Coordinated Model Predictive Control of Aircraft Gas Turbine Engine and Power System

Seok, Jinwoo; Kolmanovsky, Ilya; Girard, Anouck

doi:10.2514/1.g002562

Cited by 39 publications

(12 citation statements)

References 34 publications

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“…The first seven constraints are designed for safety protection, and the last two constraints are designed for performance optimization. To set up the engine constraints, the constraint inequality equation can be written as Equation (24). The surge margin on a high-pressure compressor is approximated from a linear function from the high-pressure shaft speed.…”

Section: Implementation and Resultsmentioning

confidence: 99%

“…On the other hand, constraints to the engine parameters can be easily implemented to the MPC, which overcomes the problem of handling the parameters in LQR [22]. In comparison with H_∞ and NN, the MPC is simpler, which does not require an accurate dynamic model to initialize transient operation, and the engine constraints can be directly embedded in the control process [23,24]. Therefore, the constrained MPC method is the most suitable one for gas turbine transient process control and performance optimization, as it is fully capable of determining the control inputs and seeking the optimal route based on the prediction of engine dynamics to optimally satisfy all the engine constraints [25].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Advanced Constraints Management Strategy for Real-Time Optimization of Gas Turbine Engine Transient Performance

Nikolaidis¹,

Li²,

Jafari³

2019

Applied Sciences

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Motivated by the growing technology of control and data processing as well as the increasingly complex designs of the new generation of gas turbine engines, a fully automatic control strategy that is capable of dealing with different aspects of operational and safety considerations is required to be implemented on gas turbine engines. An advanced practical control mode satisfaction method for the entire operating envelope of gas turbine engines is proposed in this paper to achieve the optimal transient performance for the engine. A constraint management strategy is developed to generate different controller settings for short-range fighters as well as long-range intercontinental aircraft engines at different operating conditions by utilizing a model predictive control approach. Then, the designed controller is tuned and modified with respect to different realistic considerations including the practicality, physical limitations, system dynamics, and computational efforts. The simulation results from a verified two-spool turbofan engine model and controller show that the proposed method is capable of maneuverability and/or fuel economy optimization indices while satisfying all the predefined constraints successfully. Based on the parameters, natural frequencies, and dynamic behavior of the system, a set of optimized weighting factors for different engine parameters is also proposed to achieve the optimal and safe operation for the engine at different flight conditions. The paper demonstrates the effects of the prediction length and control horizon; adding new constraints on the computational effort and the controller performance are also discussed in detail to confirm the effectiveness and practicality of the proposed approach in developing a fully automatic optimized real-time controller for gas turbine engines.

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Section: Implementation and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Advanced Constraints Management Strategy for Real-Time Optimization of Gas Turbine Engine Transient Performance

Nikolaidis¹,

Li²,

Jafari³

2019

Applied Sciences

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“…Progressive methodologies like robust control [30], linear quadratic control [31,32], and model predictive and fuzzy control [33,34] are able to produce controllers robust enough to cover a large spectrum of states and uncertainties; they are however often computationally too complex as shown in the references. Another approach, which has already been often used in solution of control problems, is to design simpler specific controllers for specific operational states of the investigated dynamic system for example in an application using different controllers for gas turbine generator and power system of aeropropulsion system as described in [38]. In aviation, this approach is widely used in flight control systems with a broad spectrum of applicable algorithms [39] combined in modern avionic digital control systems [40] as well as engine control system switching control algorithms for optimal dynamic characteristics [41].…”

Section: Situational Control Methodology Framework Designmentioning

confidence: 99%

Intelligent Situational Control of Small Turbojet Engines

Andoga

Főző

Judicak

et al. 2018

International Journal of Aerospace Engineering

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Improvements in reliability, safety, and operational efficiency of aeroengines can be brought in a cost-effective way using advanced control concepts, thus requiring only software updates of their digital control systems. The article presents a comprehensive approach in modular control system design suitable for small gas turbine engines. The control system is based on the methodology of situational control; this means control of the engine under all operational situations including atypical ones, also integrating a diagnostic system, which is usually a separate module. The resulting concept has been evaluated in real-world laboratory conditions using a unique design of small turbojet engine iSTC-21v as well as a state-of-the-art small turbojet engine TJ-100. Our results show that such advanced control system can bring operational quality of an engine with old turbocompressor core iSTC-21v on par with state-of-the-art engines. Dedicated to the memory of Ladislav Madarász

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“…Since the predictive properties and physical constraints can be directly included in the control framework, a model predictive controller is employed for the turbine engine system in [19], and the fuel and air flow are considered as physical constraints. The rate-based model for MPC design is introduced in [20], and errors between linear model predictions and the response of the actual nonlinear system is compensated by extra offset states. However, it is worth noting that the conclusions of above methods are mostly drawn from numerical simulations and have not been verified by any experimental test.…”

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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Coordinated Model Predictive Control of Aircraft Gas Turbine Engine and Power System

Cited by 39 publications

References 34 publications

Advanced Constraints Management Strategy for Real-Time Optimization of Gas Turbine Engine Transient Performance

Advanced Constraints Management Strategy for Real-Time Optimization of Gas Turbine Engine Transient Performance

Intelligent Situational Control of Small Turbojet Engines

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

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