Browse Topic: Fuzzy logic
In order to effectively improve the chassis handling stability and driving safety of intelligent electric vehicles (IEVs), especially in combing nonlinear observer and chassis control for improving road handling. Simultaneously, uncertainty with system input, are always existing, e.g., variable control boundary, varying road input or control parameters. Due to the higher fatality rate caused by variable factors, how to precisely chose and enforce the reasonable chassis prescribed performance control strategy of IEVs become a hot topic in both academia and industry. To issue the above mentioned, a fuzzy sliding mode control method based on phase plane stability domain is proposed to enhance the vehicle’s chassis performance during complex driving scenarios. Firstly, a two-degree-of-freedom vehicle dynamics model, accounting for tire non-linearity, was established. Secondly, combing with phase plane theory, the stability domain boundary of vehicle yaw rate and side-slip phase plane based
The aim of this study is to create an Adaptive Neuro-Fuzzy Inference System (ANFIS) model for the Electrochemical Machining (ECM) process using Nimonic Alloy material, with a specific focus on several performance aspects. The optimization strategy utilizes the combination of the Taguchi method and ANFIS integration. Nimonic Alloy is widely employed in the aerospace, nuclear, marine, and car sectors, especially in situations that are susceptible to corrosion. The experimental trials are designed according to Taguchi's method and involve three machining variables: feed rate, electrolyte flow rate, and electrolyte concentration. This study investigates performance indicators, such as the rate at which material is removed, the roughness of the surface, and geometric characteristics, including overcut, shape, and tolerance for orientation. Based on the analysis, it has been determined that the feed rate is the main component that influences the intended performance criteria. In order to
Wire Electrical Discharge Machining (WEDM) is a sophisticated machining technique that offers significant advantages for processing materials with elevated hardness and complex geometries. Invar 36, a nickel-iron alloy characterized by a reduced coefficient of thermal expansion, is extensively used in the aerospace, automotive, and electronic sectors due to its superior dimensional stability across a wide temperature range. The primary goals are to improve machining settings and develop regression models that can precisely predict critical performance metrics. Experimental experiments were conducted using a WEDM system to mill Invar 36 under diverse machining parameters, including pulse-on time, pulse-off time, and current setting percentage (%). The machining performance was assessed by quantifying the material removal rate (MRR) and surface roughness (Ra). The design of experiments (DOE) methodology was used to systematically explore the parameter space and identify the optimal
The intention of this exploration is to evolve an optimization method for the Electrochemical Machining (ECM) process on Haste alloy material, taking into account various performance characteristics. The optimization relies on the amalgamation of the Taguchi method with an Adaptive Neuro-Fuzzy Inference System (ANFIS). Haste alloy is extensively utilized in the aerospace, nuclear, marine, and car sectors, specifically in situations that are prone to corrosion. The experimental trials are organized based on Taguchi's principles and involve three machining variables: feed rate, electrolyte flow rate, and electrolyte concentration. This examination examines performance indicators, including the pace at which material is removed and the roughness of the surface. It also includes geometric factors such as overcut, shape, and tolerance for orientation. The results suggest that the rate at which the feed is supplied is the most influential element affecting the necessary performance standards
Additive Manufacturing (AM), particularly Fused Deposition Modeling (FDM), has emerged as a revolutionary method for fabricating complex geometries using a variety of materials. Polyethylene terephthalate glycol (PETG) is a thermoplastic material that is biodegradable and environmentally friendly, making it a preferred choice in additive manufacturing (AM) due to its affordability and ease of use. This study aims to optimize the FDM settings for PETG material and investigate the impact of key process parameters on printing performance. An experimental study was conducted to evaluate the influence of crucial factors in FDM, including layer thickness, infill density, printing speed, and nozzle temperature, on significant outcomes such as dimensional accuracy, surface quality, and mechanical properties. The use of the Grey Relational Analysis (GRA) approach enabled a systematic assessment of multi-performance characteristics, facilitating the optimization of the FDM process. The findings
Electrochemical machining (ECM) is a highly efficient method for creating intricate structures in materials that conduct electricity, regardless of their level of hardness. Due to the growing demand for superior products and the necessity for quick design adjustments, decision-making in the manufacturing industry has grown increasingly intricate. This study specifically examines Titanium Grade 7 and suggests the creation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) model for predictive modelling in ECM. The study employs a Taguchi-grey relational analysis (GRA) methodology to attain multi-objective optimization, with the goal of concurrently maximizing material removal rate, minimizing surface roughness, and achieving precise geometric tolerances. Analysis of variance (ANOVA) is used to assess the relevance of process characteristics that impact these performance measures. The ANFIS model presented for Titanium Grade 7 provides more flexibility, efficiency, and accuracy in
Electrochemical machining (ECM) is a highly efficient method for creating intricate structures in materials that conduct electricity, regardless of their level of hardness. Due to the growing demand for superior products and the necessity for quick design changes, decision-making in the manufacturing industry has become increasingly intricate. The preliminary intention of this work is to concentrate on Cupronickel and suggest the creation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) model for the purpose of predictive modeling in ECM. The study employs a Taguchi-grey relational analysis (GRA) methodology to attain multi-objective optimization, with the target of maximizing material removal rate, minimizing surface roughness, and simultaneously achieving precise geometric tolerances. The ANFIS model suggested for Cupronickel provides more flexibility, efficiency, and accuracy compared to conventional approaches, allowing for enhanced monitoring and control in ECM operations
Fused Deposition Modeling (FDM), a form of Additive Manufacturing (AM), has emerged as a groundbreaking technology for the production of complex shapes from a variety of materials. Acrylonitrile Butadiene Styrene (ABS) is an opaque thermoplastic that is frequently employed in additive manufacturing (AM) due to its affordability and user-friendliness. The purpose of this investigation is to enhance the FDM parameters for ABS material and develop predictive models that anticipate printing performance by employing the Adaptive Neuro-Fuzzy Inference System (ANFIS). Through experimental trials, an investigation was conducted to evaluate the influence of critical FDM parameters, including layer thickness, infill density, printing speed, and nozzle temperature, on critical outcomes, including mechanical properties, surface polish, and dimensional accuracy. The utilization of design of experiments (DOE) methodology facilitated a systematic examination of parameters. A predictive model was
Electrochemical machining (ECM) is a highly efficient method for creating intricate structures in electrically conductive materials, regardless of their hardness. Due to the growing demand for superior products and the necessity for quick design adjustments, decision-making in the manufacturing industry has become increasingly complex. This study specifically examines Titanium Grade 19 and suggests the creation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) model for predictive modeling in ECM. The study employs a Taguchi-grey relational analysis (GRA) methodology to attain multi-objective optimization, with the goal of concurrently maximizing material removal rate, minimizing surface roughness, and achieving precise geometric tolerances. Analysis of variance (ANOVA) is used to assess the relevance of process characteristics that impact these performance measures. The ANFIS model presented for Titanium Grade 19 provides more flexibility, efficiency, and accuracy in comparison to
Intelligent vehicles can utilize a variety of sensors, computing, and control technologies to autonomously perceive the environment and make decisions to achieve safe, efficient, and automated driving. If the speed planning of intelligent vehicles ignores the vehicle dynamics state, it leads to unreasonable planning speed and is not conducive to improving the accuracy of trajectory tracking control. Meanwhile, trajectory tracking usually does not consider the road and speed information beyond the prediction horizon, resulting in poor tracking precision that is not conducive to improving driving comfort. To solve these problems, this study proposes a new longitudinal speed planning method based on variable universe fuzzy rules and designs the piecewise preview model predictive control (PPMPC) to realize the vehicle trajectory tracking. First, the three-degrees-of-freedom vehicle dynamics model and trajectory tracking model are established and verified. Then, the variable universe fuzzy
In this work, the large-angle rotational movement and vibration suppression of a flexible spacecraft are carried out based on an adjustable system. First the spacecraft model is transformed into a canonical affine control form, then two fuzzy systems are used: The first (of Takagi–Sugeno type) estimates the feedback linearization control law as a whole, while the second (of Mamdani type) adjusts and stabilizes the control parameters using the gradient descent technique and based on the minimization of the control error rather than the tracking error. Stability results are presented in terms of Lyapunov’s theory, and simulation tests illustrate the significant transient robustness of the closed-loop system against perturbations, the accurate trajectory control, and vibration suppression of the flexible spacecraft. Consequently, as will be shown later, the error will stay confined and converges quickly to zero, confirming the smoothing property of the proposed method using fuzzy logic
Hydrogen fuel cell trucks have enormous development potential in the pursuit of global carbon neutrality and sustainable development. However, their commercialization and mass production are facing challenges in various aspects, especially the durability problem of fuel cells. This paper is intended to set up a high-power hydrogen fuel cell system (FCS) model, considering the fuel cell degradation factors, and based on this, proposes a two-layer fuzzy energy management strategy (EMS) to optimize the life of fuel cell and the total energy consumption of the vehicle. The first control layer provides real-time energy distribution efficiently from multiple sources and thus allows flexibility in energy supply. The second layer regulates the dynamic adjustment of fuel cell output power with degradation of both fuel cells and batteries considered, to make the prolonging of system lifetime possible. In this respect, the equivalent hydrogen consumption, which incorporates fuel cell degradation
Adaptive cruise control (ACC) systems have increasingly become more robust in adapting to the motion of the preceding vehicle and providing safety and comfort to the driver. But conventional ACC hangs with a concern for rear-end safety in the presence of traffic or aggressive car maneuvers. It often leads to getting dangerously close to the vehicle behind in scenarios where there is less space and time for the rear vehicle to adjust. This research article develops an ACC approach that considers the rear vehicle in addition to the front vehicle, thereby ensuring safety with the rear vehicle without compromising the safety of the front vehicle. Two novel methodologies are devised to enhance the ACC system. The first approach involves utilizing fuzzy logic to associate the inputs with the throttle and brake based on the inference rules within a fuzzy logic controller overseeing both vehicles. The other utilizes a cascaded model predictive control (MPC) system framework that integrates a
This study investigates the influence of tungsten inert gas (TIG) welding parameters on the dilution and hardness of AA5052 aluminum alloy. Employing Taguchi’s L27 orthogonal array, the research systematically explores the effects of current, voltage, and welding speed. Analysis of the experimental data utilizes signal-to-noise ratio, analysis of variance (ANOVA), and regression techniques. The study compares a traditional regression model with a fuzzy logic approach for result validation, finding that the latter exhibits marginally better predictive accuracy. Optimal welding parameters are identified as 150 A current, 20 V voltage, and 45 mm/s welding speed, yielding a maximum dilution of 52.81% and hardness of 145.3 HV 0.5. Current emerges as the most significant factor influencing both dilution and hardness. Microstructural examination, hardness profiling, and tensile testing of specimens welded under optimized conditions reveal a characteristic hardness distribution across the weld
The advancement of the automotive industry towards automation has fostered a growing integration between this field and automation. Future projects aim for the complete automation of the act of driving, enabling the vehicle to operate independently after the driver inputs the desired destination. In this context, the use of simulation systems becomes essential for the development and testing of control systems. This work proposes the control of an autonomous vehicle through fuzzy logic. Fuzzy logic allows for the development of sophisticated control systems in simple, easily maintainable, and low-cost controllers, proving particularly useful when the mathematical model is subject to uncertainties. To achieve this goal, the PDCA method was adopted to guide the stages of defining the problem, implementation, and evaluation of the proposed model. The code implementation was done in Python and validated using different looping scenarios. Three linguistic variables were used, one with three
In order to reduce the incidence of traffic accidents and improve passengers’ driving experience, intelligent driving technology has attracted more and more attention. The core content of intelligent driving technology includes environment perception, behavior decision-making and control follow-up. Simulating driver’s behavior decision-making based on multi-source heterogeneous environment information is the key to liberate drivers and become the focus and difficulty of intelligent driving technology. Aiming at this key problem, this paper presents a design method of driving behavior decision maker based on machine learning after fuzzy classification of historical data. Firstly, 1000 sets of driving environment-decision results database are generated randomly according to driving rules and driving state. A fuzzy classification rule is established to classify driving environment information such as speed and relative distance. Then, a driving behavior decision maker is designed based on
Additive Manufacturing (AM), specifically Fused Deposition Modeling (FDM), has become a revolutionary technology for creating intricate shapes using different materials. Polylactic Acid (PLA) is a biodegradable thermoplastic that is commonly used in additive manufacturing (AM) because of its environmentally friendly properties, affordability, and ease of use. The objective of this study is to optimize the FDM parameters for PLA material and create predictive models using the Adaptive Neuro-Fuzzy Inference System (ANFIS) to forecast printing performance. An investigation was carried out through experimental trials to examine the impact of important FDM parameters, such as layer thickness, infill density, printing speed, and nozzle temperature, on critical outcomes such as dimensional accuracy, surface finish, and mechanical properties. The utilization of design of experiments (DOE) methodology enabled a methodical exploration of parameters. A predictive model using ANFIS was created to
Wire Electrical Discharge Machining (WEDM) is an essential manufacturing process used to shape complex geometries in conductive materials such as cupronickel, which is valued for its corrosion resistance and electrical conductivity. The aim of this explorative study is to enhance the efficiency and precision of machining by creating a specialized predictive model using an Adaptive Neuro-Fuzzy Inference System (ANFIS) for cupronickel material. The study examines the intricate correlation between process variables of the WEDM (Wire Electrical Discharge Machining) technique, such as pulse-on time (Ton), pulse-off time (Toff), and discharge current, and crucial machining responses, including surface roughness, material removal rate. Data is collected through systematic experimentation in order to train and validate the ANFIS predictive model. The ANFIS model utilizes the collective learning capabilities of neural networks and fuzzy logic systems to precisely forecast machining responses by
Wire Electrical Discharge Machining (WEDM) is a highly accurate machining approach that is well-known for its capability to create intricate forms in materials with high levels of hardness and intricate geometries. Invar 36, a nickel-iron alloy, is extensively utilized in industries that demand exceptional dimensional stability across a wide temperature range. The objective of this exploration is for optimizing the WEDM parameters of Invar 36 material. Additionally, a predictive model called Adaptive Neuro-Fuzzy Inference System (ANFIS) will be developed to forecast the machining performance. The study involved conducting experimental trials to analyze the influence of crucial factors in WEDM. These parameters included pulse-on time (Ton), pulse-off time (Toff), and current. The objective was to examine their influence on key performance indicators such as material removal rate (MRR), surface roughness (Ra). The methodology of Design of Experiments (DOE) enabled a systematic
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
The objective of the Integrated Chassis Controllers (ICC) is to combine multiple actuators and dynamics controllers to maximize the overall vehicle performance at all driving conditions. It is well known that there are two methods that can be used to develop an ICC. The first is a centralized method, where all the actuators are considered in one controller to ensure a harmonic integration between different actuators. The second method is called decentralized integration, where each actuator is considered in a separate controller and a low-level controller is used to coordinate the operation of the controllers. In this paper, the second method is used to develop a decentralized ICC using a novel controller coordinator based on Genetic Programming (GP). The GP is used to integrate torque vectoring and active rear steering controllers of an 8x8 combat vehicle. The controller is utilized to enhance the lateral stability of the vehicle in various driving conditions. The GP-based method
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
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
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