An Innovative Applied Control System of Helicopter Turboshaft Engines Based on Neuro-Fuzzy Networks
Abstract
:1. Introduction
1.1. The Relevance of the Research
1.2. The State of the Art
1.3. The Main Attributes of the Research
- The development of the proposed neural network system for predicting anomalous data in sensor systems;
- The development of the helicopter TE adaptive control law;
- The development of an intelligent automatic control system for the helicopter TE adaptive control law implementation;
- The development of the fuzzy controller architecture and training algorithm;
- The development of a semi-physical simulation stand for conducting computational experiments;
- Conducting a computational experiment to evaluate helicopter TE control quality under conditions of actuator failure in the fuel flow control mechanism within the gas generator rotor r.p.m. channel.
2. Materials and Methods
2.1. Development of Helicopter Turboshaft Engine Adaptive Control Law
- Engine (gas generator) operation is controlled by varying the fuel supply GT. It is the sole parameter influencing the gas generator’s operating mode, as the compressor is fixed, and the first stage of the accessible turbine functions as a throttle with a constant cross-section.
- Maintaining constant free turbine speed nFT = const is key for safe helicopter piloting. To meet this condition, the automatic control system (ACS) adjusts GT when the blade pitch angle of the main rotor φm.r. changes.
- The control model is expressed as follows:
- nFT = const, if φm.r. ≤ φm.r.max;
- If φm.r. > φm.r.max, the ACS limits fuel supply, and nFT starts to decrease to constrain one of the limiting parameters (e.g., maximum turbine speed nTCmax or maximum power Ne).
- The free turbine rotor speed regulator central equation when the fuel supply changes is as follows:
- 5.
- The power limitation condition is represented as follows:
- 6.
- Upon reaching the maximum power limit , maintaining nFT = const becomes impossible, and the nFT frequency decreases.
2.2. The Development of the Algorithm for the Discretized Equation for the Main Rotor Thrust Numerical Solution
- Initialization of the initial conditions as follows:
- 2.
- Computation is performed at each time step:
- Fuel flow GT(t) and the blade pitch angle φm.r.(t) are updated according to the “pitch-throttle” system control law based on the specified target parameters and .
- The free turbine rotor speed nFT(t + Δt) is calculated.
- The thrust Tm.r.(t + Δt) is calculated.
- External parameters H(t + Δt), Ta(t + Δt), and Pa(t + Δt) are updated.
- 3.
- Deviations are assessed. Deviations in turbine speed and thrust from target values are calculated according to the following expression:
- 4.
- Control signals are adjusted as follows:
- If , then then fuel flow GT(t) is adjusted.
- If , then the blade pitch angle φm.r.(t) is adjusted.
- 5.
- Transition to the next time step is carried out according to the following expression:t ← t + Δt.
φm.r.(t + Δt) = φm.r.(t) + kφ ⋅ ΔTm.r.(t).
- Determining limiting modes by establishing a limiting-mode line. This is based on the relations between the maximum allowable values for the parameter and temperature at the input to the gas generator (in this case, temperature TN).
- Identifying areas of limitation modes based on changes in temperature TN. In this case, three areas are distinguished (Figure 2) [31]:
- At low values of TN (for example, TN < TN2), a limitation on ΔKymin is observed.
- In the temperature range from TN2 to TN3, a limitation on nTCmax must be maintained.
- When TN > TN3, a limitation on another limiting parameter takes effect.
- Additional limitations involve adding a power limitation line to the limiting-mode line, resulting in a structure consisting of four segments that account for all constraints. In this case, equations describing the relations between power Ne, engine parameters, and temperature are represented by (9).
- Under limitation conditions, the proposed control program forms an equation system linking fuel supply GT, blade pitch angle φm.r., and power parameters, presented in (11).
2.3. The Development of an Intelligent Automatic Control System for the Implementation of the Helicopter Turboshaft Engine Adaptive Control Law
2.4. Development of Fuzzy Controller
- The control error is calculated as
- 2.
- The error change rate .
- . This control law uses the error in gas generator rotor r.p.m. as one of the inputs.
- 2.
- GT = + kG ⋅ ΔnFT(t). It can be controlled based on the fuzzy output u(t), where u(t) affects ΔnFT(t), the change in free turbine speed.
- 3.
- φm.r.(t + Δt) = φm.r.(t) + kφ ⋅ ΔTm.r.(t), where the control action u(t) determines the adjustment to the rotational speed φm.r.(t).
- 4.
- The control actions must also satisfy the constraints given in the system , nTC ≤ nTCmax, .
3. Results
3.1. Input Data Preprocessing
3.2. Results of Computational Experiment
3.3. Neural Network Model Quality Evaluation
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Nomenclature
GT | is the fuel supply; |
φm.r. | is the central rotor blade pitch angle; |
nFT | is the free turbine rotor speed; |
nTC | is the gas generator rotor r.p.m.; |
is the gas temperature in the front of the compressor turbine; | |
Ne | is the engine output shaft power; |
H | is the flight altitude; |
Pα | is the ambient air pressure; |
Tα | is the ambient air temperature; |
ηFT | is the free turbine efficiency; |
is the fuel combustion thermal power; | |
NFT | is the free turbine power; |
Nm.r. | is the main rotor power; |
Npower loss | is the power loss (for transmission and resistance); |
Hu | is the fuel combustion heat; |
cp | is the gas specific heat capacity |
CT(φm.r.) | is the thrust coefficient depending on the blade angle; |
ρ | is the air density; |
Am.r. | is the main rotor area; |
Tm.r. | is the required main rotor thrust; |
kH | is the coefficient of change in fuel supply with altitude; |
Hmax | is the maximum design altitude; |
kT | is the coefficient of dependence of fuel supply on ambient temperature; |
TN | is the nominal temperature; |
is the limited fuel supply consistent with safe operating conditions; | |
is the base fuel supply; | |
α and β | are the adaptation coefficients for power and rotation speed, respectively; |
JFT | is the free turbine inertia moment; |
MFT(t) | is the torque from the free turbine; |
Mm.r.(t) | is the main rotor resistance moment; |
kG and kφ | are the gain factors for regulating fuel supply and blade angle; |
are the fuzzy values; | |
μA(x) | is the membership function; |
y | is the output value; |
θ | are the control parameters; |
ci | is the membership function center; |
σi | is the membership function width; |
γ | is the adaptation rate; |
ydesired and yactual | are the desired and actual output values; |
μ | is a factor defining the adaptation degree; |
δ | is the control parameter; |
ω | is the disturbance and noise; |
D | is the fuel metering unit position; |
k | is the gain coefficient; |
c | is the damping coefficient; |
u | is the control action; |
e | is the control error |
Kp, Ki, and Kd | are the proportional, integral, and derivative gains, respectively; |
S | is the measured value; |
h | is the measurement function; |
ϵ | is the measurement error; |
u0(t) | is the control signal under normal operating conditions; |
uc(t) | is the corrective action dependent on the failure vector; |
d(t) | is the failure vector; |
xref(t) | are the target system parameters; |
γ1 and γ2 | are weights defining the contribution of control actions and failures to the total cost; |
γ | is the iteration step size. |
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Number | 1 | … | 38 | … | 84 | … | 127 | … | 181 | … | 219 | … | 256 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
e(t) | 0.008 | … | 0.005 | … | 0.003 | … | 0.007 | … | 0.006 | … | 0.006 | … | 0.006 |
0.017 | … | 0.012 | … | 0.013 | … | 0.018 | … | 0.014 | … | 0.014 | … | 0.014 |
Parameter | Criterion Meaning | Description | |
---|---|---|---|
Calculated | Critical | ||
The Fisher–Pearson criterion | |||
e(t) | 6.318 | 6.6 | that fell below the critical threshold, signifying homogeneity within the training dataset. |
6.327 | |||
The Fisher–Snedecor criterion | |||
e(t) | 2.388 | 2.58 | that were below the critical threshold, suggesting homogeneity within the training dataset. |
2.394 |
Metric | Proposed Approach | Alternative Approach 1 | Alternative Approach 2 | Alternative Approach 3 | Alternative Approach 4 |
---|---|---|---|---|---|
Accuracy | 0.995 (99.5%) | 0.961 (96.1%) | 0.975 (97.5%) | 0.999 (99.9%) | 0.882 (88.2%) |
Precision | 0.981 (98.1%) | 0.953 (95.3%) | 0.962 (96.2%) | 0.986 (98.6%) | 0.869 (86.9%) |
Recall | 1.0 | 0.983 | 0.988 | 1.0 | 0.909 |
F1-score | 0.990 | 0.973 | 0.975 | 0.993 | 0.889 |
Metric | Proposed Approach | Alternative Approach 1 | Alternative Approach 2 | Alternative Approach 3 | Alternative Approach 4 |
---|---|---|---|---|---|
True Positives | 96 | 90 | 92 | 99 | 85 |
True Negatives | 4 | 10 | 8 | 1 | 15 |
False Positives | 287 | 282 | 284 | 291 | 277 |
False Negatives | 14 | 20 | 18 | 11 | 25 |
True Positive Rate | 0.828 | 0.785 | 0.793 | 0.844 | 0.626 |
False Positive Rate | 0.0101 | 0.0169 | 0.0152 | 0.0097 | 0.0235 |
False Negative Rate | 0.0098 | 0.0109 | 0.0103 | 0.0093 | 0.0192 |
AUC-ROC | 0.831 | 0.773 | 0.791 | 0.848 | 0.651 |
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Vladov, S.; Lytvynov, O.; Vysotska, V.; Vasylenko, V.; Pukach, P.; Vovk, M. An Innovative Applied Control System of Helicopter Turboshaft Engines Based on Neuro-Fuzzy Networks. Appl. Syst. Innov. 2024, 7, 118. https://doi.org/10.3390/asi7060118
Vladov S, Lytvynov O, Vysotska V, Vasylenko V, Pukach P, Vovk M. An Innovative Applied Control System of Helicopter Turboshaft Engines Based on Neuro-Fuzzy Networks. Applied System Innovation. 2024; 7(6):118. https://doi.org/10.3390/asi7060118
Chicago/Turabian StyleVladov, Serhii, Oleksii Lytvynov, Victoria Vysotska, Viktor Vasylenko, Petro Pukach, and Myroslava Vovk. 2024. "An Innovative Applied Control System of Helicopter Turboshaft Engines Based on Neuro-Fuzzy Networks" Applied System Innovation 7, no. 6: 118. https://doi.org/10.3390/asi7060118
APA StyleVladov, S., Lytvynov, O., Vysotska, V., Vasylenko, V., Pukach, P., & Vovk, M. (2024). An Innovative Applied Control System of Helicopter Turboshaft Engines Based on Neuro-Fuzzy Networks. Applied System Innovation, 7(6), 118. https://doi.org/10.3390/asi7060118