in real time, with a recursive estimation of these parameters. Using the experimental input/output measurements in order to study its behavior, by integrating the predictive control estimators, ensures the superior performance of the analyzed gas turbine in terms of energy, efficiency and robustness regarding the parametric uncertainties and the variation of the turbine load.

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Control performance enhancement of gas turbines in the minimum command selection strategy

Cited by 4 publications

References 36 publications

Data‐assisted control: A framework development by exploiting NASA Generic Transport platform

Data‐assisted control: A framework development by exploiting NASA Generic Transport platform

Design Optimization of a Gas Turbine Engine for Marine Applications: Off-Design Performance and Control System Considerations

Gas Turbine Speed Monitoring Using a Generalized Predictive Adaptive Control Algorithm

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