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Adaptive fuzzy logic control Framework for Aircraft Landing Gear Automation: Optimized Design, Real-Time Response, and Enhanced Safety

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INTERNATIONAL JOURNAL OF AERONAUTICAL AND SPACE SCIENCES 2025年 1-29页

作者： Dagal, Idriss Mbasso, Wulfran Fendzi Ambe, Harrison Erol, Bilal Jangir, Pradeep Beykent Univ Elect Engn Ayazaga MahallesiHadim Koruyolu Cd19 Sariyer Istanbul Turkiye Univ Douala Technol & Appl Sci Lab UIT Douala POB 8689 Douala Cameroon Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Biotechnol Chennai 602105 Tamil Nadu India Univ Buea Coll Technol COT Dept Elect & Elect Engn POB 63 Buea Cameroon Yildiz Tech Univ Dept Control & Automat Engn TR-34220 Esenler Istanbul Turkiye Chandigarh Univ Univ Ctr Res & Dev Mohali 140413 India Graph Era Hill Univ Dept CSE Dehra Dun 248002 India Graph Era Deemed Be Univ Dept CSE Dehra Dun Uttarakhand India Appl Sci Private Univ Appl Sci Res Ctr Amman 11937 Jordan Chitkara Univ Inst Engn & Technol Ctr Res Impact & Outcome Rajpura 140401 Punjab India

The landing gear systems of aircraft are fundamental for the safety of flight operations, which include takeoffs, landings, and inflight operations. Nonetheless, conventional automation and control systems have difficulties with these tasks due to internal non-linear dynamics, external factors, and the complexity of coordinating all gear positions. This paper solves these problems by developing an optimized sequence control fuzzy logic controller (FLC) for the landing gears. With the use of a Mamdani-type fuzzy inference system (FIS), the work presented in this paper introduces novel control states of "Transit" and "Locked" that provide additional resilience and accommodation for the system. Optimization methods were used to improve the form of membership functions and refine rule bases to increase responsive decision-making. This research highlights the lack of fuzzy logic application in aviation subsystems by attempting to fill the gap of highly adaptive real-time operational and emergencies inadequately addressed in previous research. All the conducted simulations for different flight conditions, including hostile conditions and system failures, demonstrated a 25% improvement in the accuracy of transitions compared to the conventional systems and a 15% faster response time. Moreover, the system maintained functionality during mechanical malfunctions, suggesting that these new approaches may be leading toward true robustness and redundancy within the system. This micromachining strategy can be easily integrated with advanced optimization techniques and adaptive control schemes, significantly contributing to safety as well as efficiency in aerospace engineering. By focusing on the practical application of these advancements, this work leads to reliability and operational safety improvement in airline automation intelligence.

关键词： fuzzy logic control Landing gear automation Aircraft safety Mamdani FIS Flight phase management

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Optimization of energy management strategy with fuzzy logic control and low-pass filtering for hydrogen production system

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2025年 116卷 473-485页

作者： Lin, Guangwei Yang, Jin Sun, Haoran Li, Hongkun Liu, Weiqun Zhu, Qiao Southwest Jiaotong Univ Sch Mech Engn Chengdu 610031 Peoples R China DongFang Boiler Co Ltd Chengdu 611730 Peoples R China

This paper focuses on energy management strategy to fully utilize the energy storage battery's advantages in enhancing hydrogen production and smoothing electrolytic power fluctuation. First, a wind-solar hydrogen production system is introduced and the components are modeled in detail, including photovoltaic, wind turbine, electrolyzer, and energy storage system. Then, fuzzy logic control is implemented to increase hydrogen production by establishing rational logic rules for the distribution of electrolytic power. However, the electrolytic power obtained by fuzzy logic usually fluctuates significantly and tends to accelerate the rate of electrolyzer degradation. For this reason, we add a low-pass filter after the fuzzy logic control to reduce the fluctuation of electrolytic power. After that, a multi-objective optimization problem is proposed and solved by the non-dominated sorting whale optimization algorithm (NSWOA) in order to balance the hydrogen production and the electrolytic power fluctuation. Finally, this paper compares four different strategies to verify the effectiveness of the strategy. The results show that the strategy proposed achieves a balance between hydrogen production and fluctuation, which effectively ensures the hydrogen production efficiency and extends the system lifetime. This is crucial for the efficient and stable production of hydrogen systems.

关键词： Wind-solar hydrogen production system Energy management strategy Power fluctuation fuzzy logic control Low-pass filtering Multi-objective optimization

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Deep-fuzzy logic control for Optimal Energy Management: A Predictive and Adaptive Framework for Grid-Connected Microgrids

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ENERGIES 2025年第4期18卷 995-995页

作者： Cavus, Muhammed Dissanayake, Dilum Bell, Margaret Northumbria Univ Dept Math Phys & Elect Engn Newcastle Upon Tyne NE1 8QH England Univ Birmingham Sch Geog Earth & Environm Sci Birmingham B15 2TT England Newcastle Univ Sch Engn Newcastle Upon Tyne NE1 7RU England

This paper introduces a novel energy management framework, Deep-fuzzy logic control (Deep-FLC), which combines predictive modelling using Long Short-Term Memory (LSTM) networks with adaptive fuzzy logic to optimise energy allocation, minimise grid dependency, and preserve battery health in grid-connected microgrid (MG) systems. Integrating LSTM-based predictions provides foresight into system parameters such as state of charge, load demand, and battery health, while fuzzy logic ensures real-time adaptive control. Results demonstrate that Deep-FLC achieves a 25.7% reduction in operational costs compared to the conventional system and a 17.5% saving cost over the fuzzy logic control (FLC) system. Additionally, Deep-FLC delivers the highest battery efficiency of 61% and constraints depth of discharge to below 2% per time step, resulting in a reduction of the state of health degradation to less than 0.2% over 300 h. By combining predictive analytics with adaptive control, this study addresses the limitations of standalone approaches and establishes Deep-FLC as a robust, efficient, and sustainable energy management solution. Key novel contributions include the integration of advanced prediction mechanisms with fuzzy control and its application to battery-integrated grid-connected MG systems.

关键词： battery health energy management system fuzzy logic control long short-term memory state of charge state of health

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Intelligent Transportation System for Traffic Congestion Based on Dempster-Shafer Evidence Theory and fuzzy logic control

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TRANSPORTATION RESEARCH RECORD 2025年

作者： Li, Hongli Zhengzhou Univ Technol Sch Electromech & Vehicle Engn Zhengzhou Peoples R China

The increasing numbers of vehicles and drivers resulting from rapid economic development has led to a greater requirement for road traffic congestion planning. With the emergence of intelligent transportation, measures to solve traffic congestion have gradually shifted to the guidance and intelligent management of traffic flows. The research described here was based on the use of a combination of Dempster-Shafer evidence theory and fuzzy control to propose a fusion model for intelligent signal-light timing and an intelligent transportation system to relieve traffic congestion. The findings revealed that the model's average recognition accuracy for the congestion level was 98.87%, and it also had the lowest root mean square error and average absolute error values and the biggest value of the coefficient of determination for the training and test sets. In simulation experiments, the intelligent signal timing proposed by the study increased the speed of the vehicles by 1.65%, 4.8%, and 15.4%, and reduced the average delay time by 11.6%, 24%, and 24.1% compared with when fixed timing was used for small, medium, and high traffic flows, respectively. The average number of stops under these three traffic volumes was 0.46 stops/vehicle, 0.59 stops/vehicle, and 0.78 stops/vehicle, respectively. It can be concluded that the model is able to achieve congestion relief and a traffic load reduction through automatic control of the green light delay after the accurate identification of the traffic congestion conditions.

关键词： traffic congestion intelligent transportation system Dempster-Shafer evidence theory fuzzy logic control congestion management transportation demand management

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Multi-objective optimization of electro-pneumatic braking process with fuzzy logic control for heavy haul railway applications

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INTERNATIONAL JOURNAL OF RAIL TRANSPORTATION 2025年第2期13卷 332-351页

作者： Eckert, Jony Javorski Teodoro, Icaro Pavani Lopes, Matheus Valente Wu, Qing Santos, Auteliano A. Univ Estadual Campinas Sch Mech Engn Integrated Syst Dept UNICAMP Mendeleyev St 200 BR-13083860 Campinas SP Brazil Cent Queensland Univ Ctr Railway Engn Rockhampton Australia

This paper presents a novel approach to multi-objective optimization in heavy haul trains' electro-pneumatic braking systems using optimizable fuzzy control. Initial findings indicate that a straightforward replacement of pneumatically controlled valves with electro-pneumatic ones, without significant control schedule modifications, fails to substantially improve the dynamics behaviour of rolling-stock. Consequently, a more sophisticated strategy based on optimizable fuzzy logic control for the electro-pneumatic valves was developed. This approach utilizes multi-objective optimization to simultaneously minimize energy dissipation during braking and reduce force peaks in freight car draft gears. The optimal solution achieved demonstrates a 13.41% reduction in kinetic energy dissipation during downhill sections, along with a concurrent decrease in draft gear peak forces by 14.22% in traction and 3.16% in buff regimes compared to standard rolling-stock with pneumatically controlled valves. This optimized fuzzy braking controller outperforms standard procedures, highlighting its potential to enhance heavy haul train efficiency while adhering to safety standards.

关键词： Air brake freight railway train dynamics fuzzy logic control multi-objective optimization

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Utilizing fuzzy logic control and Neural Networks Based on Artificial Intelligence Techniques to Improve Power Quality in Doubly Fed Induction Generator-Based Wind Turbine System

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2025年第1期2025卷

作者： Sayeh, Karim Fathi Tamalouzt, Salah Ziane, Djamel Bekhiti, Abdellah Belkhier, Youcef Univ Bejaia Fac Technol Lab Technol Ind & Informat Bejaia 06000 Algeria Nantes Univ Inst Res Elect Energy Nantes Atlantique IREENA Nantes France Kasdi Merbah Univ Lab Elect Engn Ouargla 30000 Algeria Univ Brest F-29238 Brest France

This article presents novel artificial intelligence (AI)-based techniques for controlling wind energy conversion systems, specifically fuzzy logic control and neural networks, known as fuzzy hysteresis-direct power control (FH-DPC) and neural hysteresis-DPC (NH-DPC), respectively. The primary purpose is to overcome conventional DPC (C-DPC) limitations in doubly fed induction generator wind turbines (WT-DFIG), focusing on power quality improvement and enhanced system efficiency. The techniques aim to reduce power ripples and improve the quality of alternating current (AC) grid energy by improving current signal quality in all WTs' operation modes with WT-DFIG and all compensation power modes. The suggested techniques are thoroughly examined using the MATLAB/Simulink environment under various wind scenarios, demonstrating a reduction in active power ripples by over 70%, a reduction in reactive power ripples of around 77% on average, a decrease in generated current total harmonic distortions (THDs) by over 70% compared to C-DPC. The performances of FH-DPC and NH-DPC are contrasted with C-DPC and other previously suggested methods, and it is concluded that the proposed control approaches perform more effectively than them regarding ripples in local reactive compensation and generated active powers as well as THD currents, with FH-DPC slightly outperforming NH-DPC. The research indicates that AI can enhance the effectiveness and quality of power generated by wind-power systems.

关键词： artificial intelligence DFIG DPC fuzzy logic control neural networks wind energy conversion system

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Hybrid PI and fuzzy logic control for Energy Optimization in Train Operations

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IEEE ACCESS 2025年 13卷 59366-59376页

作者： Cho, Hwan-Hee Kim, Jae-Won Song, Min-Sup Yun, Chi-Myeong Cho, Gyu-Jung Tian, Zhongbei Korea Railroad Res Inst Electrificat Syst Res Dept Uiwang Si 16105 South Korea Pai Chai Univ Dept Elect & Elect Engn Daejeon 35345 South Korea Univ Birmingham Dept Elect Elect & Syst Engn Birmingham B15 2TT England

This paper presents a fuzzy logic-based train control algorithm designed to enhance energy efficiency across a complete railway route. The proposed framework dynamically integrates practical information such as curvature data, gradient profiles, and station schedules to optimize acceleration, coasting, and braking phases. By using fuzzy logic, the algorithm adaptively balances energy consumption, travel time, and stopping precision. Key features include predictive speed scanning and adaptive PI control, enabling comprehensive energy optimization. Simulation results confirm significant energy savings or voltage regulation while maintaining operational reliability and precision. This study highlights the potential of fuzzy-based control systems in advancing sustainable railway operations.

关键词： Energy efficiency Heuristic algorithms Vehicle dynamics fuzzy logic Rail transportation Resistance Optimization Force Motors Mathematical models Cooperative train operation railway computer simulation energy-efficient scheduling fuzzy logic control operation energy saving

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Fast flexible power point tracking algorithm for photovoltaic systems using variable domain fuzzy logic control

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JOURNAL OF POWER ELECTRONICS 2025年第4期25卷 604-618页

作者： Wang, Manliang Gao, Bingtuan Sun, Ruizhe Wang, Shibo Southeast Univ Sch Elect Engn Nanjing Peoples R China State Grid Shandong Elect Power Res Inst Jinan Peoples R China

As the penetration rate of photovoltaic (PV) power generation continues to increase, PV systems are being required to achieve frequency responses according to grid codes. In this case, PV systems do not work in the maximum power point tracking mode. Instead, they work in the flexible power point tracking (FPPT) mode, which tracks any power points on the right side of the PV power-voltage curve including the maximum power point. In this paper, a fast FPPT algorithm using variable domain fuzzy logic control (FLC) is proposed. The proposed FPPT algorithm adaptively calculates the voltage step size by FLC with a single input and a single output for low computational complexity and fast tracking speed. In addition, to further improve the tracking speed and tracking accuracy, the secant method is integrated to modify the output domain of the FLC. The secant method uses the horizontal distance between two points on the power-voltage curve of a PV array as the domain of the FLC, and this distance gradually narrows as the iteration increases. Simulation and experimental results confirm the fast dynamic response of the proposed FPPT algorithm when compared with existing fixed step size perturb and observe-based and adaptive step size perturb and observe-based FPPT algorithms under different variations of active power reference. When compared to two existing algorithms, the proposed algorithm reduces the tracking time, transient energy loss, and steady energy loss by at least 75.6, 36.3, and 5.8%, respectively.

关键词： Active power control Flexible power point tracking fuzzy logic control Photovoltaic systems Variable domain

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Improvement of power quality in WT-DFIG systems using novel direct power control based on fuzzy logic control under randomness conditions

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INTERNATIONAL JOURNAL OF MODELLING AND SIMULATION 2025年第1期45卷 55-67页

作者： Sayeh, Karim Fathi Tamalouzt, Salah Sahri, Younes Univ Bejaia Fac Technol Lab Technol Ind & Informat LTII Bejaia 06000 Algeria

In this paper, a novel direct power control technique founded on fuzzy logic controller (FLC-DPC) is selected to master and control the DFIG for wind energy conversion system (WECS). The fuzzy logic controller replaces both hysteresis regulators and the switching table in the proposed strategy. Seeking to enhance the control and overcome the defects associated with the conventional DPC (C-DPC) technique, this control depends on the errors of both active and reactive powers. The suitable rotor voltage vector for the inverter is obtained by FLC-DPC. The proposed control strategy is applied to the WT-DFIG system, in order to study its effectiveness. To reflect a real WECS operation, this study considers the wind's random behaviour in successive and continuous ways throughout all WT-DFIG operating modes. Also, it takes into consideration all compensated local reactive power modes. The studied system and the proposed control were tested under MATLAB/Simulink environment. The obtained results showed the high effectiveness of the proposed control in terms of response time, robustness and ease. Consequently, C-DPC's drawbacks are eliminated, and the ripples in compensated local reactive and produced active powers are reduced. Additionally, the total harmonic distortions (THDs) of injected currents are reduced, which improves their quality.

关键词： Renewable energy wind power conversion system DFIG DPC fuzzy logic control

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Neuro-modelling and fuzzy logic control of a two-wheeled wheelchair system

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JOURNAL OF LOW FREQUENCY NOISE VIBRATION AND ACTIVE control 2025年第1期44卷 588-602页

作者： Nasir, NIzaruddin M. Ghani, Nor Maniha Abdul Nasir, Ahmad Nor Kasruddin Ahmad, Mohd Ashraf Tokhi, Mohammad Osman Univ Malaysia Pahang Al Sultan Abdullah Fac Elect & Elect Engn Technol Pekan Campus Pekan 26600 Pahang Malaysia London South Bank Univ Sch Engn London England

Two-wheeled wheelchairs have been used as alternatives for the elderly and disabled people to perform physical activities due to their restriction of movement. Significant challenges posed by two-wheeled wheelchairs control due to their inherent instability, resembling that inverted pendulum system. This research addresses these challenges by developing a dynamic non-linear model and stability control using computational algorithms. A neural network-based Nonlinear Autoregressive model with Exogenous Inputs (NARX) was developed, capturing and behaving similar to the complex dynamics of the wheelchair system with the identification on the experimental input-output data. Ensuring stable and responsive control, design of optimized PD-type and PID-type fuzzy logic controllers using Particle Swarm Optimization (PSO) were established and were tested under a simulation environment. The performance was evaluated across various metrics, including Integral Squared Error (ISE), Integral Absolute Error (IAE), Mean Squared (MSE), and Integral Time Absolute Error (ITAE). The result demonstrates that the PSO Optimized PID-type fuzzy logic controller with scaling factor from MSE index performance come out as best overall, significantly outperforms PD-type fuzzy logic controller, reducing its settling time by 12.5% to 35 s, minimizing overshoot to 0.81 degrees, and achieving a negligible steady-state error of 0.046%. These results highlight the significant of integrating fuzzy logic control and PSO to the neural network model in enhancing the stability and performance of two-wheeled wheelchair systems, offering user safety and comfort.

关键词： Neural network fuzzy logic control particle swarm optimization system identification two-wheeled wheelchair

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