Adaptive Fuzzy System Research Articles

Short-term hybrid prognostics of fuel cells: A comparative and improvement study

Accurately predicting the performance degradation trend of fuel cells helps take measures in advance and prolong the stack's service life, leading to a novel hybrid forecasting approach. The first grey model prediction method based on residual exponential smoothing optimization (ES-R-GM) can capture the voltage deterioration trend. We explore the integration of two different ES techniques, specifically the double ES (ES2) and cubic ES (ES3), to investigate their effect on enhancing the predictive accuracy of the GM model. The second method of the adaptive network fuzzy inference system (ANFIS) can characterize local nonlinear behavior. We utilize the simulated annealing (SA) algorithm to optimize ANFIS results under different fuzzy rule selection strategies. The outcomes of the two prediction methods mentioned above are combined to create a hybrid prediction using the data fusion method and the moving window technique. Various hybrid methods are evaluated under general conditions and further detailed optimization. The data collected from the experimental platform confirms the suggested hybrid framework. The results show that the hybrid ES3-R-GM + ANFIS-SC method outperformed the single models in final prediction accuracy and can effectively track both global trends and local changes. Simultaneously, it takes less time to calculate than the literature. Moreover, when applied to consistent public datasets, the hybrid approach maintains its robustness and accuracy compared with other hybrid prognostic methods.

Prediction of Power Equipment Emergency Repair Based on Adaptive Neural Network Fuzzy Inference Method

If the emergency repair prediction of power equipment is only made from the perspective of historical spare parts inventory data, it cannot reflect the impact of disaster-causing elements and disaster evolution on the demand for emergency repair spare parts in the future. Therefore, this paper aims to propose a reliable power equipment emergency repair prediction method, and constructs a demand prediction method for emergency repair spare parts of power equipment based on scenario analysis. Constructing a power equipment repair system through intelligent reasoning methods to improve the efficiency of power equipment repair. This article comprehensively uses methods such as literature analysis, model inference, and case simulation verification, this paper innovatively combines the adaptive neural network fuzzy inference system with expert experience. This paper validates the superiority of the prediction method constructed in this paper through comparative analysis. The results show that with the increase of the amount of data, the prediction accuracy of the method proposed in this paper will be improved, which can provide a reference for the subsequent emergency repair prediction of power equipment.

A fuzzy skyhook control strategy optimized by particle swarm strategy for magnetorheological semi-active suspension

In order to solve the problem of unsatisfactory control effect caused by uncertain parameters in the fuzzy skyhook control method, a fuzzy skyhook control strategy optimized by particle swarm strategy is proposed. The magnetorheological (MR) damper is put on the bench for mechanical characteristics experiment, and the forward model of MR damper is established. The adaptive network fuzzy inference system (ANFIS) system in simulation software is used to build its inverse model to verify the accuracy of the forward model of MR damper. A quarter vehicle suspension model is established, and a fuzzy skyhook control strategy based on particle swarm optimization is designed. Numerical simulations are carried out under the excitation of random road. The acceleration, suspension dynamic deflection and tire dynamic load are used to evaluate its performance. Compared with the passive control, the root mean square (RMS) values of the acceleration for the fuzzy skyhook control strategy are improved by 41.9% and 37.6%, the RMS values of the suspension dynamic deflection for the fuzzy skyhook control strategy are improved by 53.3% and 48.0%, and the RMS values of the tire dynamic load for the fuzzy skyhook control strategy are improved by 17.6% and 14.5% under the B-Class and C-Class road excitations. The simulations and experimental results verify the effectiveness of the controller.

Barrier function–based adaptive STSMC for steering feel feedback of SBW vehicle with uncertainty

As an essential component of human–computer interaction in steer-by-wire (SBW) vehicles, the steering feel feedback system provides drivers with accurate steering feel by precisely controlling the output torque of the steering feel motor. Meanwhile, the existence of uncertainties makes the accurate control of steering feel feedback torque become an acknowledged challenging issue. This article proposes an adaptive super-torque sliding mode control strategy based on barrier functions for the steering feel feedback system of SBW vehicles with highly complex dynamics in the presence of uncertainties. First, to achieve intelligent modeling under the influence of uncertainty factors, a dynamic system with unknown uncertainty based on adaptive fuzzy logic system (FLS) is proposed. In the framework of this model, the uncertainty factors introduced by unknown external disturbance, parameter uncertainty, and model couplings are considered as a lumped uncertainty function. Furthermore, the membership functions of the FLS are dynamically adjusted based on nearest neighbor clustering, aiming to enhance the modeling accuracy beyond traditional fuzzy modeling. To further offset the system uncertainty and FLS approximation error, an adaptive super-twisting sliding mode control (STSMC) method based on barrier function is proposed. This algorithm retains the advantages of traditional sliding mode control (SMC) methods in dealing with systems with uncertainty and eliminates the need for a priori knowledge of the upper bound of uncertainties by introducing the adaptive law based on barrier function, avoiding the use of complex identification algorithms. Moreover, it eliminates the gain of overestimation and significantly reduces chattering phenomenon in traditional sliding mode control. Finally, based on Lyapunov stability theory, this article proves the proposed system can achieve convergence within a finite time. Compared with STSMC, traditional SMC and proportional–integral–derivative (PID) control, the results verify the effectiveness of the proposed control method. The proposed strategy provides a new solution to reduce the steering feel feedback torque error under the influence of uncertain factors.

Novel Controller Methodology for the Cessna Citation X Under Turbulence During Cruise

The design of a new control approach for the longitudinal motion of the Cessna Citation X during cruise is performed using a combination of sliding mode control (SMC) system, type 1 fuzzy logic system, and adaptive control system. This methodology is presented for 1) controlling the aircraft pitch rate and 2) stabilizing the aircraft speed during turbulence. The nonlinear model of the aircraft was generated using a simulation platform, which was designed based on flight data obtained from the highest Federal Aviation Administration–certified Level-D Research Aircraft Flight Simulator. The type 1 adaptive fuzzy logic system was implemented to approximate unknown functions for constructing the equivalent part of the SMC system that handled the effects of uncertainties and turbulence. The adaptation laws, derived from the Lyapunov theorem, were used to update the approximated functions in the control law at each flight condition and simulation iteration. Using the control systems combination, the pitch rate could follow the given reference signal, while the aircraft speed remained at a reference value with and without turbulence across the whole flight envelope. Results have shown that the proposed controllers satisfied tracking performance while generating smooth elevator deflection, both of which are important for real-aircraft applications.

Energy management strategy of integrated adaptive fuzzy power system in fuel cell vehicles

Fuel cell vehicles are a reliable solution to address energy shortages. However, when the road conditions are complex, the system distributes power unevenly between fuel cells and lithium batteries, and cannot effectively absorb the energy generated by braking. In response to this issue, an adaptive control strategy is adopted to allocate the required power of the car to two types of batteries in real time. Fuzzy logic is used to continuously optimize the relevant parameters of the controller based on the vehicle state, and a multi-island genetic algorithm is used to optimize the control strategy, enhancing the global search ability of the control strategy and increasing the vehicle’s ability to absorb and reuse the energy generated by braking. The experiment findings denote that the optimized control strategy increases the remaining capacity of lithium batteries by an average of 1.67%, increases energy recovery by an average of 135 W, increases the overall energy recovery rate by an average of 2.8%, and reduces vehicle fuel consumption by an average of 0.24 L/100 Km. It can be concluded that the optimized adaptive fuzzy control strategy can reduce the probability of over-charging and discharging of lithium batteries and improve the battery life. Meanwhile, the optimized strategy can improve the energy reuse rate, reduce vehicle fuel consumption, lower usage costs. The optimized strategy provides a reference for subsequent research on energy management of fuel cell vehicles.

Coordinated Control of Differential Drive-Assist Steering and Direct Yaw Moment Control for Distributed-Drive Electric Vehicles

Direct yaw moment control (DYC) and differential drive-assist steering (DDAS) for distributed-drive vehicles are both realized by allocating the in-wheel motor torque. To address the interference caused by overlapping control objectives, this paper proposes a multilayer control strategy that integrates DYC and DDAS, consisting of an upper controller, a coordinated decision layer, and a torque distribution layer. The upper controller, designed based on the vehicle’s dynamic characteristics, incorporates an adaptive fuzzy control DYC system and a dual PID control DDAS system. The coordinated decision layer is developed utilizing a phase-plane dynamic weighting method, delineating region boundaries by applying the double-line and limit cycle methods. The torque distribution strategy is formulated considering motor peak torque and road adhesion conditions. Multi-condition joint simulation experiments indicate that the proposed multilayer control strategy, integrating the advantages of DYC and DDAS, reduces peak steering wheel torque by approximately 10%, peak yaw rate by around 25%, peak sideslip angle by roughly 29%, and peak sideslip angle rate by about 19%, significantly improving driving stability and maneuvering flexibility.

Assessing water quality of kazerun county in southwest Iran: Multi-analytical techniques, deterministic vs. probabilistic water quality index, geospatial analysis, fuzzy C-means clustering, and machine learning

Water quality is critical to human health and the environment, especially in arid and semi-arid regions. Hence, the objectives of this study were to assess drinking water quality, identify critical parameters, investigate spatial patterns, and investigate accurate predictive models for the water quality index (WQI) in the Kazerun county in southwest Iran. To address this issue using deterministic and probabilistic WQI, correlation matrix, fuzzy C-Means (FCM) clustering, geostatistics, and adaptive network-based fuzzy inference system (ANFIS) with FIS generation by fuzzy C-Means (FCM-ANFIS) and sub-clustering (SC-ANFIS).Various software tools, including Excel, MATLAB, Python, and GIS were used to analyze groundwater data collected from 25 sampling sites. Water parameters, including pH, Cl−, SO4−2, EC, NO3−, NO2−, Ca2+, Mg2+, and F−, were examined. The results showed that F− levels were within acceptable limits set by the US EPA, but about one-third of sites posed potential health risks based on WHO guidelines. In one-third of regions, the levels of Mg2+ exceeded the recommended guidelines. In deterministic and probabilistic approaches, water quality was excellent in 68% and 81.3% of sites, respectively. Sobol sensitivity analysis identified SO4−2> Mg2+>Cl− > EC > F− > NO3− as significant WQI variables. Spearman correlation matrix shows substantial positive correlations between WQI and EC, F−, SO4−2, Mg2+, and Cl− were shown by the Spearman correlation matrix. Based on the FCM results, the southeast and central sites (56% of sites) have similar water quality. In comparison, the northern and four central sites (28% of sites) have distinct regional features, and the southern sites (16% of sites) had unique water quality characteristics. Geostatistical analyses showed that pH had the most substantial local clustering, while SO4−2 had significant high-value clustering. Furthermore, hot spot research revealed specific sites with high pH, F−, NO3−, and Cl− levels. The FCM-ANFIS model outperformed the SC-ANFIS model, emphasizing FCM clustering's importance in water quality forecasting accuracy.

A hybrid genetic-fuzzy ant colony optimization algorithm for automatic K-means clustering in urban global positioning system

This paper introduces an innovative automatic K-means clustering algorithm, namely HGA-FACO, which seamlessly integrates the noise algorithm, Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Adaptive Fuzzy System (AFS). The rationale behind the HGA-FACO algorithm is to mitigate the shortcomings of traditional K-means, particularly the reliance on pre-determined cluster centers and the need for specifying the number of clusters in advance. By optimizing the search strategy, HGA-FACO efficiently circumvents local optima and effectively explores the global optimal solution, resulting in more accurate and stable clustering outcomes. To validate the superiority of the HGA-FACO over conventional K-Means Clustering (KMeans) and other intelligent clustering approaches such as ACO-KMeans, GA-KMeans (GAK), particle swarm optimization KMeans (PSOK), and ACO-GAK, we conducted comprehensive experiments on taxi Global Positioning System (GPS) datasets sourced from four distinct cities. Employing rigorous evaluation metrics including Silhouette Coefficient (SC), Partition Coefficient (PBM), Davies-Bouldin Index (DBI), and Sum of Squared Errors (SSE), the experimental results convincingly demonstrate that the HGA-FACO significantly outperforms its counterparts across all metrics, highlighting its exceptional performance in clustering effectiveness and compactness. While the HGA-FACO faces challenges related to computational complexity and the necessity for initial parameter tuning, its performance limitations on small-sized or unevenly distributed datasets are acknowledged. Nevertheless, the algorithm's advancements in the field of clustering algorithms are undeniable and hold immense potential for practical applications, notably in city hotspot identification.

Control of Dstatcom in Distribution System by Artificial Intelligence Based Controller

Issues linked to power quality are currently of the utmost importance. The utilization of non-linear loads is the cause of the power quality issues. All of these loads cause disruptions in the force of power services, system effectiveness, power factor decrease, etc. because of their nonlinearity. In order to increase power quality in grid connections, this research proposes an adaptive neuro fuzzy conclusion system (ANFIS) based control method for a three- phase DSTATCOM. In addition to improving power factor and balancing power between active and reactive components, the primary function of the DSTATCOM is to compensate for current harmonics introduced into the supply current due to non-linear loads. Matlab software is used to carry out the simulation.

A novel approach for energy consumption management in cloud centers based on adaptive fuzzy neural systems

A novel approach for energy consumption management in cloud centers based on adaptive fuzzy neural systems

Improving the Detectionand Warning Fire System on the Smart Campus Area using ANFIS

The adoption of IoT technology in universities has significantly improved campus security, emergency response, and control systems, particularly through IoT-based surveillance and fire detection systems. Traditional fire detection systems suffer from high false alarm rates, leading to unnecessary resource allocation. This paperproposes an intelligent fire detection system leveraging IoT and adaptive fuzzy systems to enhance accuracy and reduce false alarms.The system uses sensors to collect real-time data, which is analyzed by an Adaptive Neuro-Fuzzy Inference System (ANFIS) to determine alarm levels based on input severity. ANFIS combines fuzzy logic and neural networks, enabling learning and adaptation. Data analyzed by ANFIS is sent to ThingSpeak channels for real-time monitoring. When a fire is confirmed, alerts with fire location, timestamp, and severity are sent via SMS through GSM to the fire management system. The system utilizes cost-effective, small-sized sensors, ensuring repeatable solutions.After training, the ANFIS system achieved 99.59% accuracy, significantly reducing false alarms. It demonstrated faster detection times by reducing the fire detection rate by 28% and maintaining high detection accuracy with fewer sensors, training inputs, and epochs. Utilizing IoT and ANFIS technologies, the system integrates efficiency, speed, and reliability, protecting lives and property and highlighting the importance of safety andtechnology in serving humanity.

Model reference adaptive controller with interpretable fuzzy rules for linear MIMO systems

This paper presents a method for fuzzy model reference adaptive control system design for a partially known multi-input multi-output (MIMO) linear dynamic plant. Differential or convolutional equations can describe the plant to be controlled, and the fuzzy controller is assumed to be from the MIMO sector-bounded nonstationary nonlinearities class. The task is to obtain robust adaptive stabilization. Some absolute stability theorems and integral evaluations of the state and control vectors are applied in the MIMO fuzzy adaptive controller design procedure for the first time. A method for automatically obtaining a near-optimal nonlinear dynamic reference model is described. A practical step-by-step procedure of the MIMO adaptive fuzzy controller design is proposed, resulting in highly interpretable MIMO fuzzy control rules based on triangular fuzzy sets and strong triangular fuzzy partition. An example of the fuzzy controller design according to the proposed procedure is given.

The method of assessing the effectiveness of the security of confidential data of the distributed information system

The paper proposes a method of assessing the effectiveness of the security of confidential data of a distributed information system, based on a model for determining the current threats to the security of confidential data in the information system, on algorithms of fuzzy inference and the theory of fuzzy neural systems, unlike known ones, it uses sufficient and necessary indicators, excludes expert errors , increases the detection of actual threats to the security of confidential information system data by 5%, reduces the cost of purchasing information protection tools from 15 to 30%. It takes into account the following factors: the IT infrastructure of the distributed information system, the capabilities of attackers and their level of motivation in the information system. The proposed approach differs from existing ones by an automated process, the need to involve highly qualified specialists in the field of information security, and low computational complexity; absence of deficiencies in expert assessments; allows you to determine the list of current information security threats in information systems of various classes and types. The task of ensuring the security of confidential data is urgent, which is due to the growth of computer attacks and leaks of information, which are reflected in the statistical data on the commission of crimes in the field of high technologies, the growth of criminal activity using modern communication devices and the Internet. The existing methods of identifying current threats to information security and assessing the effectiveness of the security of confidential data cannot be used at all stages of the life cycle of distributed information systems, they do not take into account the following indicators in the complex: IT infrastructure of distributed systems, current threats to information security, security requirements of confidential data, their cost as important indicators when solving these problems. One of the most important tasks of ensuring the security of confidential data is the evaluation of the effectiveness of the protection system. In this regard, the goal of the research is to improve the quality of the assessment of the effectiveness of the security of confidential data of a distributed information system by determining sufficient and necessary indicators using modern information technologies, which allow the most effective solution of the following tasks: determining the parameters of the adaptive production fuzzy neural systems, which most suitable for solving the tasks, the application of Data Science technologies in data processing, algorithms of fuzzy output.

Performance evaluation of BLDC motor drive mounted in aerial vehicle (drone) using adaptive neuro-fuzzy

The development of autonomous drones equipped with cameras and various sensors has paved the way for their application in agriculture and perimeter security. These aerial drones require specific power, acceleration, high torque, and efficiency to meet the demands of agricultural tasks, utilizing built-in brushless DC (BLDC) motors. However, a common challenge drone’s face is maintaining the desired speed for extended periods. Enhancing the performance of BLDC motors through advanced controllers is crucial to address this issue. This research paper proposes optimizing the size and speed of brushless DC motors for aerial vehicles using an adaptive fuzzy inference system and supervised learning techniques. When these drones carry loads, the BLDC motors must dynamically adjust the drone's speed. During this phase, the motors must control their speed and torque using artificial intelligence controllers like adaptive neuro-fuzzy inference systems (ANFIS) to enhance the drone's functionality, resilience, and safety. This research has conducted analyses focused on improving the performance of BLDC motors explicitly personalized for unmanned aerial vehicle (UAVs). The proposed method will be implemented using MATLAB/Simulink, expecting to significantly enhance the BLDC motor's performance compared to conventional controllers. Comparative analyses will be conducted between traditional and ANFIS controllers to validate the effectiveness of the proposed approach.

Robust Control Based on Adaptative Fuzzy Control of Double-Star Permanent Synchronous Motor Supplied by PWM Inverters for Electric Propulsion of Ships

This study presents the development of an adaptive fuzzy control strategy for double-star PMSM-PWM inverters used in ship electrical propulsion. The approach addresses the current and speed tracking challenges of double-star permanent magnet synchronous motors (DSPMSMs) in the presence of parametric uncertainties. Initially, a modeling technique employing a matrix transformation method is introduced, generating decoupled and independent star windings to eliminate inductive couplings, while maintaining model consistency and torque control. The precise DSPMSM model serves as the foundation for an unknown nonlinear backstepping controller, approximated directly using an adaptive fuzzy controller. Through the Lyapunov direct method, system stability is demonstrated. All signals in the closed-loop system are ensured to be uniformly ultimately bounded (UUB). The proposed control system aims for low tracking errors, while also mitigating the impact of parametric uncertainties. The effectiveness of the adaptive fuzzy nonlinear control system is validated through tests conducted in hardware-in-the-loop (HIL) simulations, utilizing the OPAL-RT platform, OP4510.

Research on the influence of solar radiation fuzzy adaptive system on the wet and hot environment in greenhouse

Chinese solar greenhouse (CSG) is an artificially constructed agricultural facility that provides a suitable environment for crop growth. However, the imbalanced temperature and humidity phenomenon occurring in greenhouse during summer greatly affects crop yield. This article proposes the application of solar radiation fuzzy adaptive system in greenhouse to address this issue. The design process includes hardware and algorithm design. The hardware part involves designing the travel distance of the curtain machine and the film winding machine, laying out the temperature and humidity sensors, and designing the control cabinet. The algorithm is optimized based on fuzzy control theory, data acquisition module consists of temperature and humidity sensors, executive components include the curtain machine and the film winding machine, and the data transmission, storage and feedback module consists of the control cabinet and the cloud-based database. These form an adaptive system, which will be subjected to a 10-day comparative experiment. The experimental results showed that in region 3, the peak value of temperature reduction in the third layer during the daytime was 26.2 °C, and the humidity increased by a maximum of 31.5 %. In region 2, the temperature increased by a maximum of 4.2 °C during the nighttime in the second layer, and the humidity decreased by 4.1 °C. Introduction of adaptive systems can play an important role in maintaining microclimate temperature and humidity in summer.

Study on the use of different machine learning techniques for prediction of concrete properties from their mixture proportions with their deterministic and robust optimisation

The construction industry relies so heavily on concrete that it's crucial to precisely forecast and optimize the strength and workability of concrete mixtures, while reducing costs as much as possible. For this objective, this study tries to predict and optimize the compressive strength and workability (slump) of concrete by using deterministic and robust optimization approaches, so as to determine the optimum concrete mixture proportions, while minimizing cost. Specifically, strength and slump were predicted based on concrete mixture proportions with five different machine learning techniques—support vector machine (SVM), artificial neural network (ANN), fuzzy inference system (FIS), adaptive fuzzy inference system (ANIS), and genetic expression programming (GEP), based on a dataset comprising two hundred concrete mixtures, which has various levels of key ingredients, including cement, water, fine aggregate, coarse aggregate, and size of coarse aggregate, along with their associated measures of strength and workability. These ingredients were used as input parameters, while compressive strength and slump (representing workability) served as output parameters for each mix proportion. Experimental investigations were conducted on fifteen distinct concrete mixes to validate the performance of the five networks, finding that ANFIS can yield the best results both for training and validation. This study provides valuable insights for predicting concrete properties and optimizing concrete mixture proportions, thus helping to maximize strength and workability while minimizing costs.

Comparative analysis and validation of advanced control modules for standalone renewable micro grid with droop controller

A micro grid system with renewable source operation control is a complex part as each source operates at different parameters. This renewable micro grid with multiple sources like solar plants, wind farm, fuel cell, battery backup has to be operated in both grid connected and standalone condition. During grid connection the micro grid, inverter has to inject power to the grid and compensate load in synchronization to the grid voltages. And during standalone condition the inverter is controlled with droop control module which stabilizes the voltage and frequency of the system even during grid disconnection. The droop control module is further updated with new advanced controllers like fuzzy inference system (FIS) and adaptive neuro-fuzzy inference system (ANFIS) replacing the traditional proportional integral derivative (PID) and proportional integral (PI) controllers improving the response rate and for achieving better stabilization. This paper has comparative analysis of the micro grid system with different droop controllers under various operating conditions. Parameters like voltage magnitude (Vmag), frequency (F), load and inverter powers (Pload and Pinv) of the test system are compared with different controllers. A numeric comparison table is given to determine the optimal controller for the inverter operation. The analysis is carried out in MATLAB/Simulink software with graphical and parametric validations.

Adaptive Particle Swarm Optimization of a Photovoltaic System under Partial Shading

The solar photovoltaic (PV) energy is the most prevalent and popular source of energy. But the PV output characteristics are mainly depending on temperature and irradiance and are nonlinear in nature. Therefore, PV array characteristics greatly vary with change in the atmospheric condition. Under partial shading condition (PSC), PV modules will not receive the same level of incident solar irradiance throughout the system due to some obstructions such as: dust, cloudy weather, shadows of nearby objects: buildings, trees, mountains, birds etc… which causes mismatch in PV module characteristics of the PV array and losses arise in the entire PV configuration. Consequently, power extraction from the PV system is reduced and the PSC on the PV array can be minimized by the proper selection of PV configurations, physical relocation of the PV modules and maximum power point tracking techniques (MPPT) to overcome this problem. The present article studies and compares the MPPT based on the Adaptive particle swarm optimization (APSO) algorithm under partial and completely shaded. The perturbation and observation (P&O) method is widely due to its simplicity and easy implementation but the Intelligent and hybrid control such as: fuzzy logic control (FLC) and adaptive neural fuzzy inference system (ANFIS) can track the MPP with better efficiency but in a long time compared to conventional approaches. In addition, these methods need big data for good results and the data problem is regulated with the evolutionary algorithms and searching the duty cycle (d) in a shorter time than FLC and ANFIS. The principle of PSO, grey wolf optimization (GWO), and APSO techniques is the search for a global solution, and it have good behaviour under PSC but APSO can be classified as best solution between the studied approaches. The simulation results, which are presented in MATLAB/Simulink software, show the effectiveness of the proposed APSO technique.