# Fuzzy Controller Structures Investigation for Future Gas Turbine Aero-Engines

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## Abstract

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_{2}emissions per passenger kilometer, a 90% reduction in NOx emissions, and 65% reduction in noise emission of flying aircraft relative to the capabilities of typical new aircraft in 2000). In order to meet these requirements, aircraft engines should work very close to their operating limits. Therefore, the importance of advanced control strategies to satisfy all engine control modes simultaneously while protecting them from malfunctions and physical damages is being more crucial these days. In the last three decades, fuzzy controllers (FCs) have been proposed as a high potential solution for performance improvement of the next generation of aircraft engines. Based on an analytic review, this paper divides the trend of FCs design into two main lines including pure FCs (PFC) and min–max FCs (MMFC). These two main architectures are then designed, implemented on hardware, and applied in a case study to analyze the advantages and disadvantages of each structure. The analysis of hardware-in-the-loop (HIL) simulation results shows that the pure FC structure would be a high potential candidate for maneuverability and response time indices improvement (e.g., military applications); while min–max FC architecture has a great potential for future civil aero-engines where the fuel consumption and steady-state responses are more important. The simulation results are also compared with those of industrial min–max controllers to confirm the feasibility and reliability of the fuzzy controllers for real-world application. The results of this paper propose a general roadmap for fuzzy controllers’ structure selection for new and next generation of aircraft engines.

## 1. Introduction

- Hydro-mechanical fuel control, which consists of a simple mechanical actuator controlled by the operator. In other words, in this embodiment, GTEs are manipulated by hydro-mechanical control systems.
- Hydro-mechanical/electronic fuel control, which is the former fuel flow controller with added an electronic control unit. This electronic unit performed the function of thrust setting, speed governing, and acceleration and deceleration in response to power lever inputs.
- Digital electronic engine control (DEEC), in this embodiment, functions carried out after input data from the airframe and engine were processed by the DEEC computer included setting the variable vanes, positioning compressor start bleeds, controlling gas-generator, adjusting the augmenter segment sequence valve, and controlling the exhaust nozzle position.
- Full authority digital engine (or electronic) control (FADEC), works by receiving multiple input variables of the current flight condition including air density, throttle lever position, engine temperatures, engine pressures, and many other parameters. The inputs are received by the electronic engine controller (EEC) and analyzed up to 70 times per second. Engine operating parameters such as fuel flow, stator vane position, air bleed valve position, and others are computed from this data and applied as appropriate. FADEC also controls engine starting and restarting procedures. The FADEC’s basic purpose is to provide optimum engine efficiency for a given flight condition [1].

- The first structure uses pure fuzzy control (PFC) strategy in which all control rules and loops are replaced by fuzzy rules.
- In the second structure, the controller keeps the industrial min–max structure in which the winner of different control loops will be selected by a pre-defined min–max strategy. However, control loops will be replaced by a fuzzy logic controller to result in a min–max fuzzy controller (MMFC).

## 2. PFC and MMFC Design

- Pure fuzzy controller (PFC)
- Min–max fuzzy controller (MMFC)

#### 2.1. Pure Fuzzy Controller (PFC) Design

#### 2.2. MIN–MAX Fuzzy Controller (MMFC) Design

- Steady-state control mode to meet pilot thrust level requirement.
- Transient control mode to reach the required thrust in a proper time.
- Physical limitations control mode to prevent the engine from damages and malfunctions (e.g., over-speed, over-temperature, surge, stall, etc.).

- PLA control loop: this loop has to supply the pilot desired thrust in each situation.
- Maximum speed limitation loop (MSLL): this loop is to prevent the engine from exceeding the rotor speed from the permissible amount. This control loop takes this responsibility to guarantee the integrity of the GTE.
- Maximum acceleration limitation loop (MALL): at the primary acceleration time abrupt fuel injection is the main cause of aerodynamic instability (surge and stall). MALL loop protects the engine against this fault.
- Maximum deceleration limitation loop (MDLL): at the primary sharp deceleration time control system must prevent fuel flow from abruptly reducing because the rotor inertia could lead to flame burnout. Therefore, the fuel flow reducing rate must be limited.

## 3. Hardware Implementation

#### 3.1. Experimental Apparatus

#### 3.2. Initial Preparation

#### 3.2.1. PFC Controller Hardware Implementation

#### 3.2.2. MMFC Hardware Implementation

#### 3.3. Hardware in the Loop Simulation

- The controller could be modeled in the software to be running on target computer hardware while it is connected to your physical plant or system. (The target computer hardware acts as the controller.).
- The other option is to implement the controller on the hardware, which can include production or embedded controls implementation, using a simulation of your plant or system. (Here, the target computer acts as a physical plant or system.).

- Simulation without time limitations;
- Real-time simulation;
- Simulation faster than real-time.

## 4. Results Analysis

## 5. Conclusions

- The pure fuzzy controller structure performs better in terms of pilot command tracking and, therefore, it is an appropriate candidate for control of the next generation of military aero-engines.
- The min–max Fuzzy controller structure performs better from fuel consumption and economic points of view that makes it a strong candidate for the next generation of civil aero-engines.
- Both fuzzy controller structures are feasible for real-world application and perform better than the conventional min–max controller in terms of fuel economy.

## Author Contributions

## Funding

## Conflicts of Interest

## Appendix A

#### Appendix A.1. Activating Codes for Calculating Time (Each Interrupt Overflow Led to Read Serial Port)

#### Appendix A.2. These Codes Activate Serial Port for Receive and Send Data to PC

#### Appendix A.3. Gaussian Input Membership Function

#### Appendix A.4. Defuzzification----Weighted Average Method

#### Appendix A.5. Sample PLA Membership Function

#### Appendix A.6. Input Membership Functions for Accdcc

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**Figure 1.**Schematic of single spool turbojet engine (image adopted from [20]).

**Figure 13.**The schematic of novel generalized describing function (NGDF) for modeling jet engines [38]

**Figure 14.**The schematic and characteristics of the modelled turbojet engine (images from [43]).

**Figure 15.**Modelling a turbojet engine with different reduced-order approaches: (

**a**) normalized compressor pressure ratio (CPR) tracking; (

**b**) normalized rotor speed tracking.

**Figure 16.**Model in the loop (MIL) and hardware in the loop (HIL) shaft rotational speed (RPM) signal comparison for PFC.

**Figure 23.**Fuel consumption comparison between PFC, MMFC and the MIN–MAX controller in the HIL test.

Paper Title | Main Achievement | Publication Year |
---|---|---|

Fuzzy Computing for Control of Aero Gas Turbine Engines | Certain stipulations, rules, and fuzzy logic are suggested for the control of a single spool aero gas turbine (pure fuzzy) | 1994 [12] |

Fuzzy Scheduling Control of a Gas Turbine Aero-Engine: A Multi-objective Approach | Combination of fuzzy logic and evolutionary algorithms (EA) to refine the control performance and to increase the flexibility of GTEs (pure fuzzy) | 2002 [13] |

Fuzzy Fuel Flow Selection Logic for a Real-Time Embedded Full Authority Digital Engine Control | In order to achieve proper performance, Typical control loops chosen by min–max theory are replaced by fuzzy logic loops (min–max fuzzy) | 2003 [14] |

Advanced Control of Turbofan Engines | Different control loops for turbofan engine control modes are designed and analyzed based on industrial min–max strategy and improved by fuzzy rules with respect to the implementation considerations (min–max fuzzy) | 2012 [15] |

Heavy-duty gas turbine monitoring based on adaptive neuro-fuzzy inference system: speed and exhaust temperature control | Using an adaptive neuro-fuzzy inference system (ANFIS) to maintain turbine operation at optimum performance. The results obtained, based on the use of the Rowen model, show the effectiveness of the proposed system (pure fuzzy) | 2017 [16] |

Design of an Interval Fuzzy Type-2 PID Controller for a Gas Turbine Power Plant | The selected model is Rowen’s model to present the mechanical behavior of the gas turbine, the main goal is aimed to improve the system dynamic performance, all gains for conventional PID and interval fuzzy type-2 PID are tuned using social spider optimization(SSO) technique, and showed the performance improvement for interval fuzzy type-2 PID controller in comparison with conventional PID via simulation (min–max fuzzy) | 2018 [17] |

Turbojet engine industrial min–max controller performance improvement using fuzzy norms | The minimum and maximum functions in the industrial min–max strategy are replaced with the different fuzzy norms to improve the performance of the GTE FADEC (min–max fuzzy) | 2018 [18] |

Fuzzy modeling and fast model predictive control of gas turbine system | For achieving high tracking performance and disturbance rejection ability within less settling time under various operating conditions, an improved fuzzy modeling approach and corresponding fast model predictive control algorithm were introduced and applied to a gas turbine system (pure fuzzy) | 2020 [19] |

Linguistic Variables | Symbol | Linguistic Variable | Symbol |
---|---|---|---|

Negative Big | NB | Positive Big | PB |

Negative Medium | NM | Positive Medium | PM |

Negative Small | NS | Positive Small | PS |

Zero | Z |

ΔNdot | Z | PS | PM | PB | NS | NM | NB | |
---|---|---|---|---|---|---|---|---|

ΔN | ||||||||

Z | Z | PS | PS | PS | NS | NS | NS | |

PS | NS | Z | PS | PS | NM | NM | NM | |

PM | NM | NS | Z | PM | NM | NM | NB | |

PB | NB | NM | NS | Z | NB | NB | NB | |

NS | PS | PS | PM | PM | Z | NS | NS | |

NM | PM | PM | PM | PB | PS | Z | NM | |

NB | PB | PB | PB | PB | PM | PS | Z |

ΔN | NB | NM | NS | Z | PS | PM | PB |

FMF (Transient Fuel Flow) | NB | NM | NS | Z | PS | PM | PB |

ΔN = POS | ΔN = NEG | ||||||||
---|---|---|---|---|---|---|---|---|---|

${\stackrel{\xb7}{N}}_{max}-\stackrel{\xb7}{N}$ | Z | PS | PM | PB | ${\stackrel{\xb7}{N}}_{min}-\stackrel{\xb7}{N}$ | Z | NS | NM | NB |

FMF (Acc.) | Z | PS | PM | PB | FMF (Dec.) | Z | NS | NM | NB |

**Table 6.**Comparison between response time and fuel consumption for Pure Fuzzy Controller (PFC), Fuzzy Min-Max (FMM) controller, ans Min-Max controller.

Mean Response Time (s) | Fuel Consumption (kg/s) | |
---|---|---|

Pure Fuzzy Controller | 4.01 | 7.05 |

Min–Max Fuzzy Controller | 4.18 | 6.76 |

Min–Max Controller | 3.95 | 7.21 |

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**MDPI and ACS Style**

Mohammadi Doulabi Fard, S.J.; Jafari, S.
Fuzzy Controller Structures Investigation for Future Gas Turbine Aero-Engines. *Int. J. Turbomach. Propuls. Power* **2021**, *6*, 2.
https://doi.org/10.3390/ijtpp6010002

**AMA Style**

Mohammadi Doulabi Fard SJ, Jafari S.
Fuzzy Controller Structures Investigation for Future Gas Turbine Aero-Engines. *International Journal of Turbomachinery, Propulsion and Power*. 2021; 6(1):2.
https://doi.org/10.3390/ijtpp6010002

**Chicago/Turabian Style**

Mohammadi Doulabi Fard, Seyed Jalal, and Soheil Jafari.
2021. "Fuzzy Controller Structures Investigation for Future Gas Turbine Aero-Engines" *International Journal of Turbomachinery, Propulsion and Power* 6, no. 1: 2.
https://doi.org/10.3390/ijtpp6010002