A novel direct performance adaptive control of aero-engine using subspace-based improved model predictive control

Chen, Qian; Sheng, Hanlin; Zhang, Tianhong

doi:10.1016/j.ast.2022.107760

Cited by 28 publications

(6 citation statements)

References 24 publications

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“…5. According to equation (22), adjust the range of weights and constraints, and then switch them smoothly based on equation ( 23…”

Section: Comparison Of Smpc Performance On Different Datatraining Setsmentioning

confidence: 99%

“…The first step is to establish a high-fidelity predictive model. The datadriven methods such as neural network, [15][16][17] support vector machines, 18,19 random forests, 20 and subspace identification [21][22][23][24] are proposed in recent years. These methods learn the system information through input 1 and output data, which can refine the precision of predictive model.…”

Section: Introductionmentioning

confidence: 99%

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Digital twin-based subspace model predictive control for thermal power plant

Zhao

Cai

Jiang

2023

Proceedings of the Institution of Mechanical Engineers, Part I:

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In this article, a digital twin-based subspace model predictive control scheme is proposed for solving model mismatch caused by parameter perturbations of thermal power plants. First, the rational range of essential parameters in the nonlinear model is identified based on the nominal data of an actual 600 MW power plant, which provides necessary foundation for subsequent studies. Second, to improve the load-changing capacity under parameter disturbances, a novel digital twin-based subspace model predictive control algorithm is established by regarding the crucial parameter as extended input of predictive model. Finally, the small fluctuations during steady-state operation are effectively restrained through transforming weights and constraints slowly and smoothly at the end of load change. Simulation results illustrate that compared with the conventional subspace-based model predictive control, the proposed digital twin-based subspace model predictive control scheme can obtain a better performance in terms of both dynamic regulation rate and steady-state performance under parameter perturbations.

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“…5. According to equation (22), adjust the range of weights and constraints, and then switch them smoothly based on equation ( 23…”

Section: Comparison Of Smpc Performance On Different Datatraining Setsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Digital twin-based subspace model predictive control for thermal power plant

Zhao

Cai

Jiang

2023

Proceedings of the Institution of Mechanical Engineers, Part I:

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show abstract

“…A nonlinear state-space was used to build a predictive model, which employs an extended Kalman filter to estimate the present engine operating state. Chen et al [17] devised an innovative adaptive predictive control method using subspace-based improved model predictive control (SIMPC) to provide predictive control across all engine operating points. The results demonstrated that the engine's performance has been enhanced in comparison to the min/max limit controller.…”

Section: Introductionmentioning

confidence: 99%

Control system optimisation of biodiesel-based gas turbine for ship propulsion

Machmudah,

Bakar,

et al. 2024

IJ-AI

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<p>Reducing a gas emission of shipping transportations become a main goal of international maritime organization to achieve a clean energy. One of best scenarios to achieve this goal is to shift a fossil fuel to a renewable energy-based fuel of a ship propulsion. This paper studies an optimization of a control system of the renewable-based small gas turbine engine for the ship propulsion. Proposed control system consists of a proportional-integral with engine performance limiters to avoid an engine damage. Proportional-integral gains are tuned by a whale optimization algorithm. A gain scheduling analysis of a step response is performed to obtain a searching area of tuning parameters and values of constant gains. In this step, the gains are modeled as function of plant variables. After the searching area is obtained, the proportional-integral gains are optimized using the whale optimization algorithm while the additional gains are set as constant values. Using this scenario, stable and optimal gains have been successfully achieved. Results show that the proposed method has better performance than that of the previous methods, i.e. gain scheduling and gain scheduling optimized by the whale optimization algorithm. The proposed method has lowest fitness value and does not have an overshoot problem.</p>

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“…Chen et al [ 24 ] proposed a new direct performance adaptive predictive control based on a subspace-based improved model predictive control (SIMPC) to obtain the predictive control applicable for any engine range operation points. The proposed method reduced the calculation amount and ensured the estimation accuracy within their allowable operating points.…”

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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A novel direct performance adaptive control of aero-engine using subspace-based improved model predictive control

Cited by 28 publications

References 24 publications

Digital twin-based subspace model predictive control for thermal power plant

Digital twin-based subspace model predictive control for thermal power plant

Control system optimisation of biodiesel-based gas turbine for ship propulsion

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

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