Method for Helicopter Turboshaft Engines Controlling Energy Characteristics Through Regulating Free Turbine Rotor Speed and Fuel Consumption Based on Neural Networks DOI Creative Commons
Serhii Vladov, Maryna Bulakh,

Jan Czyżewski

et al.

Energies, Journal Year: 2024, Volume and Issue: 17(22), P. 5755 - 5755

Published: Nov. 18, 2024

This research is devoted to the development of a method for helicopter turboshaft engine energy characteristics control by regulating free turbine rotor speed and fuel consumption using neural network technologies. A mathematical model was created that links main parameters, based on which relation with output power established. In this research, differential equation obtained consumption, power, speed, makes it possible monitor dynamics in various operating modes. controller developed neuro-fuzzy processes input data, including desired current allows real-time adjustments improve operational efficiency. flight data analysis during Mi-8MTV TV3-117 test, improved signal processing quality due time sampling adaptive quantisation methods (this confirmed assessing homogeneity representativeness training test datasets). comparative traditional controllers showed use reduces transient process 8.92% while increasing accuracy F1 score 18.28% 21.32%, respectively.

Language: Английский

An Efficient Coordinated Observer LQR Control in a Platoon of Vehicles for Faster Settling Under Disturbances DOI Creative Commons
M. Nandhini,

Mohamed Rabik Mohamed Ismail

World Electric Vehicle Journal, Journal Year: 2025, Volume and Issue: 16(1), P. 28 - 28

Published: Jan. 7, 2025

The rapid proliferation of vehicles globally presents significant challenges to road transportation efficiency and safety, including accidents, emissions, energy utilization, management. Autonomous vehicle platooning emerges as a promising solution within intelligent systems, offering benefits like reduced fuel consumption optimized use. However, implementing autonomous faces obstacles such stability under disturbances, safety protocols, communication networks, precise control. This paper proposes novel control strategy coordinated Kalman observer–Linear Quadratic Regulator (CKO-LQR) ensure platoon formation in the presence disturbances. disturbances considered include movements, sensor noise, delays, with leading vehicle’s movement serving commanding signal. proposed controller maintains constant inter-gap distance between despite utilizing observer estimate preceding movements. A comparative analysis conventional PID controllers demonstrates superior performance terms faster settling times robustness against research contributes enhancing systems.

Language: Английский

Citations

0
Disturbance and uncertainty compensation control for heterogeneous platoons under network delays DOI
E. Silva, Leonardo Amaral Mozelli, Armando Alves Neto

et al.

Computers & Electrical Engineering, Journal Year: 2025, Volume and Issue: 123, P. 110066 - 110066

Published: Jan. 24, 2025

Language: Английский

Citations

0
Longitudinal motion control algorithm for autonomous vehicles taking decisions based on the preceding vehicle behavior pattern DOI

Xinghan Qiao,

Xinze Li, Weiyang Ma

et al.

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: April 4, 2025

In the field of autonomous driving, a key concern is whether driving algorithms can better adapt to their environments. Currently, vehicles often adopt single control strategy, which reduce traffic efficiency and negatively impact other road users. To address this issue, paper presents longitudinal motion algorithm for that makes decisions based on preceding vehicle’s behavior pattern, aiming comprehensively improve both safety. Firstly, using NGSIM dataset, large number kinematic features from highway-driving are extracted standardized. Subsequently, Principal Component Analysis (PCA) applied dimensionality decouple data. Following this, Fuzzy C-Means clustering (FCM) employed categorize vehicles’ characteristics into several typical patterns. By incorporating regulations various countries, external metrics established evaluate results. Based these metrics, parameters optimized enhance reliability outcomes. Additionally, vehicle pattern identification module was developed lightweight Convolutional Neural Network (CNN), achieving high accuracy low computational load in online experiments. Depending different patterns vehicle, we design safety distance model balances efficiency. ensure target following met, Deep Reinforcement Learning (DRL) developed. Finally, comparative experiments conducted, results demonstrate proposed effectively optimizes efficiency, safety, comfort comprehensive manner, thereby verifying its feasibility.

Language: Английский

Citations

0
Compensation control of commercial vehicle platoon considering communication delay and response lag DOI

Hongxiang Liu,

Duanfeng Chu, Wei Zhong

et al.

Computers & Electrical Engineering, Journal Year: 2024, Volume and Issue: 119, P. 109623 - 109623

Published: Sept. 7, 2024

Language: Английский

Citations

2
Method for Helicopter Turboshaft Engines Controlling Energy Characteristics Through Regulating Free Turbine Rotor Speed and Fuel Consumption Based on Neural Networks DOI Creative Commons
Serhii Vladov, Maryna Bulakh,

Jan Czyżewski

et al.

Energies, Journal Year: 2024, Volume and Issue: 17(22), P. 5755 - 5755

Published: Nov. 18, 2024

This research is devoted to the development of a method for helicopter turboshaft engine energy characteristics control by regulating free turbine rotor speed and fuel consumption using neural network technologies. A mathematical model was created that links main parameters, based on which relation with output power established. In this research, differential equation obtained consumption, power, speed, makes it possible monitor dynamics in various operating modes. controller developed neuro-fuzzy processes input data, including desired current allows real-time adjustments improve operational efficiency. flight data analysis during Mi-8MTV TV3-117 test, improved signal processing quality due time sampling adaptive quantisation methods (this confirmed assessing homogeneity representativeness training test datasets). comparative traditional controllers showed use reduces transient process 8.92% while increasing accuracy F1 score 18.28% 21.32%, respectively.

Language: Английский

Citations

0