Neuro-fuzzy Inference System Controller Research Articles

Modeling and adaptive neuro-fuzzy inference system control of quarter electric vehicle

Electric vehicles (EVs) have gained importance in recent years, prompting the development of several control systems to improve their efficiency and performance. In this work, a quarter electric vehicle (QEV) was controlled using a conventional proportional integral derivative (PID) and fuzzy controller to examine and compare with the response of the adaptive neuro-fuzzy inference system (ANFIS) controller. The response of the ANFIS controller was evaluated using MATLAB/Simulink according to different parameters and compared with those of other controllers. In addition, the simulation was based on different driving conditions such as the acceleration and deceleration modes and the type of road: wet and dry. The simulations were carried out on a longitudinal electric vehicle model based on a brushless DC motor, including the Pacejka tire model. The results showed that the ANFIS controller outperformed the PID and fuzzy logic controllers, providing superior dynamic responsiveness and stability when the ANFIS controller smoothly followed the input speed and the longitudinal slip value reached 3%.

Optimizing Power Control for Dual Excited Synchronous Generators in Wind Turbine Systems

This study presents a MATLAB Simulink-based approach for optimizing power control in wind turbine systems with dual excited synchronous generators (DESGs), implementing an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller. DESGs offer advantages in efficiency and reliability, necessitating effective power control strategies. The proposed methodology integrates Simulink models of wind turbine dynamics, DESG behavior, and an ANFIS controller to optimize power output while ensuring stability and minimizing losses. Various factors such as wind speed variations and grid conditions are incorporated into the simulation environment. The ANFIS controller dynamically adjusts DESG control parameters based on real-time inputs, enhancing system adaptability and performance. Simulation graphs validate the efficiency of proposed approach in improving DESG performance and overall wind turbine system efficiency. This research contributes to advancing renewable energy technologies by leveraging MATLAB Simulink and ANFIS controllers for optimized power control in DESG-based wind turbine systems. Key Words: Wind power generation, Grid, DESG, MSC, GSC, and ANFIS.

Simulation and Analysis of Optimal Power Injection System Based on Intelligent Controller

Many countries are seeing significant improvements in the fields of building, urban planning, technology, network management, and the need for diverse forms of energy and different generating techniques, as well as the necessity for low and middle distributing voltage in all areas. Depending on the needs of the user, starting needs, capacity, intended usage, waste output, and economic efficiency, many methods are used to generate this energy. To solve the problems brought on by the suggested excessive voltage of the provided system, energy collection devices can be used, and they can be used efficiently with smart grid intelligent control systems. A mathematical model was developed with four main components: simulation, correlation, and evaluation following the solar the program was set of photovoltaic panels solar panels, An Adaptive Neuro-Fuzzy Inference System (ANFIS) controller based on Maximum Power Point Tracking (MPPT), as well as 600-volt electric network, in order to examine and analyze the viability of the proposed network collaboration and storage of electricity in private photovoltaic networks based on solar energy. This phase next looks at the output power impact on the network, as well as the influence of network temperature and coincident radiation. An analysis was conducted to ascertain the impact of these basic limitations on actual use. This section covers the computer simulation of the proposed system. The final section contains the created system's block diagram. The system's input light is transformed into electricity that circulates in this system's power. The main electrical system with a 600-volt capacity can use this energy. The suggested system was evaluated using MATLAB simulation tapes and graphing for each system component, and the simulation outcomes of the entire system were considered.

Modified switching control of SRM drives for electric vehicles application with torque ripple reduction

The switched reluctance motor (SRM) offers extensive prospects, particularly within the realm of electric vehicles (EVs). Its robust construction, wide speed range, high torque density, and efficiency provide significant advantages that surpass other motors. Nonetheless, controlling these motors is more intricate when compared to conventional DC brushed or AC motors. This complexity arises due to the non-linear inductance characteristics of SRMs, resulting in undesirable effects like torque ripple, vibrations, and noise. Using the conventional full-bridge inverter, three switching approaches are highlighted for various operating modes of an EV. This aims to reduce costs and the number of switching pulses, leading to a more compact system, elimination of dead time, and switching losses. The MATLAB/Simulink platform was utilized to examine the operational effectiveness of a three-phase 6/4 poles SRM drive. Additionally, this paper focuses on mitigating torque ripple concerns by employing an adaptive neuro-fuzzy inference system (ANFIS) controller that has better efficiency and superior responses compared to conventional controllers such as fuzzy logic control (FLC) and proportional integral (PI) control. The results of the simulation encourage the practical implementation of the system, which is the next step in the author’s research.

Type‐2 fuzzy‐based adaptively predictive controlled variable frequency transformer coordinated to SMES for improved load frequency control

AbstractTraditionally, phase shifters powered by power electronics are used in conjunction with energy storage devices to improve the load frequency control. Conventional phase shifters provide minimal inertia and offer a significant threat to power quality due to harmonics. This study suggests the type‐2 fuzzy based adaptively predictive controlled variable frequency transformer (F2‐APC‐VFT) as a phase shifter to work in coordination with superconducting magnetic energy storage (SMES) for improved load frequency control. To assess the effectiveness of coordinated control, a two‐area, four‐machine linked power system with F2‐APC‐VFT installed in succession with the connecting line near to area one and SMES in the other area is employed. A type‐2 fuzzy controller is used to tune the cost function weights of the adaptive predictive controller (APC). A simulation study is conducted in MATLAB to show the effectiveness of the suggested strategy. Tuning the cost function weights with a type‐2 fuzzy controller considerably impacts in minimising the frequency and tie‐power deviations. The effectiveness of the F2‐APC is verified by comparing its performance with that of an adaptive neuro fuzzy inference system (ANFIS) controller and constant weight‐based adaptive predictive controller (C‐APC).

Power Quality Gain of Self-Lift Luo Converter used for Hybrid Renewable Power System with Battery Grid Integration

The renewable power sources such as wind as well as photovoltaic (PV) energy are fashionable for residential and commercial applications. For the reason, that eco friendlessness plus expenditure are effective. This paper intends a Self-Lift Luo converter integrated with solar photovoltaic system to the utility grid along with its Adaptive Neuro-Fuzzy Inference System (ANFIS) and Recurrent Neural Network (RNN) control strategies are proposed. By achieve an energy management for the proposed system using Bidirectional Battery converter along with battery system. Power supervision move towards is suggested to control the DC bus voltage by way of Self-Lift Luo converter. The wind power with Doubly Fed Induction Generator (DFIG) based grid integration with a PI controller. Under this work, DSTATCOM has been used to improve the quality of power under different load conditions. The obtained results designate that the proposed approach delivers recovered performance with enhanced efficiency and nominal harmonics. The intact system is validated through a MATLAB simulation.

ANFIS Controlled MMC-UPQC to Mitigate Power Quality Problems in Solar PV Integrated Power System

This paper focuses on the implementation of a Modular Multilevel Converter (MMC) based Unified Power Quality Conditioner (UPQC) in a grid-integrated Photovoltaic (PV) system. The integration of photovoltaic (PV) systems into the grid brings numerous benefits in terms of renewable energy utilization like sustainable energy generation, energy security, grid resilience, and economic growth. However, the voltage source converters (VSCs) employed in PV systems can introduce power quality (PQ) issues, including voltage fluctuations, harmonics, and reactive power imbalances and grid instability. The objective of this work is to enhance power quality by mitigating voltage and current fluctuations and harmonics generated due to VSC, compensating reactive power requirements by the load and PV system, and regulating the DC link voltage. For DC voltage regulation, an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller is employed to control for MMC-UPQC. The proposed control strategy is simulated using MATLAB/SIMULINK software and the results are compared with conventional Proportional Integral (PI) and fuzzy controllers. Various dynamic conditions such as voltage sag and swell at the point of common coupling (PCC), changes in irradiance for the PV system, and load variations are considered. The simulation results demonstrate that the ANFIS-controlled MMC-based UPQC outperforms the PI and fuzzy controllers in terms of power quality improvement. It effectively suppresses harmonics, maintains a stable DC link voltage, and compensates for reactive power fluctuations. The proposed control strategy provides superior performance and achieves better power quality compared to conventional controllers. The findings of this work highlight the potential of using an ANFIS controller in MMC-based UPQC systems for grid-integrated PV systems.

Mitigation of power quality disturbances in wind energy conversion systems with fed unified power flow controllers using cascaded adaptive neuro-fuzzy inference system controller

Mitigation of power quality disturbances in wind energy conversion systems with fed unified power flow controllers using cascaded adaptive neuro-fuzzy inference system controller

Fuzzy Control Strategies Development for a 3-DoF Robotic Manipulator in Trajectory Tracking

This research delves into the development and evaluation of two distinct controllers for a 3-DoF robotic arm in the context of Industry 4.0. Two primary control strategies are presented in the study. The first is a Fuzzy Logic Controller that utilizes joint position error and its derivative as inputs, employing a set of 9 control knowledge rules. The second is an Adaptive Neuro-Fuzzy Inference System (ANFIS) Controller, trained to learn the inverse dynamic model of the robot through a structured dataset. The research emphasizes the importance of accurate parameter tuning and data acquisition to achieve optimal control system performance. Extensive experimentation was conducted to evaluate the controllers’ performance in trajectory tracking and their response against external disturbances, such as load variations. The controllers exhibited remarkable precision and proficiency in tracking reference trajectories, with minimal deviations, overshoots, or oscillations. A quantitative analysis using performance indices such as root mean square error (RMSE) and the integral of the absolute value of the time-weighted error (ITAE) further confirmed the controllers’ effectiveness. Notably, the ANFIS Controller consistently outperformed the Fuzzy Logic Controller, demonstrating superior precision in trajectory tracking. The study underscored the importance of selecting the right control method and obtaining high-quality training data. Challenges in parameter tuning for Fuzzy Logic Controllers and potential time constraints in training ANFIS were discussed. The findings have significant implications for advancing robotic control systems, particularly in the era of Industry 4.0.

Direct torque control of electric vehicle drives using hybrid techniques

Permanent magnet synchronous motors (PMSM) have the capability of delivering a high torque-to-current ratio, better efficiency and low noise. Because of the above-mentioned factors, PMSMs are commonly employed in variable speed drives, especially in electric vehicle (EV) applications. Without the usage of electromechanical devices, the conventional direct torque control (DTC) can control the speed and torque of PMSM. DTC is highly efficient, fast-tracking and provides smooth torque while limiting its ripple during transient periods. There are many benefits to using a DTC-controlled PMSM drive, including quick and reliable torque reaction, high-performance control speed, and enhanced performance. This research examines the use of the DTC approach to enhance the speed and torque behavior of PMSM. The jellyfish search optimizer (JSO) is used to adjust the DTC's responsiveness and tailor the controller's best gains. In order to train the adaptive neuro-fuzzy inference system (ANFIS) controller, JSO data are utilized. The simulation outcomes demonstrate that the proposed JSO-ANFIS controller achieves a minimal torque ripple of 0.26 Nm and preserves the speed with a harmonic error of 1.21% while contrasted to existing methods.

Enhanced modular multi-level converter-based static synchronous compensator with social ski-driver optimized adaptive neuro-fuzzy inference system controller for power quality enhancement

Enhanced modular multi-level converter-based static synchronous compensator with social ski-driver optimized adaptive neuro-fuzzy inference system controller for power quality enhancement

Performance Improvement of AGC Using Novel Controllers in a Multi-Area Solar Thermal System under Deregulated Environment

The performance improvement of Automatic Generation Control (AGC) in a Multi Area Solar Thermal Power System (MASTPS) under a deregulated environment is considered as MIPS and their performance is compared using various controllers to improve the performance of AGC in the proposed MIPS. This study takes into account a three-area conventional thermal power system in conjunction with a solar thermal power plant in each area, along with the proper generation rate constraints (GRC). The performance of the different controllers, including the Adaptive Neuro Fuzzy Inference System (ANFIS) controller and the Droop Controller (DC), Fuzzy Logic Controller (FLC), Artificial Neural Network (ANN), and Fractional Order PID (FOPID) controllers, is compared in the proposed AGC deregulated power system. The proposed MIPS is designed and implemented to make the system as realistic as possible with real world scenarios. The recommended MIPS is being simulated in MALTAB Simulink environment and simulated for multiple times to investigate the performance improvement in AGC using the adopted controllers with 0.1 per unit of step load perturbation to verify its dynamic characteristics. According to the investigation, the suggested ANFIS controller improves the AGC performance in proposed MIPS and offers better dynamic performance than other controllers.

Modeling and Design of ANFIS Dynamic Sliding Mode Controller for a Knee Orthosis of Hemiplegia

The use of assistive devices to control the loss of strength and range of motion of hemiplegic patients is becoming common. It is difficult to develop a precise control approach for a knee orthosis system because of the unpredictability of the dynamics and the unwanted subject’s spasm, jerk, and vibration during gait assistance. In this study, an adaptive neuro-fuzzy inference system (ANFIS) control system based on a nonlinear disturbance observer (NDO) and dynamic sliding mode controller (DSMC) is presented to restore the natural gait of hemiplegic patients experiencing mobility disorder and strength loss as well as monitor patient-induced disturbances and parameter variations during semiactive assistance of both the stance and swing phases. The knee orthosis system’s nonlinear dynamic relations are first developed using the Euler–Lagrange formation. Using MATLAB/Simulink, the dynamic model and controller design for the knee orthosis system was created. The Lyapunov theory is then used to ensure the knee orthosis system is asymptotically stable in view of the proposed controller once the proposed control scheme has been designed. The proposed control scheme’s (ANFIS–NDO–DSMC) gait tracking performances are shown and contrasted with the conventional sliding mode controller (SMC). Furthermore, a comparative performance analysis for parametric uncertainties and disturbances is presented to look at the robustness of the proposed controller (ANFIS–NDO–DSMC). The coefficient of determination ( R 2 ) and root mean square error (RMSE) between the reference knee angle and ANFIS–NDO–DSMC for stance phase are 1 and 0.000516 rad, respectively. For swing phase, R 2 and RMSE are 0.9999 and 0.003202 rad, respectively. For SMC, RMSE is 0.000643 and 0.003252 rad for stance and swing phases, respectively. Stance and swing phase R 2 is 0.9997 and 0.9994, respectively. As seen from simulation results, the proposed controller exhibited excellent gait tracking performance for the knee orthosis control with high robustness and very fast convergence to a steady state compared to SMC.

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

<div>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 all the advantages of controllers and provides the most accurate results. The mathematical model of all the controllers is discussed and its performance is simulated in MATLAB/Simulink. ANFIS includes all the advantages of controllers and provides the most accurate results.</div>

Power quality enhancement of solar–wind grid connected system employing genetic-based ANFIS controller

Abstract The demand for electricity globally has led to the search for renewable energy resources for power generation and attaining it in an eco-friendly manner. The solar photovoltaic systems and wind-based generators of power are regarded as primary resources of renewable energy and are called Distributed Generation units as they are scattered in nature. These are operated with bidirectional converters by providing auxiliary services at grid side and load side in either mode of microgrid operation. Besides, the DC power generation units’ integration gets converted into AC system by means of inverters. These types of systems not only increase voltage and current harmonics and power frequency deviations but also drive the distribution system to risky operating zone. This emphasizes the stipulation of advanced control schemes for microgrid architecture. Consequently, power electronic converters introduce harmonics in the system and affect system performance. To report these expanded issues, the authors recognized an advanced custom power device entitled Distributed Power Flow Controller (DPFC). In this study, the proposed system on solar–wind-based hybrid energy approach has been examined primarily through the strategy of DPFC mechanism. Later, the system has been examined with Genetic Algorithm (GA)-based fuzzy logic controller and GA-based adaptive neuro fuzzy inference system controller for shunt control of context built with DPFC mechanism. Furthermore, the validated results are verified using MATLAB/Simulink software.

MMC-based PV-Fed STATCOM with Hybrid GA-RBFNN for PQ Enhancement

Power quality (PQ) is a consequential factor, which is highly influenced by the functioning of the transmission and distribution network. In the distribution systems, voltage swells, sags, harmonics and flickers are regarded as major power quality issues. The Static Synchronous Compensator (STATCOM) is one among the specialized power devices, which has gained considerable interest for its capacity of enhancing the performance of power systems. This paper analyses the power quality issues of a photovoltaic (PV) system adopting a STATCOM-based five-level Modular Multilevel Converter (MMC). An interleaved quadratic boost converter (QBC) is employed in this work, which escalates the PV output in a wider range. An Adaptive Neuro-Fuzzy Inference System (ANFIS) controller is employed for obtaining the fast, efficient and flexible control of the DC-DC converter. The MMC plays a crucial part in mitigating power quality issues as it has the beneficial impacts such as scalability, power quality and modularity. In this approach, a five-level MMC is implemented, which transfers active power to the load by maintaining the power factor constant. The reference current generation of MMC is carried out by a hybrid Genetic Algorithm-Radial Basis Function Neural Network (GA-RBFNN) algorithm and the pulses of MMC are generated through a hysteresis controller. It provides improved training speed and convergence along with enhanced operating efficiency of the network. The output of five-level MMC is fed to a transformer-coupled LC filter. The simulations are carried out in Matlab and a minimized Total Harmonic Distortion (THD) of 1.12% is obtained.

Speed Control of an Electrical Cable Extrusion Process Using Artificial Intelligence-Based Technique

Most cable manufacturing companies use Programmable Logic Controllers with conventional controllers to control line speed during cable extrusion. These traditional controllers have difficulties keeping the line speed constant, causing surface defects on the extruded cables and affecting the quality of the manufactured cables. To overcome these challenges, data on the causes of defects during cable manufacturing were collected from a cable manufacturing company in Ghana to ascertain the possible causes during cable manufacturing. Adaptive Neuro-Fuzzy Inference System (ANFIS) controller was designed to provide a constant line speed during the cable extrusion process. To ascertain its robustness, the ANFIS controller was compared to a conventional Proportional Integral Derivative controller and a Fuzzy Logic controller. The controllers were designed and simulated using MATLAB/Simulink software. The analysis of the collected data indicated that a break in insulation/ sheath was a frequently occurring defect during the cable manufacturing process due to improper line speed control of the machines used in the cable manufacturing process. Based on the results obtained from the various controllers, it was concluded that the ANFIS controller was robust in achieving stability regarding line speed variations.

Robust sensor-less sliding mode of second-order control of doubly fed induction generators in variable speed wind turbine systems based on a novel MRAS-ANFIS observer

The current study proposed a robust sensor-less sliding mode second-order based on a super twisting algorithm (STA-SMSO) approach using a new observer Model Reference Adaptive System-Adaptative Neuro-Fuzzy Inference System (MRAS-ANFIS). This model was applied to a doubly fed induction generator (DFIG) wind turbine running under variable wind speed and DFIG fed with a power voltage source without a speed sensor, while the control objective was used to regulate independently, the active and reactive power DFIG stator were decoupled by using the field-oriented control technique. Additionally, this process reduced the cost of the control scheme and the size of DFIG by eliminating the speed sensor (encoder). In order to improve the traditional MRAS, the MRAS-ANFIS observer was proposed to replace the usual PI controller in the adaptation mechanism of MRAS with an Adaptative Neuro-Fuzzy Inference System (ANFIS) controller. The estimation of rotor position was tested and discussed under varying load conditions in low, zero, and high-speed region. The results mentioned that the proposed observer (MRAS-ANFIS) presented an attractive feature, such as guarantees finite time convergence, good response on speed wind variations, high robustness against machine parameter variations, and load variations compared to the conventional MRAS observer and MRAS-Fuzzy. Hence, the estimated rotor speed converged to their actual value has the capacity for estimating position in deferent region (low/zero/high) of speed.

Modeling and control of a photovoltaic-wind hybrid microgrid system using GA-ANFIS

This paper aims to model a PV-Wind hybrid microgrid that incorporates a Battery Energy Storage System (BESS) and design a Genetic Algorithm-Adaptive Neuro-Fuzzy Inference System (GA-ANFIS) controller to regulate its voltage amid power generation variations. Two microgrid models have been developed; a scalable Simulink Case Study Model from underlying mathematical equations and a nested voltage-current loop-based Transfer Function model. The proposed GA-ANFIS controller has been used as a Maximum Power Point Tracking (MPPT) algorithm to optimize the converter outputs and provide voltage regulation. The performance of the GA-ANFIS algorithm was compared with the Search Space Restricted-Perturb and Observe (SSR-P&O) and the Proportional-plus-Integral-plus-Derivative (PID) controllers using a simulation model built in MATLAB/SIMULINK. Results indicated that the GA-ANFIS controller is superior to the SSR-P&O and PID in terms of reduced rise time, settling time, overshoot, and the ability to handle non-linearities in the microgrid. In future work, the GA-ANFIS microgrid control system can be replaced with a three-term hybrid artificial intelligence algorithms controller.

ANFIS MPPT with power management strategy to harvest reliable power from standalone PV systems to residential loads

<span lang="EN-US">In recent decades, the matching between the growing energy demand and generation is becoming the challenging task to the researcher’s leads for the development of standalone solar photo voltaic (SSPV) power system. The SSPV system is more suited for electrification of essential loads uses DC power as it offers high efficiency. This work aims to model and simulate SSPV with lead acid battery is used as a DC source. The proposed series shunt charge controller with power management strategy (PMS)is designed and modeled to control the power flow among SSPV, battery and the load. adaptive neuro fuzzy inference system (ANFIS) controller effectively regulates the output voltage by controlling duty ratio of the suitable converter for driving a DC load. A PMS is developed for selecting the operating mode of SSCC by sensing and regulating the battery voltage within 11.6-12.95 V. Here, the 250 Wp panel has been employed to charge a 12 V, 34 AH battery. The practicability of SSPVB system is verified under various loaded conditions using MATLAB/Simulink for a period of 24 hours. A simulation result proves that this SSPV Battery system is capable to electrify the essential loads in rural and isolated areas and also reduce the dependency of grid power.</span>