Model-based deduction learning control: A novel method for optimizing gas turbine engine afterburner transient

Feng, Hailong; Liu, Bei; Xu, Maojun; Li, Ming; Song, Zhiping

doi:10.1016/j.energy.2024.130512

Energy

2024

DOI: 10.1016/j.energy.2024.130512

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Model-based deduction learning control: A novel method for optimizing gas turbine engine afterburner transient

Hailong Feng,

Bei Liu,

Maojun Xu

et al.

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Cited by 5 publications

References 26 publications

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Ding

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Fast uncertainty assessment of in-service thrust control for turbofan engines: An equivalent model using Taylor expansion

Wei,

Zhang,

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Incipient instability real-time warning via adaptive wavelet synchrosqueezed transform: Onboard applications from compressors to gas turbine engines

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Onboard Neuro-Fuzzy Adaptive Helicopter Turboshaft Engine Automatic Control System

Vladov,

Bulakh,

Vysotska

et al. 2024

Energies

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A modified onboard neuro-fuzzy adaptive (NFA) helicopter turboshaft engine (HTE) automatic control system (ACS) is proposed, which is based on a circuit consisting of a research object, a regulator, an emulator, a compensator, and an observer unit. In this scheme, it is proposed to use the proposed AFNN six-layer hybrid neuro-fuzzy network (NFN) with Sugeno fuzzy inference and a Gaussian membership function for fuzzy variables, which makes it possible to reduce the HTE fuel consumption parameter transient process regulation time by 15.0 times compared with the use of a traditional system automatic control (clear control), 17.5 times compared with the use of a fuzzy ACS (fuzzy control), and 11.25 times compared with the use of a neuro-fuzzy reconfigured ACS based on an ANFIS five-layer hybrid NFN. By applying the Lyapunov method as a criterion, its system stability is proven at any time, with the exception of the initial time, since at the initial time the system is in an equilibrium state. The use of the six-layer ANFF NFN made it possible to reduce the I and II types of error in the HTE fuel consumption controlling task by 1.36…2.06 times compared with the five-layer ANFIS NFN. This work also proposes an AFNN six-layer hybrid NFN training algorithm, which, due to adaptive elements, allows one to change its parameters and settings in real time based on changing conditions or external influences and, as a result, achieve an accuracy of up to 99.98% in the HTE fuel consumption controlling task and reduce losses to 0.2%.

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Model-based deduction learning control: A novel method for optimizing gas turbine engine afterburner transient

Cited by 5 publications

References 26 publications

Fast uncertainty assessment of in-service thrust control for turbofan engines: An equivalent model using Taylor expansion

Fast uncertainty assessment of in-service thrust control for turbofan engines: An equivalent model using Taylor expansion

Incipient instability real-time warning via adaptive wavelet synchrosqueezed transform: Onboard applications from compressors to gas turbine engines

Onboard Neuro-Fuzzy Adaptive Helicopter Turboshaft Engine Automatic Control System

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