ABSTRACT Army vehicles are complex due to various on-board mission critical communication devices. The Army cannot afford unreliable software to interact between the devices. The Army vehicle software’s reliability is influenced by multiple factors during or prior to its development. Using complex statistical and mathematical models, software’s reliability can be predicted, but it is dependent on the accuracy and context of the historical software failure data. The cost of developing such complex models does not yield a good return on investment. The data collection process to use these models is very difficult and time consuming. In this paper, we propose reliability metrics based on the current software development and design process factors. We also propose a fuzzy logic based software reliability prediction algorithm using the proposed reliability metrics

Dattathreya, Macam S., Singh, Harpreet

Development of ANFIS Predictive Model for Additive Manufacturing of TPU Material

2024-28-0025

10/17/2024

Additive Manufacturing (AM) techniques, particularly Fusion Deposition Modeling (FDM), have received considerable interest due to their capacity to create complex structures using a diverse array of materials. The objective of this study is to improve the process control and efficiency of Fused Deposition Modeling (FDM) for Thermoplastic Polyurethane (TPU) material by creating a predictive model using an Adaptive Neuro-Fuzzy Inference System (ANFIS). The study investigates the impact of FDM process parameters, including layer height, nozzle temperature, and printing speed, on key printing attributes such as tensile strength, flexibility, and surface quality. Several experimental trials are performed to gather data on these parameters and their corresponding printing attributes. The ANFIS predictive model is built using the collected dataset to forecast printing characteristics by analyzing input process parameters. The ANFIS model utilizes the learning capabilities of neural networks

Pasupuleti, Thejasree, Natarajan, Manikandan, D, Palanisamy, A, Gnanarathinam, Umapathi, D, Kiruthika, Jothi

Method for Modeling Automotive Manufacturing Equipment Preventive Maintenance Derived by Multi-Criteria Decision and Synthesized Weibull Distribution

2024-01-5096

10/08/2024

To avoid equipment failures in automotive manufacturing activities, particular attention is paid to the design of an effective preventive maintenance strategy model for automotive component processing equipment. The selection of appropriate maintenance intervals as well as the equilibrium between the benefits and costs should be the primary challenges in high-quality maintenance process. In this study, a reliable preventive maintenance strategy model is proposed and the aim is to suggest an appropriated approach for the selection of maintenance intervals from a comprehensive view of importance, hazard, and maintenance cost. First and foremost, a new Fermatean fuzzy entropy (FFE) measure method on the basis of analytic hierarchy process (AHP) is innovatively employed to access more objective weights of each indicator. Moreover, a more objective scoring of importance and hazard indicator is executed to aggregate the expert group judgments. Furthermore, this study emphasizes the

Ma, Zexin, Pan, Zhesheng, Wang, Chengxiang, Wei, Mingxin, Yu, Wenbin, Li, Guoxiang, Zhao, Feiyang, Zhu, Sipeng

A Hardware-in-the-Loop Platform for Developing a Fuel Cell Hybrid Electric Microcar: Fuel Cell Stack Sizing Using a Real-Time Testing Approach

2024-24-0006

09/18/2024

Sustainable mobility is a pressing challenge for modern society. Electrification of transportation is a key step towards decarbonization, and hydrogen Fuel Cell Hybrid Electric Vehicles (FCHEVs) offer a promising alternative to Battery Electric Vehicles (BEVs), especially for long-range applications: they combine a battery system with a fuel cell, which provides onboard electric power through the conversion of hydrogen. Paramount importance is then given to the design and sizing of the hybrid powertrain for achieving a compromise between high performance, efficiency, and low cost. This work presents a Hardware-in-the-Loop (HIL) platform developed for designing and testing the powertrain layout of an FCHEV. The platform comprises two systems: a simulation model reproducing the dynamics of a microcar and a hardware system for the fuel cell hybrid electric powertrain. The former simulates the vehicle's behavior, while the latter is composed of a 2kW real fuel cell stack and a 100Ah Li-ion

Bartolucci, Lorenzo, Cennamo, Edoardo, Cordiner, Stefano, Donnini, Marco, Grattarola, Federico, Mulone, Vincenzo

Personalized Design of Variable Transmission Ratio and Selection Switching Strategies Considering Drivers’ Steering Characteristics

10-08-04-0028

09/14/2024

In order to meet the driving characteristics and needs of different types of drivers and to improve driving comfort and safety, this article designs personalized variable transmission ratio schemes based on the classification results of drivers’ steering characteristics and proposes a switching strategy for selecting variable transmission ratio schemes in response to changes in driver types. First, data collected from driving simulator experiments are used to classify drivers into three categories using the fuzzy C-means clustering algorithm, and the steering characteristics of each category are analyzed. Subsequently, based on the steering characteristics of each type of driver, suitable speed ranges, steering wheel travel, and yaw rate gain values are selected to design the variable transmission ratio, forming personalized variable transmission ratio schemes. Then, a switching strategy for variable transmission ratio schemes is designed, using a support vector machine to build a

Chen, Chen, Zheng, Hongyu, Zong, Changfu

Improved Monitoring and Classification of Engine Oil Condition through Two Machine Learning Techniques

04-18-01-0005

09/14/2024

This study explores the effectiveness of two machine learning models, namely multilayer perceptron neural networks (MLP-NN) and adaptive neuro-fuzzy inference systems (ANFIS), in advancing maintenance management based on engine oil analysis. Data obtained from a Mercedes Benz 2628 diesel engine were utilized to both train and assess the MLP-NN and ANFIS models. Six indices—Fe, Pb, Al, Cr, Si, and PQ—were employed as inputs to predict and classify engine conditions. Remarkably, both models exhibited high accuracy, achieving an average precision of 94%. While the radial basis function (RBF) model, as presented in a referenced article, surpassed ANFIS, this comparison underscored the transformative potential of artificial intelligence (AI) tools in the realm of maintenance management. Serving as a proof-of-concept for AI applications in maintenance management, this study encourages industry stakeholders to explore analogous methodologies. Highlights Two machine learning models, multilayer

Pourramezan, Mohammad-Reza, Rohani, Abbas

Modelling and Simulation of Electronic Brake Booster with Fuzzy Logic Control

2024-01-3052

09/08/2024

On one hand, simulation tools are widely used to study and examine new technologies before building prototypes. It is a cost and time saver if it is mathematically modeled with and simulated in real time with sufficient fidelity. On the other hand, the expansion of electric and hybrid vehicle development requested advancing the Electronic Brake Booster (EBB) technologies. In this paper, a simulation tool for the EBB is developed to simulate the performance in real time with a very quick response compared to the previous models with a novel fuzzy logic control (FLC) for the position tracking control. The configuration of the EBB is established, and the system model, including the permanent magnet synchronous motor (PMSM), a double reduction transmission (gears and a ball screw), a servo body, a reaction disc, and the hydraulic load, is modeled. The load-dependent friction has been compensated by using the Karnopp-friction model. FLC has been used for the control algorithm. The control

Soliman, Amr M.E., Kaldas, Mina M., Soliman, Aref M.A., Huzayyin, Ahmed

Research on Network Security Situation Prediction Algorithm Combining Intuitionistic Fuzzy Sets and Deep Neural Networks

12-07-03-0022

04/17/2024

The expansion of the internet has made everyone’s personal and professional lives more transparent. There are network security issues because people like sharing resources under the right conditions. Academics have demonstrated significant interest in situation awareness, which includes situation prediction, situation appraisal, and event detection, rather than focusing on the security of a single device in the network. Multi-stage attack forecasting and security situation awareness are two significant issues for network supervisors because the future usually is unknown. Hence, this study suggests combined intuitionistic fuzzy sets and deep neural network (CIFS-DNN) for network security situation prediction. The goal is to provide network administrators with a resource they can use as a point of reference while they formulate and carry out preventive actions in the event of a network assault. The job requires differentiating between the event of an assault and a typical instance, as

Gao, Hui, Guo, Liang

Fuzzy Control of Regenerative Braking on Pure Electric Garbage Truck Based on Particle Swarm Optimization

2024-01-2145

04/09/2024

To improve the braking energy recovery rate of pure electric garbage removal vehicles and ensure the braking effect of garbage removal vehicles, a strategy using particle swarm algorithm to optimize the regenerative braking fuzzy control of garbage removal vehicles is proposed. A multi-section front and rear wheel braking force distribution curve is designed considering the braking effect and braking energy recovery. A hierarchical regenerative braking fuzzy control strategy is established based on the braking force and braking intensity required by the vehicle. The first layer is based on the braking force required by the vehicle, based on the front and rear axle braking force distribution plan, and uses fuzzy controllers. Achieve one-time distribution of the front axle braking force; the second layer, according to the magnitude of the braking intensity, divides the braking conditions into light braking, moderate braking and emergency braking, and realizes braking under the three

Zhang, Yu

Enhancing Lateral Stability in Adaptive Cruise Control: A Takagi-Sugeno Fuzzy Model-Based Strategy

2024-01-1962

04/09/2024

Adaptive cruise control is one of the key technologies in advanced driver assistance systems. However, improving the performance of autonomous driving systems requires addressing various challenges, such as maintaining the dynamic stability of the vehicle during the cruise process, accurately controlling the distance between the ego vehicle and the preceding vehicle, resisting the effects of nonlinear changes in longitudinal speed on system performance. To overcome these challenges, an adaptive cruise control strategy based on the Takagi-Sugeno fuzzy model with a focus on ensuring vehicle lateral stability is proposed. Firstly, a collaborative control model of adaptive cruise and lateral stability is established with desired acceleration and additional yaw moment as control inputs. Then, considering the effect of the nonlinear change of the longitudinal speed on the performance of the vehicle system. And the input penalty factor of the adaptive cruise control system is designed as a

Yan, Yang, Xin, Yafei, Zheng, Hongyu

Vehicle Braking Performance Improvement via Electronic Brake Booster

10-08-01-0005

02/10/2024

Throughout the automobile industry, the electronic brake boost technologies have been widely applied to support the expansion of the using range of the driver assist technologies. The electronic brake booster (EBB) supports to precisely operate the brakes as necessary via building up the brake pressure faster than the vacuum brake booster. Therefore, in this article a novel control strategy for the EBB based on fuzzy logic control (FLC) is developed and studied. The configuration of the EBB is established and the system model including the permanent magnet synchronous motor (PMSM), a two-stage reduction transmission (gears and a ball screw), a servo body, reaction disk, and the hydraulic load are modeled by MATLAB/Simulink. The load-dependent friction has been compensated by using Karnopp friction model. Due to the strong nonlinearity on the EBB components and the load-dependent friction, FLC has been used for the control algorithm. The control concept focused on transforming the

Soliman, Amr M.E., Kaldas, Mina M., Soliman, Aref M.A., Huzayyin, A.S.

Hydrodynamic Flow Characteristics Prediction for Bluff Body Wake via Novel Adaptive Neuro-Fuzzy Controller Avoiding Fuzzy Rule Explosion

2023-01-5081

11/22/2023

This study analyses the effect of Reynolds number (Re) and bluff body shape (quantified by shape factor SF) variation on various hydrodynamic characteristics of unsteady bluff body flow, such as Strouhal number, maximum lift coefficient, and mean drag coefficient. The study initially examines a relationship among these characteristics and further utilizes artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) controllers for their precise prediction. The results from real-time computational fluid dynamics (CFD) experimentations were gathered and considered to train ANN controllers. A novel ANFIS controller has been designed using only three membership functions thus solving the problem of fuzzy rule explosion. The results indicate that both the ANN and ANFIS controllers can precisely predict these hydrodynamic flow characteristics as validated through minimal values of root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage

Kharola, Ashwani, Dobriyal, Ritvik, Sharma, Rakesh Chandmal, Sharma, Neeraj, Sharma, Ashwini, Raturi, Anuj

Artificial Intelligence Model for Machinability Investigations on Drilling of AA6061 with Micro Textured Tool for Automobile Applications

2023-28-0082

11/10/2023

Considering the advancements in manufacturing industries, which are crucial for economic growth, there is a substantial demand for exploration and analysis of advanced materials, especially alloy materials, to enable efficient utilization of new technologies. Lightweight and high-strength materials, like aluminium alloys, are highly recommended for various applications that necessitate both strength and resistance to corrosion, such as automobile, marine and high-temperature applications. Therefore, there is a significant need to investigate and analyse these materials to facilitate their effective application in manufacturing sectors. This study investigates the machinability of drilling AA6061 using a micro-textured tool and proposes an Adaptive Neuro Fuzzy Inference System (ANFIS) model for investigating the machinability of drilling AA6061 aluminium alloy with a micro-textured uncoated tool. The ANFIS model considers various input parameters such as spindle speed, feed rate, and

Katta, Lakshmi Narasimhamu, Natarajan, Manikandan, Pasupuleti, Thejasree, Siva Rami Reddy, Narapureddy, Sivaiah, Potta

Renewal Fuzzy Logic Energy Management System

2023-01-1687

10/31/2023

This work proposes a unique control method consisting of ameliorated with reinforcement learning renewal module. The combined fuzzy logic and reinforcement learning regime is utilized to promote robust energy management control in complex working conditions. The coupled optimization proposition tackles unforeseen disturbance by two-pronged approach, with fuzzy logic analyzing backbone power contribution schemes while reinforcement learning takes responsibility for improving a higher efficiency strategy. The vehicle dynamic parameters and energy map are co-modeled through learning extrapolation function. Fuzzy rule undergoes efficient feedback revival via modulating factors driven from multi-objective RL reward computation. Meanwhile, reinforcement learning system leverages adaptive fuzzy representation that generalizes coordination potential vectors, effectively extends exploration quality compared to vanilla learning strategy. To this end, this work effectively considers traction

Ouyang, Qianyu

Prediction of the Remaining Useful Life of the Proton Exchange Membrane Fuel Cell with an Integrated Health Index

2023-01-7013

10/30/2023

To improve the prediction accuracy of the remaining useful life (RUL) of the proton exchange membrane fuel cell (PEMFC), an integrated health index (IHI) including electrical and non-electrical parameters of PEMFC is established, and the RUL prediction is conducted based on the above index. Firstly, several operating conditions including the PEMFC degradation information are selected according to the information theory method. Moreover, the IHI is established by the sequential quadratic programming method. Secondly, RUL predictions based on the power and IHI are conducted by the adaptive neuro fuzzy inference system (ANFIS), respectively. Finally, different results comparisons including power and IHI differences, differences between experimental and training/predicting results, amounts of different differences in training and predicting phases, and RUL prediction results are presented in detail. The results show that the accuracy of the RUL prediction based on the IHI is higher than

Fan, Lei, Zhou, Su, Wen, Chaokai, Gao, Jianhua

Damping Magnetorheological Systems Based on Optimal Neural Networks Preview Control Integrated with New Hybrid Fuzzy Controller to Improve Ride Comfort

10-07-04-0032

10/03/2023

Adaptive neural networks (ANNs) have become famous for modeling and controlling dynamic systems. However, because of their failure to precisely reflect the intricate dynamics of the system, these have limited use in practical applications and perform poorly during training and testing. This research explores novel approaches to this issue, including modifying the simple neuron unit and developing a generalized neuron (GN). The revised version of the neuron unit helps to develop the system controller, which is responsible for providing the desired control signal based on the inputs received from the dynamic responses of the vehicle suspension system. The controller is then tested and evaluated based on the performance of the magnetorheological (MR) damper for the main suspension system. These results of the tests show that the optimal preview controller designed using the GN both ∑-Π-ANN and Π-∑-ANN can accurately capture the complex dynamics of the MR damper and improve their damping

Shehata Gad, Ahmed, Darakhshan Jabeen, Syeda, Galal Ata, Wael

Interval Lower Singleton Fuzzy Optimal Controller Design of Magnetorheological Seat Suspension Integrated with Semi-Active Vehicle Suspension System

2023-01-5066

09/22/2023

In this paper, semi-active MR main suspension system based on system controller design to minimize pitch motion linked with MR-controlled seat suspension by considering driver’s biodynamics is investigated. According to a fixed footprint tire model, the transmitted tire force is determined. The linear-quadratic Gaussian (LQG) system controller is able to enhance ride comfort by adjusting damping forces based on an evaluation of body vibration from the dynamic responses. The controlled damping forces are tracked by the signum function controllers to evaluate the supply voltages for the front and rear MR dampers. Based on the sprung mass acceleration level and its derivative as the inputs, the optimal type-2 (T-2) fuzzy seat system controller is designed to regulate the controlled seat MR damper force. The best rate for each linguistic variable is acquired by modifying the range between upper and lower membership functions (MFs), which enables accurate tracking of the seat-damping force

Shehata Gad, Ahmed

Design and Implementation of Adaptive and Artificial Intelligence Controller for Brushless Motor Drive Electric Vehicle

14-13-01-0003

06/29/2023

Brushless direct current (BLDC) motor aims to obtain high efficiency when compared to conventional DC motors due to several reasons. But when it comes to the control then its control is much more complicated due to the requirement of a phase supply switching circuit. Usually, the conventional and classical proportional integral derivative (PID) controller is used but it is quite cumbersome to tune its fixed gains. APID controller is used where PID fails to fulfill the objectives in varying situations. So, the adaptive proportional integral derivative (APID) controller is utilized to enhance the results. An artificial neural network (ANN) controller is one of the recent control methods, which gives accurate and precise results and utilizes ANN to give more accurate results. But it lacks fuzzy logic, that is, human tendency, and finally, the artificial neuro-fuzzy inference system (ANFIS) controller is concluded as the best controller to limit the speed of the BLDC motor. ANFIS includes

Saxena, Aditi, Gupta, Amit, Tiwari, Nitesh

A Simple Prototype to Forecast High Ice Water Content Using TAT Anomalies as Training Data

2023-01-1495

06/15/2023

In the last decades there have been many temporary engine failures, engine-related events and erroneous airspeed indication measurements that occurred by a phenomenon known as Ice Crystal Icing (ICI). This type of icing mainly occurs in high altitudes close to tropical convection in areas with a high concentration of ice crystals. Direct measurements or in-situ pilot observations of ICI that could be used as a warning to other air-traffic are rare to nearly non-existent. To detect those dangerous high Ice Water Content (IWC) areas with already existing airborne measurement instruments, Lufthansa analyzed observed Total Air Temperature (TAT) anomalies and used a self-developed search algorithm, depicting those TAT anomalies that are related to ice crystal icing events. To optimize the flight route for dispatchers several hours before the flight, e.g. for long distance flights through the intertropical convergence zone (ITCZ), reliable forecasts to identify hazardous high IWC regions are

Kalinka, Frank, Butter, Max, Jurkat, Tina, De La Torre Castro, Elena, Voigt, Christiane

Automotive Steering System and Its Controller Design for Intelligent Vehicles

13-04-02-0011

04/24/2023

With the rapid development of intelligent vehicles technology, it is extremely urgent to solve environmental pollution and energy crisis. The electric intelligent vehicles technology can accelerate the world to move towards low carbonization and intelligence. In this article, one automatic steering system and its controller are designed with this electric vehicle as the verification platform. First, based on the digital mock-up (DMU) module of the CATIA digital prototype, the motion simulation of the automatic steering system is carried out. Then, the transient dynamics and fatigue analysis module from ANSYS Workbench 16.0 software is used to simulate and analyze the transmission mechanism. After verifying the reasonable strength of the real vehicle parts, the original platform steering system is reformed. Our intelligent vehicle uses a monocular charge-coupled device (CCD) to detect road marking lines and then employs a linear two degrees of freedom (2-DOF) vehicle model to establish

Zhao, Yibing, Chen, Yuqiao, Lv, Yanqing, Guo, Lie

Identification of the Nonlinear Dynamic Behavior of Magnetorheological Fluid Dampers using Adaptive Neuro-Fuzzy Inference System

2023-01-0123

04/11/2023

Adaptive neuro-fuzzy inference system (ANFIS) technique has been developed and applied by numerous researchers as a very useful predictor for nonlinear systems. In this paper, non-parametric models have been investigated to predict the direct and inverse nonlinear dynamic behavior of magnetorheological (MR) fluid dampers using ANFIS technique to demonstrate more accurate and efficient models. The direct ANFIS model can be used to predict the damping force of the MR fluid damper and the inverse dynamic ANFIS model can be used to offer a suitable command voltage applied to the damper coil. The architectures and the learning details of the direct and inverse ANFIS models for MR fluid dampers are introduced and simulation results are discussed. The suggested ANFIS models are used to predict the damping force of the MR fluid damper accurately and precisely. Moreover, validation results for the ANFIS models are proposed and used to evaluate their performance. Validation results with several

Abd Elwahed, Amr, Metered, Hassan, Monieb, Hany

Development of Adaptive Neuro Fuzzy Inference System Model for CNC Milling of AA5052 Alloy with Minimum Quantity Lubrication by Natural Cutting Fluid

2022-28-0511

12/23/2022

In view of the improvements in manufacturing sectors, which are an important component of any economy’s growth, there is a significant need for new and advanced materials, particularly alloy materials need to be analysed and investigated so that new technologies may be effectively utilized. Materials with low weight and high strength, such as aluminium alloys, are recommended for a variety of applications that require both strength and corrosion resistance, such as marine applications and high-temperature applications. Aluminium alloy Al 5052, a nonferrous material with outstanding properties is an Al-Mg alloy with high thermal conductivity and corrosion products that are non-toxic. Minimum Quantity Lubrication (MQL) is a cost-effective and environmentally friendly method of lubrication employed in a variety of machining processes. The investigation of CNC milling of AA5052 alloy with standard Tungsten Carbide (WC) tool inserts with MQL settings are detailed in this paper. Coconut oil

Katta, Lakshmi Narasimhamu, Pasupuleti, Thejasree, Natarajan, Manikandan, Siva Rami Reddy, Narapureddy, SOMSOLE, LAKSHMI NARAYANA

Dynamic Authority Allocation for Shared Steering Control Considering Driver Attention and Conflict Resolution

2022-01-7071

12/22/2022

Vehicle automation can improve traffic safety and reduce the driver workload. However, the driver disengagement from the control loop will cause a significant risk under current technology. Shared steering control, which can realize the cooperative control of the vehicle by the driver and the intelligent system, is a research hotspot at present. This paper proposes a dynamic authority allocation strategy for shared steering control system considering driver’s attention level and conflict resolution. Firstly, a shared steering control model based on non-cooperative game theory is established and the obtained solution is given according to the Nash equilibrium. Thereafter, a driving risk field is built to accurately describe the real-time risk distribution of the driving scenarios. The driving risk field value can also be used to determine the correct side in the event of a human-machine conflict. On this basis, a dynamic authority allocation strategy based on fuzzy controller is

Su, Chengrui, Wu, Xiaodong

Adaptive Path Tracking Controller for Intelligent Driving Vehicles for Large Curvature Paths

12-06-02-0013

12/02/2022

In this article, we use MPC algorithm to design an adaptive path tracking controller based on the vehicle coordinate system, which is effectively applicable to path tracking scenarios with different vehicle speeds and large path curvatures. To reduce the lateral position error and heading angle error, a fitting function learned through a large number of simulations is used to adaptively adjust the prediction horizon parameter and a compensation strategy of steering angle increment designed based on fuzzy control algorithm is used to reduce the influence of model mismatch and low modeling accuracy on the path tracking control effect, then the front wheel steering angle is calculated and output to the vehicle model for path tracking. In this article, multi-scenario simulations are conducted in Simulink and CarSim environments to verify the performance of the proposed controller. The result shows that the adaptive path tracking controller proposed in this article achieves a more

Liu, Jie, Yang, Can

Research on Driver Model Based on Elastic Net Regression and ANFIS Method

2022-01-5086

11/08/2022

With the aim of addressing the problem of inconsistency of the traditional proportion integration (PI) driver model with the actual driving behavior, a longitudinal driver model based on the elastic net regression (ENR) and adaptive network fuzzy inference system (ANFIS) method is proposed. First, longitudinal driving behavior data are collected through bench tests to extract the characteristic parameters that affect driving behavior. A quadratic regression model is established after considering the nonlinear characteristics of the driver behavior. The multi-collinear problem of high-dimensional variables in the regression model is solved by the ENR method, and the parameters with significant influence on driving behavior selected. A longitudinal driver model of ANFIS was established with the selected characteristic parameters as input. Finally, the validity of the model is verified by comparing it with the PI and ENR driver models. The simulation results show that compared with the

Liu, Tong, Zeng, Xiaohua, Li, Taixiang, Song, Dafeng, Zhuang, Xiao

Enhancing Ride Comfort and Stability of a Large Van Using an Improved Semi-active Stability Augmentation System

10-06-04-0026

08/12/2022

In large vehicles, controlled suspension systems play a vital role in balancing the trade-off between ride comfort and vehicle stability. This article attempts to improve the semi-active stability augmentation system (S-SAS) to provide enhanced passenger comfort and vehicle stability irrespective of the road terrain. A type-1 (T1) fuzzy attitude control strategy is developed to mitigate the loop interactions and limitations in optimizing control gains between the heave and pitch with roll motions. The inner loop called ride control uses a Mamdani interval type-2 (IT2) fuzzy logic control (FLC) to accommodate the system uncertainties and nonlinearities. Semi-active type voice-oil-actuated electrohydraulic (EH) dampers are used to provide controlled damping to suspension systems. The algorithm is deployed in a microcontroller-based hardware, and its performance is tested outdoor for bumpy road conditions at different speeds. A realistic model of the large van in CarSim is also used to

Rajasekharan Unnithan, Anand Raj, Subramaniam, Senthilkumar

Performance of the Machine Learning on Controlling the Pneumatic Suspension of Automobiles on the Rigid and Off-Road Surfaces

15-15-03-0012

07/07/2022

To enhance the ride comfort and control performance of the semi-active pneumatic suspension system (PSS) of automobiles on the different road surfaces, a machine learning method (MLM) developed on the optimal control rules of the fuzzy logic control is proposed for the semi-active PSS. A nonlinear dynamic model of the automobile with eight degrees of freedom (DOF) is established to compute the results. The root mean square (RMS) accelerations of the vertical driver’s seat and the pitching angle and rolling angle of the automobile are selected to evaluate the ride comfort of the automobile on the rigid road and off-road terrain surfaces. The research results show that the off-road terrain surfaces remarkably affect the ride comfort of the automobile, especially at a high moving speed range of the automobile over 17.5 m/s. The performance of the MLM in improving the ride comfort of the automobile is better than the fuzzy logic control under various simulation conditions. Particularly

Siping, Xu, Nguyen, Vanliem, Shiming, Li, Dengke, Ni

Interval Type-2 Fuzzy Control System Integrated with Neural Network-Predictive Control for Air Suspension System to Improve Both Ride and Braking Characteristics

2022-01-5053

06/24/2022

In this paper, the performance of a controlled air suspension system is integrated with the controlled braking system. In order to improve both ride comfort and dynamic stability, the neural network (NN)-predictive control is designed as a system controller for the air suspension system to minimize vertical, pitch, and roll motions. The rate of controlled force generated by the air suspension system is changed according to external excitation transmitted from road roughness to the vehicle body. PID controller is designed for the antilock braking system (ABS) to improve braking performance. Interval type-2 fuzzy control system (IT-2FCS) is also designed as an integrated controller to generate desired paths for both the NN-predictive controller and PID controller. Desired paths are achieved based on tuned dynamic responses of the vehicle suspension system and the relative skid ratio. Pneumatic suspension system with tuned desired paths is compared with both pneumatic suspension system

Shehata Gad, Ahmed

New Integrated Full Vehicle Suspension System for Improvements in Vehicle Ride Comfort and Road Holding

10-06-03-0018

04/27/2022

The integration of electric motors into the wheels of electric vehicles (EVs) increases the unsprung mass which leads to a deterioration of both the ride comfort performance and the road-holding ability and requires then low- and high-frequency control, respectively. In this article, a new integrated full vehicle suspension system is proposed to improve ride comfort and road holding simultaneously. We are seeking to design a hybrid fuzzy system to control the suspension damper and an intelligent proportional integral derivative (PID)-fuzzy to control the in-wheel (IW) Dynamic Vibration Absorber (DVA). To achieve an acceptable vibration performance, the parameters of the DVA system are optimized by using sequential quadratic programming (SQP) algorithm. To validate the ride comfort performance and road-holding ability of EVs driven by four in-wheel motors (IWMs), the proposed strategy of vibration control, based on the combination of the hybrid fuzzy system and the intelligent PID-fuzzy

Merah, Abdelkader, Hartani, Kada, Yazid, Nor El Houda, Mohammed Chikouche, Tarik

On-Board Estimation of Road Adhesion Coefficient Based on ANFIS and UKF

2022-01-0297

03/29/2022

The road adhesion coefficient has a great impact on the performance of vehicle tires, which in turn affects vehicle safety and stability. A low coefficient of adhesion can significantly reduce the tire's traction limit. Therefore, the measurement of the coefficient is much helpful for automated vehicle control and stability control. Considering that the road adhesion coefficient is an inherent parameter of the road and it cannot be known directly from the information of the on-vehicle sensors. The novelty of this paper is to construct a road adhesion coefficient observer which considers the noise of sensors and measures the unknown state variable by the trained neural network. A Butterworth filter and Adaptive Neural Fuzzy Interference System (ANFIS) are combined to provide the lateral and longitudinal velocity which cannot be measured by regular sensors. Unscented Kalman filter (UKF) considering vehicle model, wheel model, and tire model is proposed to estimate the road adhesion

Chen, Zixuan, Duan, Yupeng, Wu, Jinglai, Zhang, Yunqing

Enhancement of Semi-active Vehicle Suspension System Performance Using Magnetorheological Damper

2022-01-5018

03/11/2022

Vehicle suspension is considered a vital system of modern automotive and necessary to offer an adequate level of ride comfort and roadholding. In the present paper, a fuzzy-based sliding surface (FBSS) controller is designed, as a system controller for the first time, for a semi-active vehicle suspension using a magnetorheological (MR) damper in order to minimize the transmitted unwanted vibrations to the passengers. Therefore, an ideal reference skyhook model is employed to construct the sliding surface, which is the input of fuzzy logic. MR damper is a semi-active device and is controlled indirectly using an external voltage source. So a neural-based damper controller is used to compute the applied voltage to the magnet coil of the MR damper in series with the FBSS system controller. The proposed semi-active controlled quarter-vehicle suspension using an MR damper is solved numerically by Matlab. Simulation results are generated in time and frequency domains to judge the suspension

Metered, Hassan

Mathematical Model of the APC Guarani equipped with 30 mm Turret and Magnetorheological Dampers

2021-36-0096

02/04/2022

The combat cars design seeks to balance mobility and fire power, in order to enable the vehicle to shot with accuracy, although running on rough roads. The gun follows the chassis movement, as well as the cannon shots produce forces that change the car dynamics. Because of that, the suspension system of military vehicles has not only to reduce the oscillations caused by the terrain but also has to damper the gun recoil after each shot, preventing misalignment between the tube and the target. Therefore, the present study goal is to evaluate how semiactive dampers could reduce the chassis pitch motion of the Armored Personnel Carrier 6x6 Guarani, from the Brazilian Army, equipped with magnetorheological dampers, while running on a rough road and shooting with the 30 mm cannon at the same time. The proposed model uses the power flow concept to establish the kinematics relationships of the vehicle subsystems, and thus determinate the causality relationships among the components. It is

da Costa Neto, Ricardo Teixeira, Souza, Rogério Felipe Alberto

Vibration Mitigation of Commercial Vehicle Active Tandem Axle Suspension System

02-15-03-0015

01/24/2022

A tandem axle suspension is an important system to the ride comfort and vehicle stability of and road damage experience from commercial vehicles. This article introduces an investigation into the use of a controlled active tandem axle suspension, which for the first time enables more effective control using two fuzzy logic controllers (FLC). The proposed controllers compute the actuator forces based on system outputs: displacements, velocities, and accelerations of movable parts of tandem axle suspension as inputs to the controllers, in order to achieve better ride comfort and vehicle stability and extend the lifetime of road surface than the conventional passive suspension. A mathematical model of a six-degree-of-freedom (6-DOF) tandem axle suspension system is derived and simulated using Matlab/Simulink software. Control performance criteria such as vertical body acceleration (VBA), front suspension working space (FSWS), rear suspension working space (RSWS), front dynamic tire force

Metered, Hassan, Ibrahim, Ibrahim Musaad

Path Tracking Control of Vehicles Based on Adaptive Model Prediction Control

2021-01-7034

12/14/2021

In order to improve the path tracking accuracy of driverless vehicles at different speed, a fuzzy adaptive model prediction control method was proposed to adjust constant predictive horizon of MPC. Based on the MPC method of 3-DOF vehicle dynamics model, prediction horizon and weight coefficient of the MPC controller could be varied in real time according to the speed and road curvature. With the desired path as the target, the front wheel angle was changed to achieve path tracking. Simulation analysis was performed under the CarSim/Simulink co-simulation environment. Simulation results show that under the condition of satisfying ride comfort and stability of vehicle, the tracking error of the proposed method in the path tracking control is reduced by 30.0%, 29.9% and 14.6% at 36km/h, 72km/h and 108km/h, respectively, which are helpful to path tracking control

GUAN, Shuo, CHEN, Yong

Intelligent Control System of Magnetic-Impulse Parts Processing in Ship Repair Production

2021-01-5096

10/04/2021

Humanity has been interested in magnetism for over 300 years. Many authors have studied the use of applied magnetism to change the properties of products and expand the use of magnetic processing in ship repair production [1, 2]. Experience shows that magnetic pulse processing (MPP) is a simple and economical way to increase the durability of metal-cutting tools, increase the resource of the most worn parts of machines and mechanisms, and increase the durability of friction units, assembly units, and structures during their repair and manufacture. MPP has a number of advantages: simplicity of electromagnetic energy concentration on the product, its rapid accumulation by the material of the working elements of the part, and the efficiency of improving the operational characteristics (processing time is 0.3 ... 2.0 s with insignificant energy consumption). The indicated advantages of magnetic processing of products in comparison with other methods of hardening have been repeatedly

Vrublevskyi, Roman, Gritsuk, Igor, Bulgakov, Mykola, Ahieiev, Maksym, Bilousov, Ievhen, Smyrnov, Oleh, Saraieva, Iryna, Savchuk, Volodymyr

Independent Collaborative Robots

TBMG-4002010/01/2021

If you’ve ever helped someone move furniture, you know it takes coordination — simultaneously pushing or pulling and reacting based on what your helper is doing. Researchers have developed artificial intelligence to train robots to work together to move a couch — or in this case a long rod that served as a stand-in — around two obstacles and through a narrow door in computer simulations

Fault Classification using Fuzzy Logic in an Epicyclic Gearbox with Statistical Features

2021-28-0220

10/01/2021

Epicyclic geartrains are often preferred in heavy-duty machinery owing to their abilities such as transmitting large amounts of power with minimal loss, good load sharing capacity, large reduction ratios, and compact design. Machinery employing such complex geartrains need an effective monitoring system to predict gear failure at an early stage which prevents catastrophic failure. In this work, vibration signal of the geartrain is acquired using an accelerometer under various gear fault conditions such as healthy gear, defect in sun gear, defect in planet gear, defect in ring gear, defect in both sun and planet gears respectively. Then, statistical characteristics or features such as mean, median, mode, variance, skewness, kurtosis, standard error, standard deviation, maximum and minimum, of the time domain vibration signals are extracted. Afterward, a decision tree algorithm is used to select the most useful statistical features. These selected features form input to the fuzzy

Syed, Shaul Hameed, V, Muralidharan, D, Pradeep Kumar, S PhD, Ravikumar

A Preview Type-2 Fuzzy Controller Design for the Semi-active Suspension to Improve Adhesion Characteristics during Braking and Handling

2021-01-5069

06/28/2021

A full vehicle of a preview control semi-active suspension system based on an interval type-2 fuzzy controller design using a magnetorheological (MR) damper to improve ride comfort is investigated in this paper. It is integrated with the force distribution system to obtain the optimal rate of road adhesion during braking and handling. The nonlinear suspension model is derived by considering vertical, pitch, and roll motions. The preview interval type-2 fuzzy technique is designed as a system controller, and it is attached with a Signum function method as a damper controller to turn on the voltage for the MR damper. This voltage is adjusted for each wheel based on the external excitation generated by road roughness in order to enhance ride comfort. To describe the effectiveness and adaptable responses of the preview controlled semi-active system, the performance is compared with both the passive and MR passive suspension systems during time and frequency domains. The mathematical models

Shehata Gad, Ahmed

Quantitative Assessment of Minor Incidents to Accident Transformation Probability and Its Impact on Aerodrome Operations

09-09-02-000406/10/2021

Numerous operational procedures regulate aerodrome ground traffic. Detailed solutions in these procedures often come from preventive recommendations formulated as a result of accident cause analysis. With time, the conclusions drawn based on incidents, i.e., events that did not result in material damage or casualties, are becoming increasingly significant. In this article, we propose a new method for determining the probability of an incident turning into an air accident, based on the example of aerodrome traffic operations. Premises conducive to an accident in the considered class of events depend on both human and physical factors. Thus a hybrid approach was applied. We used a fuzzy inference system to analyze the premises dependent on vehicle operators, while the simulation method was selected to examine the premises dependent on physical factors. Both were integrated using the technique of event trees with fuzzy probabilities (ETFP). With the developed methodology, it is possible

Skorupski, Jacek, Rutkowska, Paulina

Linear and Nonlinear Analysis of Ride and Stability of a Three-Wheeled Vehicle Subjected to Random and Bump Inputs Using Bond Graph and Simulink Methodology

02-15-01-000106/07/2021

Bond graph framework, established with MATLAB/Simulink, has a dual objective: analyze the system using bond graph and develop the system equations in symbolic form. This approach is a combination of the simulation skill of the MATLAB/Simulink and the modelling skill of the bond graph. In this analysis, a nine-degrees-of-freedom (9 DoF) three-wheeled vehicle model integrated with a 5 DoF human subject model is formulated using bond graph methodology and simulated using the Simulink toolbox. The present work is divided into two linear and nonlinear analyses of the dynamic behavior of sprung mass subjected to random and bumps inputs, respectively. The linear analysis evaluates the ride comfort of the vehicle and human subject model under the International Organization for Standardization (ISO) and ISO-2631-1 criterion, and the stability of the vehicle is evaluated through on eigenvalues obtained from a single-order state-space form of differential equations obtained from the Simulink

Sharma, Rakesh Chandmal, Sharma, Sakshi, Sharma, Neeraj, Sharma, Sunil Kumar

Items per page:

1 – 50 of 296