Abstract
This paper presents a fault-tolerant control scheme for the sensor fault in the acceleration process of the variable cycle engine. Firstly, an adaptive equilibrium manifold model with multiple inputs and multiple outputs is established. Combined with the Kalman filter bank, sensor fault diagnosis is carried out to realize the diagnosis and signal reconstruction of the engine in the case of a single sensor and double sensor faults. On this basis, isolation and group isolation are used to diagnose sensor faults and reconstruct signal in speed closed-loop control. Then, the control plan of the acceleration process is optimized based on the target shooting method, aiming to simulate the variation of various variables in the engine acceleration process more accurately, so as to verify the feasibility of the sensor fault-tolerant control scheme. Finally, a hardware-in-loop simulation platform is built based on the idea of distributed control, and the fault-tolerant control scheme of the sensor proposed previously is verified based on this platform. The results show that the proposed scheme can accurately diagnose the sensor faults and reconstruct the signal within 0.2 s, and the actual speed can rise from 67.87% to 99.9% in 4 s, ensuring the safe and rapid completion of the acceleration process.
Highlights
As the “brain” of an aeroengine, the role of the control system is to make the engine work stably and reliably under any changing conditions and give full play to its performance benefits [1]
It has the characteristic of variable parameter of linear parameter varying (LPV) model whose coefficient matrix all changes with the working state, and the introduction of Equation (4) makes the model always change near the equilibrium manifold, which overcomes the deficiency of steadystate error in LPV model
An Fault-tolerant control (FTC) scheme for the sensor fault in the transition process of variable cyclean engine (VCE) based
Summary
As the “brain” of an aeroengine, the role of the control system is to make the engine work stably and reliably under any changing conditions and give full play to its performance benefits [1]. Since the end of the last century intelligent algorithms have been applied in sensor fault diagnosis and FTC and improve engine control reliability, which can detect and distinguish between hard andsystems soft successfully verified in turbojet, turbofan and other engines [7,8]. Sheng et al proposed an establishing a system model [14] These latest research results are mostly combined with intelligent FTC system based on an Online Sequential Extreme Learning Machine (OS-ELM).
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