Adaptive Fuzzy Neural Network Research Articles

Enhancing grid stability through predictive control and fuzzy neural networks in flywheel energy storage systems integration

Renewable energy systems, exemplified by solar and wind power, are increasingly integrated into modern power grids to mitigate environmental impact and reduce reliance on fossil fuels. However, the inherent intermittency and unpredictability of renewable energy sources pose challenges to grid stability and reliability. Energy Storage Systems (ESS) offer a promising solution to address these challenges by smoothing out power fluctuations and ensuring a consistent power supply. Among various ESS technologies, Flywheel Energy Storage Systems (FESS) have emerged as a noteworthy contender due to their rapid response times, low operating costs, and extended lifespan. This paper focuses on investigating the operation of a novel unit comprising a solar power system integrated with a Flywheel Energy Storage System (PV-FESS). The aim is to develop an effective control algorithm utilizing adaptive fuzzy neural networks and model predictive control (ANFIS-MPC) to manage power fluctuations stemming from renewable energy sources within the grid. The proposed control strategy aims to optimize the operation of the PV-FESS system by dynamically adjusting the energy absorption or release of the flywheel to maintain grid stability. Simulation studies conducted using Matlab-Simulink/Simcape software validate the efficacy of the ANFIS-MPC algorithm in mitigating abnormal fluctuations from renewable energy sources. The results demonstrate that the PV-FESS system effectively balances power fluctuations, ensuring a stable and reliable power output to the grid.

A novel temporal adaptive fuzzy neural network for facial feature based fatigue assessment

When engaging in activities such as using video display terminals, driving, or sports, effective fatigue monitoring is crucial. Nevertheless, obtaining biological information through contact devices may interfere with normal activities. Deterministic expressions in mainstream machine learning have limitations in reflecting the continuous, dynamic, and fuzzy process of fatigue status. Additionally, fatigue is a cumulative process where the previous state impacts the assessment of the current one. To address these needs and challenges, this paper proposes a novel model based on facial videos called the temporal adaptive fuzzy neural network (TAFNN) for fatigue assessment. TAFNN utilizes an adaptive fuzzy neural network as its foundation and employs causal and dilated convolutions for the time information processing method to achieve time series extraction and fatigue assessment. It leverages facial physiological and motion features to minimize interference during assessment. Furthermore, TAFNN introduces a new calculation method for rule antecedents to enhance stability. Experimental results demonstrate that TAFNN effectively captures the cumulation and fuzziness of state changes, outperforming other widely adopted methods in both assessment ability and runtime performance. The improved rule antecedent calculation method successfully mitigates the issue of multiple memberships rapidly approaching zero after combination. Through 1000 repeated experiments, the enhanced method reduces TAFNN’s instability by 81.58%.

Event-Triggered Adaptive Fuzzy Neural Network Output Feedback Control for Constrained Stochastic Nonlinear Systems.

This article investigates the problem of command-filtered event-triggered adaptive fuzzy neural network (FNN) output feedback control for stochastic nonlinear systems (SNSs) with time-varying asymmetric constraints and input saturation. By constructing quartic asymmetric time-varying barrier Lyapunov functions (TVBLFs), all the state variables are not to transgress the prescribed dynamic constraints. The command-filtered backstepping method and the error compensation mechanism are combined to eliminate the issue of "computational explosion" and compensate the filtering errors. An FNN observer is developed to estimate the unmeasured states. The event-triggered mechanism is introduced to improve the efficiency in resource utilization. It is shown that the tracking error can converge to a small neighborhood of the origin, and all signals in the closed-loop systems are bounded. Finally, a physical example is used to verify the feasibility of the theoretical results.

Adaptive fuzzy-neural network effectively disturbance compensate in sliding mode control for dual arm robot

In this study, an Adaptive Backstepping Sliding Mode Controller (ABSMC) is introduced based on the Radial Basis Function (RBF) neural network and a fuzzy logic modifier. The proposed method is used to control a Dual-Arm Robot (DAR) – a nonlinear structure with unstable parameters and external disturbances. The control aims to track the motion trajectory of both arms in the flat surface coordinate within a short time, maintaining stability, and ensuring that the tracking error converges in finite time, especially when influenced by unforeseen external disturbances. The nonlinear Backstepping Sliding Mode Control (BSMC) is effective in trajectory tracking control; however, undesired phenomena may occur if there are uncertain disturbances affecting the system or model parameters change. It is proposed to use a neural network to estimate a nonlinear function to handle unknown uncertainties of the system. The neural network parameters can be adaptively adjusted to optimal values through adaptation rules derived from Lyapunov's theorem. Additionally, fuzzy logic theory is also employed to adjust the controller parameters to accommodate changes or unexpected impacts. The performance of the Fuzzy Neural Network Backstepping Sliding Mode Control (FNN-BSMC) is evaluated through simulation results using Matlab/Simulink software. Two simulation cases are conducted: the first case assumes stable model parameters without uncertain disturbances affecting the joints, while the second case considers a model with changing parameters and disturbances. Simulation results demonstrate the effective adaptability of the proposed method when the system model is affected by various types of uncertainties from the environment

Fault Detection, Classification and Localization Along the Power Grid Line Using Optimized Machine Learning Algorithms

Distributed energy generation increases the need for smart grid monitoring, protection, and control. Localization, classification, and fault detection are essential for addressing any problems immediately and resuming the smart grid as soon as possible. Simultaneously, the capacity to swiftly identify smart grid issues utilizing sensor data and easily accessible frequency and voltage data from PMU devices is a prerequisite of this task. Therefore, this paper proposes new methods using fuzzy logic and adaptive fuzzy neural networks as well as machine learning and meta-heuristic algorithms. First, line voltage is used by a fuzzy thresholding method to estimate when a transmission line defect would develop in less than 1.2 clock cycles. Next, features taken from frequency signals in the real-time interval are utilized to classify the type of error using machine learning systems (decision tree algorithm and random forest algorithm) optimized with wild horse meta-heuristic algorithm. To locate the precise problem location, we finally use a neural fuzzy inference system that is capable of adapting to new data. We employ a simulated power transmission system in MATLAB to test our proposed solutions. Mean square error (MSE) and confusion matrix are used to assess the efficiency of a classifier or detector. For the decision tree algorithm method, the detector attained an acceptable MSE of 2.34e−4 and accuracy of 98.1%, and for the random forest algorithm method, an acceptable MSE of 3.54e−6 and accuracy of 100%. Furthermore, the placement error was less than 153.6 m in any direction along the line.

Research on Some Control Algorithms to Compensate for the Negative Effects of Model Uncertainty Parameters, External Interference, and Wheeled Slip for Mobile Robot

In this article, the research team systematically developed a method to model the kinematics and dynamics of a 3-wheeled robot subjected to external disturbances and sideways wheel sliding. These models will be used to design control laws that compensate for wheel slippage, model uncertainties, and external disturbances. These control algorithms were developed based on dynamic surface control (DSC). An adaptive trajectory tracking DSC algorithm using a fuzzy logic system (AFDSC) and a radial neural network (RBFNN) with a fuzzy logic system were used to overcome the disadvantages of DSC and expand the application domain for non-holonomic wheeled mobile robots with lateral slip (WMR). However, this adaptive fuzzy neural network dynamic surface control (AFNNDSC) adaptive controller ensures the closed system is stable, follows the preset trajectory in the presence of wheel slippage model uncertainty, and is affected by significant amplitude disturbances. The stability and convergence of the closed-loop system are guaranteed based on the Lyapunov analysis. The AFNNDSC adaptive controller is evaluated by simulation on the Matlab/simulink software R2022b and in a steady state. The maximum position error on the right wheel and left wheel is 0.000572 (m) and 0.000523 (m), and the angular velocity tracking error in the right and left wheels of the control method is 0.000394 (rad/s). The experimental results show the theoretical analysis’ correctness, the proposed controller’s effectiveness, and the possibility of practical applications. Orbits are set as two periodic functions of period T as follows.

Robust compensation with adaptive fuzzy Hermite neural networks in synchronous reluctance motors

In this paper, a robust compensation scheme using adaptive fuzzy Hermite neural networks (RCAFHNN), for use in synchronous reluctance motors (SRMs), is proposed. SRMs have a simple underlying mathematical model and mechanical structure, but are affected by problems related to parameter variations, external interference, and nonlinear dynamics. In many fields, precise control of motors is required. Although the use of neural network and fuzzy are widespread, such controllers are affected by unbound nonlinear system model. In this study, RCAFHNN, based on an adaptive neural fuzzy interface system (ANFIS), was used to bound motor system model controller algorithm. RCAFHNN can be characterized in three parts. First, RCAFHNN offers fuzzy expert knowledge, a neural network for online estimation, and recursive weight estimation. Second, the replacement of the Gaussian function by the Hermite polynomial in RCAFHNN enables reduced membership function training times. Third, the system convergence and robustness compensation of RCAFHNN were confirmed using Lyapunov stability. RCAFHNN ameliorates the problems of external load and system lump uncertainty. The experimental results, in which the output responses of RCAFHNN and ANFIS (adaptive neural fuzzy interface systems) were compared, demonstrated that RCAFHNN exhibited superior performance.

Heart sound classification algorithm based on time-frequency combination feature and adaptive fuzzy neural network

Feature extraction methods and classifier selection are two critical steps in heart sound classification. To capture the pathological features of heart sound signals, this paper introduces a feature extraction method that combines mel-frequency cepstral coefficients (MFCC) and power spectral density (PSD). Unlike conventional classifiers, the adaptive neuro-fuzzy inference system (ANFIS) was chosen as the classifier for this study. In terms of experimental design, we compared different PSDs across various time intervals and frequency ranges, selecting the characteristics with the most effective classification outcomes. We compared four statistical properties, including mean PSD, standard deviation PSD, variance PSD, and median PSD. Through experimental comparisons, we found that combining the features of median PSD and MFCC with heart sound systolic period of 100-300 Hz yielded the best results. The accuracy, precision, sensitivity, specificity, and F1 score were determined to be 96.50%, 99.27%, 93.35%, 99.60%, and 96.35%, respectively. These results demonstrate the algorithm's significant potential for aiding in the diagnosis of congenital heart disease.

Adaptive fuzzy neural network finite‐time command filtered control of n‐link robotic systems with actuator saturation

AbstractThe finite time tracking control of n‐link robotic system is studied for model uncertainties and actuator saturation. Firstly, a smooth function and adaptive fuzzy neural network online learning algorithm are designed to address the actuator saturation and dynamic model uncertainties. Secondly, a new finite‐time command filtered technique is proposed to filter the virtual control signal. The improved error compensation signal can reduce the impact of filtering errors, and the tracking errors of system quickly converge to a smaller compact set within finite time. Finally, adaptive fuzzy neural network finite‐time command filtered control achieves finite‐time stability through Lyapunov stability criterion. Simulation results verify the effectiveness of the proposed control.

Adaptive fuzzy convolutional neural network for medical image classification

Due to resource constraints and the diverse nature of the devices involved, energy efficiency and scalability enhancement are important challenges in the Internet of Things (IoT) ecosystem. It is difficult to manage the edge resources in a consistent way that promotes cooperation and sharing of resources across the devices because of the heterogeneity of the Internet of Things devices and the dynamic nature of the surroundings in which edge computing takes place. In this research, we offer Intelligent techniques for resource optimization for Internet of Things devices. This is a full-stack system architecture to support across heterogeneous Internet of Things devices that have limited resources. The paradigm that is being suggested is made up of several edge servers, and Internet of Things (IoT) devices have the qualities of being heterogeneity-compatible, high performing, and intelligently adaptable. In order to do this, a clustered environment is generated in heterogeneous Internet of Things devices, and a routing method called Search and Rescue Optimization is used to set up connections between the CH nodes. After that, the edge nodes that are closest to the source of the data are chosen for transmission. Overall, what was suggested Multi-Edge-IoT accomplishes superior efficiency in terms of consumption of energy, latency, communication overhead, and packet loss rate than existing approaches to attaining energy efficiency in the Internet of Things.

Data-Driven Optimal Control for Municipal Solid Waste Incineration Process

Municipal solid waste incineration (MSWI) is a dynamic industrial process involving complex physical and chemical reactions. Due to the uncertain municipal solid waste (MSW) composition and dynamic operation conditions, it is difficult to guarantee optimal operation for the MSWI process. To solve this problem, a data-driven optimal control scheme is proposed with a hierarchical control structure. In the set points optimization stage, an online adaptive fuzzy neural network (OAFNN) is designed to construct the objective functions, including combustion efficiency and NOx emission concentration. An adaptive mutation particle swarm optimization (AMPSO) algorithm is developed to obtain the optimal set points of oxygen content in flue gas. In the control stage, a double long short-term memory neural networks-based model predictive control (DLSTM-MPC) strategy is exploited. A self-organizing long short-term memory (SOLSTM) network is employed to predict the oxygen content in flue gas with a compact structure. To reduce the influences from dynamic disturbances and further improve the tracking control performance, another long short-term memory (LSTM) neural network is established to correct the prediction results. Finally, the set points optimization method and DLSTM-MPC strategy are combined to realize the optimal operation of the MSWI process. The effectiveness of the proposed optimal control scheme is verified by real industrial data. The experimental results demonstrate that the optimal control scheme can achieve promising tracking control performance of oxygen content in flue gas and improve the operational performance of the MSWI process.

Adaptive Fuzzy Neural Network Command Filtered Impedance Control of Constrained Robotic Manipulators With Disturbance Observer.

This article proposes an adaptive fuzzy neural network (NN) command filtered impedance control for constrained robotic manipulators with disturbance observers. First, barrier Lyapunov functions are introduced to handle the full-state constraints. Second, the adaptive fuzzy NN is introduced to handle the unknown system dynamics and a disturbance observer is designed to eliminate the effect of unknown bound disturbance. Then, a modified auxiliary system is designed to suppress the input saturation effect. In addition, the command filtered technique and error compensation mechanism are used to directly obtain the derivative of the virtual control law and improve the control accuracy. The barrier Lyapunov theory is used to prove that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded. Finally, simulation studies are performed to illustrate the effectiveness of the proposed control method.

Chebyshev Fuzzy Neural Network Super-Twisting Terminal Sliding-Mode Control for Active Power Filter

To suppress the harmonic pollution, a global fast super-twisting terminal sliding mode controller (GFSTTSMC) using an adaptive recurrent Chebyshev fuzzy neural network (ARCFNN) is proposed to eliminate the current distortion for an active power filter (APF) with unknown model nonlinearities. First, a global fast terminal sliding mode controller (GFTSMC) is adopted due to its advantages in finite-time convergence and faster convergence rate of tracking error. Second, a supertwisting sliding mode control (STSMC) algorithm having an edge in terms of weakening the chattering and smoothening the input signals, is designed to promote the dynamic tracking capability of APF. Third, by combining the fuzzy neural network and Chebyshev polynomials, the ARCFNN is utilized to estimate the unknown model of the APF system due to its strong generalization and approximation ability. Ultimately, the availability of the ARCFNN-GFSTTSMC scheme has been fully numerically and experimentally verified in an APF prototype, showing the improvement of characteristic performance than other strategies.

A Target Damage Effectiveness Assessment Mathematical Calculation Method with Uncertain Information Based on an Adaptive Fuzzy Neural Network

Aiming to address the issue of assessing the effectiveness of target damage in air defense interception under projectile and target intersection, this paper proposes a novel target damage effectiveness assessment mathematical calculation method using an adaptive fuzzy neural network. The design and calculation methods introduce the target cabin’s damage weight factor, the dispersion error and coverage density and fire density of warhead fragment, and the ratio of the number of warhead fragments to the cross-sectional area of the target as the primary damage factors, and we establish a new model for calculating the target damage efficiency using Takagi-Sugeno adaptive fuzzy neural network with the multiple damage factors. In the designed model, we take the characteristic parameters of warhead fragments formed by the projectile explosion as the sample data and learn the mapping relationship between damage factors and damage effect expressed by the system of fuzzy rule. Combined with the vulnerability weight and damage factors of the target, the fuzzy rule system is applied to predict and calculate the damage probability and damage effectiveness. In addition, we use shot-line technology to set up the intersection criterion between warhead fragments and target, analyze the coordinate relationship of the projectile explosion relative to the target based on the ground coordinate system, and derive the distribution density function of warhead fragment and research a calculation model for target damage probability with multiple vulnerable cabins. Finally, we use the established model to train, test, and calculate the data from the actual projectile and target intersection damage test and present the comparison results to other damage calculation methods. By calculation and comparative analysis, the results show the smaller the distance between projectile and target, as well as the smaller the average intersection angle, and the larger the weight coefficient of the warhead fragment covering the target cabin, the total target damage probability increases obviously. In the two experimental calculations, the target cabin with the largest weight coefficient was considered, and compared with the existing literature, the damage probability of the proposed calculation method was increased by 9.13% and 10.93%, respectively, and it is clear that the proposed target damage assessment method can effectively reflect the real target damage effectiveness in the state of projectile and target intersection.

Dynamic Optimal Control for Wastewater Treatment Process Under Multiple Operating Conditions

Wastewater treatment process (WWTP) is operated with multiple conditions. Accurately identify these conditions and satisfy the different requirements are the keys to guarantee the optimal operation of WWTP. To solve this problem, a dynamic optimal control (DOC) strategy is designed in this paper. First, with the aid of the predicting models by adaptive fuzzy neural network for effluent nitrate and total nitrogen, different operating conditions can be identified. Corresponding to each condition, changing number of operating objectives are formulated to match the different requirements. Second, due to the changeable operating objectives can cause the expanded/contracted dimension of Pareto-optimal set, a dynamic multi-objective particle swarm optimization algorithm is developed. In this algorithm, self-adjusting mechanism of population size and global optimal solution are designed to derive the optimal solutions of control variables. Third, fuzzy controllers, matched with the different conditions, are designed to trace these optimal solutions. Finally, the effectiveness of DOC strategy is tested on a real WWTP. The results demonstrate that this proposed DOC strategy enables to achieve promising operating performance. <i>Note to Practitioners</i>&#x2014;This article aims to develop an optimal control strategy to match the multiple conditions and improve the operating performance. To achieve this goal, a dynamic optimal control (DOC) strategy, with the consideration of different operating conditions, is designed. Two key points are contained, the identification of multiple operating conditions and the satisfaction of different requirements. Due to each condition confronts with different requirements, it is important to accurately identify the conditions and construct the corresponding operating objectives. Based on the obtained operating data, changeable operating objectives are established to describe the different conditions. In addition, to satisfy the operating requirements, it is necessary to optimize the changeable objectives, but the changes in the number of objectives can cause the expanded/ contracted of Pareto-optimal set. A dynamic multi-objective particle swarm optimization algorithm, with self-adjusting mechanism of population size and global optimal solution, is designed to deal with this kind of optimization problem. The effectiveness and applicability of the proposed DOC strategy are evaluated on a pilot platform of a real WWTP, showing the improvement on the operating performance. These advantages can be valuable for transplanting this strategy to other real wastewater treatment plants to realize the optimal operation.

Designing a Fuzzy Adaptive Neural Network Leveraging a Feature Self-Enhancement Unit and Statistical Selection Methods

In this study, we propose an advanced category of a fuzzy adaptive neural network (FANN) based on a feature self-enhancement unit (FSU) and statistical selection methods (SSMs). Undoubtedly, the raw data contain a large amount of information with varying importance. One of the most important tasks for regression model design is to avoid losing these important details. However, the raw data cannot participate in the whole training process due to the data fuzzification unit in the structure of conventional fuzzy neural networks (FNNs). Meanwhile, the polynomial-based neuron also has its limitations as a common node in FNNs. For example, in the polynomial neuron, the complexity of the neurons increases exponentially with the increase in network size. Consequently, overfitting and insufficient raw data information are two primary drawbacks in the structure of conventional FNNs. To address these limitations, we designed the FSU and the SSM as effective vehicles to reduce data dimensionality and select significant raw information. The proposed FANN also demonstrates the capability to improve modeling accuracy in neural networks. Moreover, this is the first instance of integrating statistical methods and feature self-enhancement techniques into a fuzzy model. To validate and showcase the superiority of the proposed FANN, the model is applied to 16 machine learning datasets, outperforming other comparative models in 81.25% of the datasets utilized. Additionally, the proposed FANN model outperformed the latest FNN models, achieving an average 5.1% increase in modeling accuracy. The comparison experiment section not only includes classical machine learning models but also references the experimental results from two recent related studies.

An Adaptive Fuzzy Neural Network Technique for Coronavirus-based Bio-nano Communication Systems

Background: At the end of December 2019, a case of pneumonia of unknown cause was reported in Wuhan, China. A new coronavirus was then identified as the leading cause of this controversial pneumonia, changing how people worldwide live. Although science has achieved significant advances in COVID-19 during the last two years, the world must do much more to prepare for the emergence and development of viruses that may spread rapidly. Methods: This COVID-19 research project proposes a diagnosis component, an adaptive fuzzy neural network technique, serving as a virus-based bio-nano communication network system that can understand the behavior of the biological and nonbiological processes of COVID-19 virusbased disease diagnosis and detect the pandemic at the early stage. The proposed method also integrates multiple new communication technologies, allowing doctors to monitor and test patients remotely. Results: As an outcome of this technique, the receiver biological nanomachines can adjust the 1/0-bit detection threshold according to the viruses previously encountered. This adjustment contributes to the resolution of the intersymbol interference issue caused by residual particles that arrive at the receiver owing to previous bit transmission and reception noise. Diffusionbased coronavirus nanonetwork systems are evaluated using MATLAB simulations that consider each detection strategy’s most crucial characteristics of the communication system environment. The proposed technique’s performance is evaluated in the presence of different noisy channel sources, which demonstrate a significant increase in uncoded bit error rate performance when compared to the previous threshold detection systems. Conclusion: Thus, diffusion-based coronavirus nanonetwork systems can be the future tool to investigate the existence of a specific type of virus that spreads through lung cells in the respiratory system.

Strength Failure of CO2 Injection Tubular Strings Considering CO2 Phase Transition

Compared with traditional injection tubular strings, the stresses on CO2 injection tubular strings are more complex. The results from field applications show that the phase transition of CO2 fluid in CO2 injection strings is an important factor in the calculation of temperature distribution and analysis of string mechanics. Therefore, we propose a strength analysis method for CO2 injection tubular strings that considers the CO2 phase transition. We selected four CO2 injection strings in an oil field in China as examples to evaluate their strength and safety. First, we established coupled differential equations for the temperature, pressure, and physical parameters of CO2 injection strings according to the theory of fluid flow and heat transfer. Then, we used an adaptive fuzzy neural network to construct the model for calculating the CO2 convection heat transfer coefficient and used this to obtain the high-precision convection heat transfer coefficients of tubular strings under conditions of CO2 flooding. We analyzed the injection-string deformations that resulted from the piston, spiral bending, expansion, friction and temperature effects according to the stress characteristics of the CO2 injection strings with packers under different working conditions. Finally, we performed mechanical analyses on the collapse resistance, internal pressure resistance, and tensile and triaxial stresses of the CO2 injection strings, and the results of these analyses provide a theoretical basis for the strength analysis of CO2 injection strings.

A comparative study of FNN‐based dynamic sliding mode control for DC‐DC converters

SummaryIn this paper, a dynamic sliding‐mode variable‐structure control (VSC) is proposed for the voltage tracking control for DC‐DC boost converters, combining with a type of fuzzy neural networks (FNNs) based on different membership functions. In terms of the uncertainties from parameter perturbations and unmodeled dynamics in the DC‐DC converter, the FNNs including, respectively, Gaussian membership functions and ellipsoidal‐type membership functions are employed to approximate the uncertainties. Based on an average model for the present converter, a novel dynamic VSC that is induced by an auxiliary dynamics of the duty cycle is designed, by exploring an ideal line sliding surface based on the reference inputs and the duty cycle for the converter. Under the basis of the adaptive FNN dynamic VSC law, the tracking error system of the DC‐DC boost converter is analyzed to be boundedly stable. Eventually, the validation and the superiority of the proposed dynamic VSC method are shown in some comparative simulations.

Research on a new power distribution control strategy of hybrid energy storage system for hybrid electric vehicles based on the subtractive clustering and adaptive fuzzy neural network

Research on a new power distribution control strategy of hybrid energy storage system for hybrid electric vehicles based on the subtractive clustering and adaptive fuzzy neural network