Parametric and nonparametric system identification of an experimental turbojet engine

Tavakolpour-Saleh, A.R.; Nasib, S.A.R.; Sepasyan, A.; Hashemi, Seyed Mahdi

doi:10.1016/j.ast.2015.02.013

Cited by 38 publications

(12 citation statements)

References 12 publications

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“…Most subsystems associated with UAV, as well as the turbojet engine, can be viewed as nonlinear systems with strong variability [22,23]. The neural network (NN) has been recognized as an efficient means for nonlinear time-varying controller design because of its learning ability.…”

Section: Introductionmentioning

confidence: 99%

Single Neural Adaptive PID Control for Small UAV Micro-Turbojet Engine

Wei

Wang

et al. 2020

Sensors

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The micro-turbojet engine (MTE) is especially suitable for unmanned aerial vehicles (UAVs). Because the rotor speed is proportional to the thrust force, the accurate speed tracking control is indispensable for MTE. Thanks to its simplicity, the proportional–integral–derivative (PID) controller is commonly used for rotor speed regulation. However, the PID controller cannot guarantee superior performance over the entire operation range due to the time-variance and strong nonlinearity of MTE. The gain scheduling approach using a family of linear controllers is recognized as an efficient alternative, but such a solution heavily relies on the model sets and pre-knowledge. To tackle such challenges, a single neural adaptive PID (SNA-PID) controller is proposed herein for rotor speed control. The new controller featuring with a single-neuron network is able to adaptively tune the gains (weights) online. The simple structure of the controller reduces the computational load and facilitates the algorithm implementation on low-cost hardware. Finally, the proposed controller is validated by numerical simulations and experiments on the MTE in laboratory conditions, and the results show that the proposed controller achieves remarkable effectiveness for speed tracking control. In comparison with the PID controller, the proposed controller yields 54% and 66% reductions on static tracking error under two typical cases.

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Section: Introductionmentioning

confidence: 99%

Single Neural Adaptive PID Control for Small UAV Micro-Turbojet Engine

Wei

Wang

et al. 2020

Sensors

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“…There are two different methods to develop the control system depending on how to acquire the model of the plant to be controlled. The first method is the so-called black-box substitution mentioned by Tavakolpour-Saleh, Nasib, Sepasyan, and Hashemi (2015), which neglects the details of behavior and focuses solely on the system responses. The other one is to create a more or less complex model that is based on physical laws that describe the operation of the plant as noted by Tudosie (2012).…”

Section: Introductionmentioning

confidence: 99%

Linear Dynamic Mathematical Model and Identification of Micro Turbojet Engine for Turbofan Power Ratio Control

Derbel

Beneda

2019

Aviation

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Micro turbojets can be used for propulsion of civilian and military aircraft, consequently their investigation and control is essential. Although these power plants exhibit nonlinear behaviour, their control can be based on linearized mathematical models in a narrow neighbourhood of a selected operating point and can be extended by using robust control laws like H∞ or Linear Quadratic Integrating (LQI). The primary aim of the present paper is to develop a novel parametric linear mathematical model based on state space representation for micro turbojet engines and the thrust parameter being Turbofan Power Ratio (TPR). This parameter is used by recent Rolls-Royce commercial turbofan engines but can be applied for single stream turbojet power plants as well, as it has been proven by the authors previously. An additional goal is to perform the identification for a particular type based on measurements of a real engine. This model has been found suitable for automatic control of the selected engine with respect of TPR, this has been validated by simulations conducted in MATLAB® Simulink® environment using acquired data from transient operational modes.

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“…The works in [8,12] have made use of spline techniques for the design of adaptive filters and used for linear and nonlinear system identification. Parametric and nonparametric methods using neural feedforward networks have been adopted for the system identification of an experimental turbojet engine in [13]. A parameter identification method is proposed in [14], which makes use of limited measurement information to estimate the parameters of a photovoltaic array model and a sequential quadratic programming method for the problem is introduced.…”

Section: International Journal Of Computer Applications (0975 -8887)mentioning

confidence: 99%

System Identification of Thermal Process using Elman Neural Networks with No Prior Knowledge of System Dynamics

Ibraheem¹

2017

IJCA

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Dynamic Neural networks have been verified as identifiers due to its capability for manipulating processes in parallel and enduring noisy sorts of the input signals. They make them outstanding contenders for system identification applications. This paper presents a method for a black box system identification based on Elman neural network (ENN) for thermal process system to generate a prototype for the dynamical system of the thermal process without any past information about the system dynamics. This identification approach is compared to its counterpart conventional feedforward neural network (CFFNN) based system identification. The comparative simulations show that the ERNN gives an excellent results and outperforms the CFFNN in terms of accuracy with little degradation in the speed of convergence which make this neural network a motivating candidate for adaptive and gain scheduling controllers. General Termssystem identification, Recurrent neural networks, thermal process, feedforward neural network, mean square error.

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Parametric and nonparametric system identification of an experimental turbojet engine

Cited by 38 publications

References 12 publications

Single Neural Adaptive PID Control for Small UAV Micro-Turbojet Engine

Single Neural Adaptive PID Control for Small UAV Micro-Turbojet Engine

Linear Dynamic Mathematical Model and Identification of Micro Turbojet Engine for Turbofan Power Ratio Control

System Identification of Thermal Process using Elman Neural Networks with No Prior Knowledge of System Dynamics

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