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Journal of Mechanical Science and Technology

, Volume 33, Issue 11, pp 5483–5498 | Cite as

Comparison of model predictive controller and optimized min-max algorithm for turbofan engine fuel control

  • Morteza Montazeri-GhEmail author
  • Ali Rasti
Article
  • 8 Downloads

Abstract

Min-max selector structure is traditionally used as the industrial control architecture of commercial turbofan engines. However, recent studies indicate that this structure with linear compensators suffers from lack of safety guarantee in fast demands. On the other hand, model predictive control (MPC) technique, which incorporates input/output constraints in its optimization process, has the potential to fulfill the control requirements of an aircraft engine. In this paper, a practical approach is performed for design and optimization of the turbofan engine controller through a comparative study where all control modes and requirements have been taken into account simultaneously. For this purpose, a thermodynamic nonlinear model is firstly developed for the turbofan engine. The linear regulators of minmax structure are then optimized via genetic algorithm (GA). The MPC technique is formulated based on the proper discrete-time linearized state-space models at desired operating points with real-time optimization, in which the MPC tuning horizons are obtained through GA optimization procedure. The both controllers are implemented on appropriate hardware taking the real-time aspects into account. Finally, a hardware in the loop (HIL) platform is developed for the turbofan engine electronic control unit (ECU) testing. The software and HIL simulation results confirm that MPC improves the response time of the system in comparison with min-max algorithm and guarantees the engine limit protection. This study demonstrates competitive advantages of MPC in terms of limit protection assurance and fast response, despite more computational burden.

Keywords

Turbofan engine Thermodynamic model Model predictive control Optimal MPC horizons Optimized min-max algorithm HIL simulation 

Nomenclature

A, B, C, D

Matrices of the linear state-space model

bi

The value of the ith limit

e

The white-noise disturbance

F, Φ

Matrices of the predicted outputs equation

f1

The look-up table of the compressor map

f3

The look-up table of the turbine map

f2, f4, g

The tables of the thermodynamic characteristics

h

Enthalpy

H

Fuel heating value

HPC

High pressure compressor

HPC-SM

High pressure compressor stall margin

HPS

High pressure spool

HPT

High pressure turbine

I

Identity matrix

J

Objective function

JLP, JHP

Moment of inertia of low pressure and high pressure shafts

k

Time step

Ki

Min-max compensators

LPC

Low pressure compressor

LPS

Low pressure spool

LPT

Low pressure turbine

M

Mach number

M, N, Q

Matrices of the augmented model of MPC

m

The number of limits

NLP, NHP

Speed of low pressure and high pressure shafts

LP, HP

Angular acceleration of low pressure and high pressure shafts

nu, ny

Control and prediction horizons

P

Pressure

PF

Penalty factor

PLA

Power lever angle

PR

Pressure ratio

Ps3

High pressure compressor discharge static pressure

PW

Power

r

Reference trajectory

t

Time

T

Temperature

T45

High pressure turbine exit total temperature

u

Velocity

u

Vector of control variables

û

Vector of predicted inputs

Umin, Umax

Vector of lower and upper bounds of the inputs

Wf

Fuel flow rate

Wf/Ps3, RU

Ratio unit limiter

wi

Weighting values

x

Vector of state variables

y

Vector of output variables

ŷ

Vector of predicted outputs

Ymin, Ymax

Vector of lower and upper bounds of the outputs

η

Efficiency

λ

Scalar weighting factor

Subscripts

a

Augmented matrix

acc

Acceleration

amb

Ambient

b

Burner

c

Corrected

con

Controlled

d

Discrete time

dec

Deceleration

e

Exit

f

Fuel

_g

Gas

HP

High pressure

in

Intlet

LP

Low pressure

out

Outlet

s

Static

std

Standard atmospheric condition

t

Total

Superscripts

T

Transpose

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Copyright information

© KSME & Springer 2019

Authors and Affiliations

  1. 1.Systems Simulation and Control Laboratory, School of Mechanical EngineeringIran University of Science and Technology (IUST)TehranIran

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